report.bib

@article{siegel_all_2008,
	title = {Do all animals sleep?},
	volume = {31},
	issn = {0166-2236},
	url = {http://www.sciencedirect.com/science/article/pii/S0166223608000623},
	doi = {10.1016/j.tins.2008.02.001},
	abstract = {Some animals never exhibit a state that meets the behavioral definition of sleep. Others suspend or greatly reduce ‘sleep’ behavior for many weeks during the postpartum period or during seasonal migrations without any consequent ‘sleep debt.’ Rats die from one form of sleep deprivation, but sleep loss has not been shown to cause death in well-controlled studies in other vertebrate species. Some marine mammal species do not show evidence for {REM} sleep, and convincing evidence for this state in reptiles, fish and insects is lacking. The enormous variation in the nature of rest and sleep states across the animal kingdom and within the mammalian class has important implications for understanding the evolution and functions of sleep.},
	number = {4},
	urldate = {2014-06-07},
	journal = {Trends in Neurosciences},
	author = {Siegel, Jerome M.},
	month = apr,
	year = {2008},
	pages = {208--213},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4D4M4R6C/Siegel - 2008 - Do all animals sleep.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/RFPCPTIH/S0166223608000623.html:text/html}
}

@article{cirelli_is_2008,
	title = {Is Sleep Essential?},
	volume = {6},
	url = {http://dx.doi.org/10.1371/journal.pbio.0060216},
	doi = {10.1371/journal.pbio.0060216},
	abstract = {No current hypothesis can explain why animals need to sleep. Yet, sleep is universal, tightly regulated, and cannot be deprived without deleterious consequences. This suggests that searching for a core function of sleep, particularly at the cellular level, is still a worthwhile exercise.},
	number = {8},
	urldate = {2014-06-07},
	journal = {{PLoS} Biol},
	author = {Cirelli, Chiara and Tononi, Giulio},
	month = aug,
	year = {2008},
	pages = {e216},
	file = {PLoS Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ICKAPSIV/journal.pbio.html:text/html}
}

@article{torrence_practical_1998,
	title = {A Practical Guide to Wavelet Analysis},
	volume = {79},
	issn = {0003-0007},
	url = {http://journals.ametsoc.org/doi/abs/10.1175/1520-0477(1998)079%3C0061:APGTWA%3E2.0.CO;2},
	doi = {10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2},
	abstract = {Abstract A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El Niño–Southern Oscillation ({ENSO}). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmöller, cross-wavelet spectra, and coherence are described. The statistical significance tests are used to give a quantitative measure of changes in {ENSO} variance on interdecadal timescales. Using new datasets that extend back to 1871, the Niño3 sea surface temperature and the Southern Oscillation index show significantly higher power during 1880–1920 and 1960–90, and lower power during 1920–60, as well as a possible 15-yr modulation of variance. The power Hovmöller of sea level pressure shows significant variations in 2–8-yr wavelet power in both longitude and time.},
	number = {1},
	urldate = {2014-06-12},
	journal = {Bulletin of the American Meteorological Society},
	author = {Torrence, Christopher and Compo, Gilbert P.},
	month = jan,
	year = {1998},
	pages = {61--78},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/IXHMWSQ4/Torrence and Compo - 1998 - A Practical Guide to Wavelet Analysis.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/NUWDWP4N/1520-0477(1998)0790061APGTWA2.0.html:text/html}
}

@article{l&xe4_sleep_2012,
	title = {Sleep Stage Classification Using Unsupervised Feature Learning},
	volume = {2012},
	issn = {1687-7594},
	url = {http://www.hindawi.com/journals/aans/2012/107046/abs/},
	doi = {10.1155/2012/107046},
	abstract = {Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets ({DBNs}) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model ({HMM}) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the {DBN}, were comparable to a feature-based approach when validated on clinical datasets.},
	language = {en},
	urldate = {2014-06-12},
	journal = {Advances in Artificial Neural Systems},
	author = {L\&\#xe4 and Ngkvist, Martin and Karlsson, Lars and Loutfi, Amy},
	month = jul,
	year = {2012},
	pages = {e107046},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4JK5I2MC/L&#xe4 et al. - 2012 - Sleep Stage Classification Using Unsupervised Feat.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/8WEVWBKE/107046.html:application/xhtml+xml}
}

@article{pinero_sleep_2004,
	series = {Computational Neuroscience: Trends in Research 2004},
	title = {Sleep stage classification using fuzzy sets and machine learning techniques},
	volume = {58–60},
	issn = {0925-2312},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231204001833},
	doi = {10.1016/j.neucom.2004.01.178},
	abstract = {The hypnogram is determined after a study of electrophysiological records. In this paper we present the Intelligent system for sleep stages classification ({ISSSC}). This system is divided into four different modules: the first processes the electrophysiological signals and determines its most relevant parameters; the second module establishes fuzzy rules that will be used during the classification process; the third module is an inference module, it implements a fuzzy model. Finally the system builds the patient's hypnogram and provides us different outputs. We present the classification results obtained from applying the systems to classify patients with different sleep disorders.},
	urldate = {2014-06-12},
	journal = {Neurocomputing},
	author = {Piñero, Pedro and Garcia, Pavel and Arco, Leticia and Álvarez, Alfredo and Garcı́a, M. Matilde and Bonal, Rolando},
	month = jun,
	year = {2004},
	keywords = {Expert system, Fuzzy rules-based reasoning, Fuzzy system, Machine learning, Sleep Stages},
	pages = {1137--1143},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/GKE2IPDW/Piñero et al. - 2004 - Sleep stage classification using fuzzy sets and ma.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/K63WRBXW/S0925231204001833.html:text/html}
}

@article{rn_real-time_1991,
	title = {Real-time automated sleep scoring: validation of a microcomputer-based system for mice.},
	volume = {14},
	issn = {0161-8105},
	shorttitle = {Real-time automated sleep scoring},
	url = {http://europepmc.org/abstract/MED/1811319},
	abstract = {Abstract: Long-term circadian studies of sleep and wakefulness in rodents have been hindered by the labor required to analyze long polygraph records. To...},
	language = {eng},
	number = {1},
	urldate = {2014-06-12},
	journal = {Sleep},
	author = {Rn, Van Gelder and Dm, Edgar and Wc, Dement},
	month = feb,
	year = {1991},
	pmid = {1811319},
	keywords = {Computer-Assisted, info:mesh/Algorithms, info:mesh/Animals, info:mesh/Arousal, info:mesh/Cerebral Cortex, info:mesh/Circadian Rhythm, info:mesh/Electroencephalography, info:mesh/Electromyography, info:mesh/Male, info:mesh/Mice, info:mesh/Microcomputers, info:mesh/Reproducibility of Results, info:mesh/Signal Processing, info:mesh/Sleep Stages, info:mesh/Software},
	pages = {48--55},
	file = {Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/7IWVESAZ/1811319.html:text/html}
}

@article{ventouras_performance_2012,
	title = {Performance evaluation of an Artificial Neural Network automatic spindle detection system},
	volume = {2012},
	issn = {1557-170X},
	doi = {10.1109/EMBC.2012.6346924},
	abstract = {Sleep spindles are transient waveforms found in the electroencephalogram ({EEG}) of non-rapid eye movement ({NREM}) sleep. Sleep spindles are used for the classification of sleep stages and have been studied in the context of various psychiatric and neurological disorders, such as Alzheimer's disease ({AD}) and the so-called Mild Cognitive Impairment ({MCI}), which is considered to be a transitional stage between normal aging and dementia. The visual processing of whole-night sleep {EEG} recordings is tedious. Therefore, various techniques have been proposed for automatically detecting sleep spindles. In the present work an automatic sleep spindle detection system, that has been previously proposed, using a Multi-Layer Perceptron ({MLP}) Artificial Neural Network ({ANN}), is evaluated in detecting spindles of both healthy controls, as well as {MCI} and {AD} patients. An investigation is carried also concerning the visual detection process, taking into consideration the feedback information provided by the automatic detection system. Results indicate that the sensitivity of the detector was 81.4\%, 62.2\%, and 83.3\% and the false positive rate was 34\%, 11.5\%, and 33.3\%, for the control, {MCI}, and {AD} groups, respectively. The visual detection process had a sensitivity rate ranging from 46.5\% to 60\% and a false positive rate ranging from 4.8\% to 19.2\%.},
	language = {eng},
	journal = {Conference proceedings: ... Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society. {IEEE} Engineering in Medicine and Biology Society. Conference},
	author = {Ventouras, Errikos M and Economou, Nicholas-Tiberio and Kritikou, Ilia and Tsekou, Hara and Paparrigopoulos, Thomas J and Ktonas, Periklis Y},
	year = {2012},
	pmid = {23366885},
	keywords = {Adult, Aged, Alzheimer Disease, Brain, Diagnosis, Computer-Assisted, Electroencephalography, Humans, Male, Mild Cognitive Impairment, Neural Networks (Computer), Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Sleep Stages},
	pages = {4328--4331}
}

@article{muller_machine_2008,
	title = {Machine learning for real-time single-trial {EEG}-analysis: from brain-computer interfacing to mental state monitoring},
	volume = {167},
	issn = {0165-0270},
	shorttitle = {Machine learning for real-time single-trial {EEG}-analysis},
	doi = {10.1016/j.jneumeth.2007.09.022},
	abstract = {Machine learning methods are an excellent choice for compensating the high variability in {EEG} when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient {EEG}-based brain-computer interfacing ({BCI}) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface ({BBCI}), which can be operated with minimal subject training. Also, spelling with the novel {BBCI}-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.},
	language = {eng},
	number = {1},
	journal = {Journal of neuroscience methods},
	author = {Müller, Klaus-Robert and Tangermann, Michael and Dornhege, Guido and Krauledat, Matthias and Curio, Gabriel and Blankertz, Benjamin},
	month = jan,
	year = {2008},
	pmid = {18031824},
	keywords = {Algorithms, Brain, Brain Mapping, Communication Aids for Disabled, Electroencephalography, Electromyography, Feedback, Functional Laterality, Humans, Man-Machine Systems, Mental Processes, Signal Processing, Computer-Assisted, Spectrum Analysis, User-Computer Interface},
	pages = {82--90}
}

@article{sajda_data_2003,
	title = {A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces},
	volume = {11},
	issn = {1534-4320},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1214716},
	doi = {10.1109/TNSRE.2003.814453},
	language = {en},
	number = {2},
	urldate = {2014-06-12},
	journal = {{IEEE} Transactions on Neural Systems and Rehabilitation Engineering},
	author = {Sajda, P. and Gerson, A. and Muller, K.-R. and Blankertz, B. and Parra, L.},
	month = jun,
	year = {2003},
	pages = {184--185},
	file = {sajdatnsre03.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/87XXCHKH/sajdatnsre03.pdf:application/pdf}
}

@article{sen_comparative_2014,
	title = {A Comparative Study on Classification of Sleep Stage Based on {EEG} Signals Using Feature Selection and Classification Algorithms},
	volume = {38},
	issn = {0148-5598, 1573-689X},
	url = {http://link.springer.com/article/10.1007/s10916-014-0018-0},
	doi = {10.1007/s10916-014-0018-0},
	abstract = {Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from {EEG} signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance ({mRMR}); fast correlation based feature selection ({FCBF}); {ReliefF}; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent {EEG} signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest ({RF}); feed-forward neural network ({FFNN}); decision tree ({DT}); support vector machine ({SVM}); and radial basis function neural network ({RBF})) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 \% classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.},
	language = {en},
	number = {3},
	urldate = {2014-06-12},
	journal = {Journal of Medical Systems},
	author = {Şen, Baha and Peker, Musa and Çavuşoğlu, Abdullah and Çelebi, Fatih V.},
	month = mar,
	year = {2014},
	keywords = {Classification algorithms, Classification of sleep stage, {EEG} signals, Feature selection algorithms, Health Informatics, Statistics for Life Sciences, Medicine, Health Sciences},
	pages = {1--21},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/HVRU5IFT/Şen et al. - 2014 - A Comparative Study on Classification of Sleep Sta.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/9DF4AA5Q/s10916-014-0018-0.html:text/html}
}

@incollection{sommer_clustering_2001,
	title = {Clustering of {EEG}-segments using hierarchical agglomerative methods and self-organizing maps},
	url = {http://link.springer.com/chapter/10.1007/3-540-44668-0_90},
	urldate = {2014-06-13},
	booktitle = {Artificial Neural Networks—{ICANN} 2001},
	publisher = {Springer},
	author = {Sommer, David and Golz, Martin},
	year = {2001},
	pages = {642--649},
	file = {79e4150c1555375974.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/PVCHASHB/79e4150c1555375974.pdf:application/pdf}
}

@article{costa-miserachs_automated_2003,
	title = {Automated sleep staging in rat with a standard spreadsheet},
	volume = {130},
	issn = {0165-0270},
	url = {http://www.sciencedirect.com/science/article/pii/S0165027003002292},
	doi = {10.1016/S0165-0270(03)00229-2},
	abstract = {A new method of automated sleep–wake staging in the rat is described. Hippocampal electroencephalographic ({HPC}) and nuchal electromyographic signals were recorded by a digital polygraph. The {HPC} channel was filtered off-line to obtain the original plus theta and delta waves. Statistics of each of these four channels were obtained every 5 s and exported to a standard spreadsheet. The automated staging consisted of five steps: (1) automatic detection of waking, nonrapid eye movement sleep and rapid eye movement sleep patterns (5-s periods); (2) calculation of statistics for each vigilance state; (3) final classification of 5-s periods; (4) construction of a primary 20-s epoch hypnogram; and (5) automatic refinement of the previous hypnogram. The system includes indices about the accuracy of the staging and was validated with five recordings of 23 h each. The global agreement between human and automatic scoring in the validation recordings was 94.32\%.},
	number = {1},
	urldate = {2014-06-18},
	journal = {Journal of Neuroscience Methods},
	author = {Costa-Miserachs, David and Portell-Cortés, Isabel and Torras-Garcia, Meritxell and Morgado-Bernal, Ignacio},
	month = nov,
	year = {2003},
	keywords = {Automated sleep staging, Classification, Rat, Sleep, Spreadsheet},
	pages = {93--101},
	file = {Costa-Miserachs et al. - 2003 - Automated sleep staging in rat with a standard spr.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/DXQV6APW/Costa-Miserachs et al. - 2003 - Automated sleep staging in rat with a standard spr.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/FCF8RZ9J/S0165027003002292.html:text/html}
}

@article{buzsaki_origin_2012,
	title = {The origin of extracellular fields and currents — {EEG}, {ECoG}, {LFP} and spikes},
	volume = {13},
	copyright = {© 2012 Nature Publishing Group},
	issn = {1471-003X},
	url = {http://www.nature.com/nrn/journal/v13/n6/abs/nrn3241.html},
	doi = {10.1038/nrn3241},
	abstract = {Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources — including Na+ and Ca2+ spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations — can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.},
	language = {en},
	number = {6},
	urldate = {2014-07-04},
	journal = {Nature Reviews Neuroscience},
	author = {Buzsáki, György and Anastassiou, Costas A. and Koch, Christof},
	month = jun,
	year = {2012},
	pages = {407--420},
	file = {Buzsáki et al. - 2012 - The origin of extracellular fields and currents — .pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/Q8VDTF4Q/Buzsáki et al. - 2012 - The origin of extracellular fields and currents — .pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/2U8DXD5T/nrn3241.html:text/html}
}

@article{gunes_efficient_2010,
	title = {Efficient sleep stage recognition system based on {EEG} signal using k-means clustering based feature weighting},
	volume = {37},
	issn = {0957-4174},
	url = {http://www.sciencedirect.com/science/article/pii/S095741741000343X},
	doi = {10.1016/j.eswa.2010.04.043},
	abstract = {Sleep scoring is one of the most important methods for diagnosis in psychiatry and neurology. Sleep staging is a time consuming and difficult task conducted by sleep specialists. The purposes of this work are to automatic score the sleep stages and to help to sleep physicians on sleep stage scoring. In this work, a novel data preprocessing method called k-means clustering based feature weighting ({KMCFW}) has been proposed and combined with k-{NN} (k-nearest neighbor) and decision tree classifiers to classify the {EEG} (electroencephalogram) sleep into six sleep stages including awake, N-{REM} (non-rapid eye movement) stage 1, N-{REM} stage 2, N-{REM} stage 3, {REM}, and non-sleep (movement time). First of all, frequency domain features belonging to sleep {EEG} signal have been extracted using Welch spectral analysis method and composed 129 features from {EEG} signal relating each sleep stages. In order to decrease the features, the statistical features comprising minimum value, maximum value, standard deviation, and mean value have been used and then reduced from 129 to 4 features. In the second phase, the sleep stages dataset with four features has been weighted by means of k-means clustering based feature weighting. Finally, the weighted sleep stages have been automatically classified into six sleep stages using k-{NN} and C4.5 decision tree classifier. In the classification of sleep stages, the k values of 10, 20, 30, 40, 50, and 60 in k-{NN} classifier have been used and compared with each other. In the experimental results, while sleep stages has been classified with 55.88\% success rate using k-{NN} classifier (for k value of 40), the weighted sleep stages with {KMCFW} has been recognized with 82.15\% success rate k-{NN} classifier (for k value of 40). And also, we have investigated the relevance between sleep stages and frequency domain features belonging to {EEG} signal. These results have demonstrated that proposed weighting method have a considerable impact on automatic determining of sleep stages. This system could be used as an online system in the automatic scoring of sleep stages and helps to sleep physicians in the sleep scoring process.},
	number = {12},
	urldate = {2014-07-09},
	journal = {Expert Systems with Applications},
	author = {Güneş, Salih and Polat, Kemal and Yosunkaya, Şebnem},
	month = dec,
	year = {2010},
	keywords = {Decision tree, {EEG} signal, k-Means clustering based feature weighting, k-Nearest neighbor classifier, Polysomnography, Sleep scoring},
	pages = {7922--7928},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/44S3DBP7/Güneş et al. - 2010 - Efficient sleep stage recognition system based on .pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/RBUWSRF6/S095741741000343X.html:text/html}
}

@article{crisler_sleep-stage_2008,
	title = {Sleep-stage scoring in the rat using a support vector machine},
	volume = {168},
	issn = {0165-0270},
	url = {http://www.sciencedirect.com/science/article/pii/S0165027007005286},
	doi = {10.1016/j.jneumeth.2007.10.027},
	abstract = {Analysis and classification of sleep stages is a fundamental part of basic sleep research. Rat sleep stages are scored based on electrocorticographic ({ECoG}) signals recorded from electrodes implanted epidurally and electromyographic ({EMG}) signals from the temporalis or nuchal muscle. An automated sleep scoring system was developed using a support vector machine ({SVM}) to discriminate among waking, nonrapid eye movement sleep, and paradoxical sleep. Two experts scored retrospective data obtained from six Sprague–Dawley rodents to provide the training sets and subsequent comparison data used to assess the effectiveness of the {SVM}. Numerous time-domain and frequency-domain features were extracted for each epoch and selectively reduced using statistical analyses. The {SVM} kernel function was chosen to be a Gaussian radial basis function and kernel parameters were varied to examine the effectiveness of optimization methods. Tests indicated that a common set of features could be chosen resulted in an overall agreement between the automated scores and the expert consensus of greater than 96\%.},
	number = {2},
	urldate = {2014-07-09},
	journal = {Journal of Neuroscience Methods},
	author = {Crisler, Shelly and Morrissey, Michael J. and Anch, A. Michael and Barnett, David W.},
	month = mar,
	year = {2008},
	keywords = {Automated scoring, Sleep staging, Support vector machine},
	pages = {524--534},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ACKXQWHS/Crisler et al. - 2008 - Sleep-stage scoring in the rat using a support vec.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/7FWD5QMI/S0165027007005286.html:text/html}
}

@article{bhattacharya_complexity_2000,
	title = {Complexity analysis of spontaneous {EEG}},
	volume = {60},
	url = {http://www.wg.ane.pl/pdf/6051.pdf},
	number = {4},
	urldate = {2014-07-11},
	journal = {Acta neurobiologiae experimentalis},
	author = {Bhattacharya, Joydeep and {others}},
	year = {2000},
	pages = {495--502},
	file = {Bhattacharya and others - 2000 - Complexity analysis of spontaneous EEG.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/AP3VSATC/Bhattacharya and others - 2000 - Complexity analysis of spontaneous EEG.pdf:application/pdf}
}

@inproceedings{jovic_feature_2013,
	title = {Feature extraction from electroencephalographic records using {EEGFrame} framework},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6596396},
	urldate = {2014-07-11},
	booktitle = {Information \& Communication Technology Electronics \& Microelectronics ({MIPRO}), 2013 36th International Convention on},
	publisher = {{IEEE}},
	author = {Jovic, Alan and Suc, Lea and Bogunovic, Nikola},
	year = {2013},
	pages = {965--970},
	file = {06596396.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ZB4IDW39/06596396.pdf:application/pdf}
}

@article{higuchi_approach_1988,
	title = {Approach to an irregular time series on the basis of the fractal theory},
	volume = {31},
	issn = {0167-2789},
	url = {http://www.sciencedirect.com/science/article/pii/0167278988900814},
	doi = {10.1016/0167-2789(88)90081-4},
	abstract = {We present a technique to measure the fractal dimension of the set of points (t, f(t)) forming the graph of a function f defined on the unit interval. First we apply it to a fractional Brownian function [1] which has a property of self-similarity for all scales, and we can get the stable and precise fractal dimension. This technique is also applied to the observational data of natural phenomena. It does not show self-similarity all over the scale but has a different self-similarity across the characteristic time scale. The present method gives us a stable characteristic time scale as well as the fractal dimension.},
	number = {2},
	urldate = {2014-07-11},
	journal = {Physica D: Nonlinear Phenomena},
	author = {Higuchi, T.},
	month = jun,
	year = {1988},
	pages = {277--283},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/T6EQ7SKX/Higuchi - 1988 - Approach to an irregular time series on the basis .pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/7ED6QBNH/0167278988900814.html:text/html}
}

@inproceedings{petrosian_kolmogorov_1995,
	title = {Kolmogorov complexity of finite sequences and recognition of different preictal {EEG} patterns},
	doi = {10.1109/CBMS.1995.465426},
	abstract = {The problem of an adequate quantitative interpretation of epileptic {EEG} recordings is of great importance in the understanding, recognition and treatment of epilepsy. In recent years, much effort has been made to develop computerized methods which can characterize different interictal, ictal and postictal stages. The main issue of whether there exist a preictal phenomenon is unresolved. In this paper, we address this issue making use of the most basic representation of data complexity, namely the algorithmic information content. In general this measure, also known as Kolmogorov complexity, represents the compressibility of the data strings. It can also be used to describe properties (linear and nonlinear) of the underlying dynamical system. We analyze Kolmogorov complexity and related characteristics of intracranial {EEG} recordings containing preictal, ictal and postictal segments},
	booktitle = {, Proceedings of the Eighth {IEEE} Symposium on Computer-Based Medical Systems, 1995},
	author = {Petrosian, A},
	month = jun,
	year = {1995},
	keywords = {algorithmic information content, Chaos, computational complexity, data complexity, data compression, data recording, data string compressibility, dynamical system, Electrodes, Electroencephalography, epilepsy, finite sequences, Fractals, ictal segments, intracranial {EEG} recordings, Kolmogorov complexity, Limit-cycles, linear properties, medical signal processing, nonlinear properties, pattern recognition, postictal segments, preictal {EEG} patterns, quantitative interpretation, sequences, State estimation, Surgery, Yield estimation},
	pages = {212--217},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/NV3EZ9I6/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/2KWVQW63/Petrosian - 1995 - Kolmogorov complexity of finite sequences and reco.pdf:application/pdf}
}

@article{hjorth_eeg_1970,
	title = {{EEG} analysis based on time domain properties},
	volume = {29},
	issn = {0013-4694},
	url = {http://www.sciencedirect.com/science/article/pii/0013469470901434},
	doi = {10.1016/0013-4694(70)90143-4},
	abstract = {A method to describe the general characteristics of an {EEG} trace in a few quantitative terms is introduced. Its descriptive parameters are entirely based on time, but they can be derived also from the statistical moments of the power spectrum. Thus the method provides a bridge between a physical time domain interpretation and the conventional frequency domain description. Further, the parameters are based on the concept of variance, giving them an additive property so that the measured values pertain also to any basic elements from which a complex curve may be composed by superposition.

The proposed method offers a way to on-line measurement of basic signal properties by means of a time-based calculation, requiring less complex equipment compared to conventional frequency analysis. The data-reducing capability of the parameters has been experimentally stated in the recording of “sleep profiles”.},
	number = {3},
	urldate = {2014-07-14},
	journal = {Electroencephalography and Clinical Neurophysiology},
	author = {Hjorth, Bo},
	month = sep,
	year = {1970},
	pages = {306--310},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/KNKG9M6P/Hjorth - 1970 - EEG analysis based on time domain properties.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/86H6KI2B/0013469470901434.html:text/html}
}

@article{bao_pyeeg:_2011,
	title = {{PyEEG}: An Open Source Python Module for {EEG}/{MEG} Feature Extraction},
	volume = {2011},
	issn = {1687-5265},
	shorttitle = {{PyEEG}},
	url = {http://www.hindawi.com/journals/cin/2011/406391/abs/},
	doi = {10.1155/2011/406391},
	abstract = {Computer-aided diagnosis of neural diseases from {EEG} signals (or other physiological signals that can be treated as time series, e.g., {MEG}) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of {EEG} signals. In our previous works, we have implemented many {EEG} feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting {EEG} features has the potential to save much time for computational neuroscientists. In this paper, we introduce {PyEEG}, an open source Python module for {EEG} feature extraction.},
	language = {en},
	urldate = {2014-07-14},
	journal = {Computational Intelligence and Neuroscience},
	author = {Bao, Forrest Sheng and Liu, Xin and Zhang, Christina},
	month = mar,
	year = {2011},
	pages = {e406391},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/AM3CTNBH/Bao et al. - 2011 - PyEEG An Open Source Python Module for EEGMEG Fe.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/K8K46JGC/406391.html:application/xhtml+xml}
}

@inproceedings{jovic_feature_2013-1,
	title = {Feature extraction from electroencephalographic records using {EEGFrame} framework},
	abstract = {Analysis of electroencephalographic ({EEG}) signals usually includes visual inspection of the signal, feature extraction, and model generation. Computer-aided nonlinear feature extraction from {EEG} in particular has already led to improved descriptive and prognostic models of brain states and disorders. However, in this field, there is a lack of freely available powerful tools for scientific exploration of {EEG} that would help researchers to compare the results of their work with others. Especially, because of the great diversity of the proposed methods for {EEG} analysis, there exists a need for a joint framework for inspection, extraction and visualization performed on the {EEG} records. The aim of this paper is to introduce such a framework, called {EEGFrame}, with its implementation in Java. The framework currently supports the analysis of standard {EDF} records via signal inspection, feature extraction, and feature vectors storage for knowledge discovery. {EEGFrame} is the result of refactoring and extension of the {HRVFrame} framework for heart rate variability analysis, with added methods for {EEG} analysis. This paper describes the properties and capabilities of the framework and discusses its relevance with respect to similar work. The main advantage of {EEGFrame} is its support for numerous linear and nonlinear methods described in literature.},
	booktitle = {2013 36th International Convention on Information Communication Technology Electronics Microelectronics ({MIPRO})},
	author = {Jovic, A and Suc, L. and Bogunovic, N.},
	month = may,
	year = {2013},
	keywords = {brain disorder model, Brain modeling, brain state model, computer aided nonlinear feature extraction, data mining, {EDF} records, {EEG} analysis, {EEG} frame framework, {EEG} records, {EEG} signals, electroencephalographic records, electroencephalographic signals, Electroencephalography, Entropy, feature extraction, feature vectors storage, Java implementation, knowledge discovery, {MATLAB}, medical signal processing, model generation, signal inspection, visual inspection, Visualization},
	pages = {965--970},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/EGNRR5ZU/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/KSWF9P79/Jovic et al. - 2013 - Feature extraction from electroencephalographic re.pdf:application/pdf}
}

@inproceedings{hermawan_adaptive_2013,
	title = {Adaptive Multi codebook Fuzzy Neuro Generalized Learning Vector Quantization for sleep stages classification},
	doi = {10.1109/ICACSIS.2013.6761614},
	abstract = {In this paper, a new codebook based learning method, Adaptive Multicodebook Fuzzy Neuro Generalized Learning Vector Quantization ({FNGLVQ}), is proposed. The main contribution of this paper is the use of multi codebook which is adaptive in nature to the distribution of the data. The number and position of the codebook is determined through clustering approach. In this research, a decision tree based clustering, {CLTree}, is used to cluster the data to get the initial placement of the codebook. The advantage of using {CLTree} against other clustering method is {CLTree} do not need the number of cluster as initial input. In average, this method improves the accuracy rate of Mitra data 3 and 4 class 2\% and 2.12\%, respectively compared to the single codebook approach.},
	booktitle = {2013 International Conference on Advanced Computer Science and Information Systems ({ICACSIS})},
	author = {Hermawan, I and Tawakal, M.I and Setiawan, IM.A and Habibie, I and Jatmiko, W.},
	month = sep,
	year = {2013},
	keywords = {Accuracy, adaptive multi codebook fuzzy neuro generalized learning vector quantization, biology computing, {CLtree}, Clustering algorithms, decision tree based clustering, decision trees, Electric variables measurement, Electrocardiography, feature extraction, {FNGLVQ}, fuzzy neural nets, learning (artificial intelligence), Mitra data, pattern clustering, Sleep, sleep stages classification, Vector quantization},
	pages = {431--436},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/UTVZ5I6H/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/UHZZMAEP/Hermawan et al. - 2013 - Adaptive Multi codebook Fuzzy Neuro Generalized Le.pdf:application/pdf}
}

@article{costa_multiscale_2005,
	title = {Multiscale entropy analysis of biological signals},
	volume = {71},
	url = {http://link.aps.org/doi/10.1103/PhysRevE.71.021906},
	doi = {10.1103/PhysRevE.71.021906},
	abstract = {Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy ({MSE}) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct {MSE} curve profiles, suggesting diagnostic uses. The results support a general “complexity-loss” theory of aging and disease. We also apply the method to the analysis of coding and noncoding {DNA} sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called “junk {DNA}” sequences contain important biological information.},
	number = {2},
	urldate = {2014-07-14},
	journal = {Physical Review E},
	author = {Costa, Madalena and Goldberger, Ary L. and Peng, C.-K.},
	month = feb,
	year = {2005},
	pages = {021906},
	file = {APS Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ECMER9FG/PhysRevE.71.html:text/html;Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/VXH2KHTD/Costa et al. - 2005 - Multiscale entropy analysis of biological signals.pdf:application/pdf}
}

@article{richman_physiological_2000,
	title = {Physiological time-series analysis using approximate entropy and sample entropy},
	volume = {278},
	copyright = {Copyright © 2000 the American Physiological Society},
	issn = {0363-6135, 1522-1539},
	url = {http://ajpheart.physiology.org/content/278/6/H2039},
	abstract = {Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy ({ApEn}), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. {ApEn} statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy ({SampEn}), and have compared {ApEn} and {SampEn} by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-{ApEn} and cross-{SampEn}, which use cardiovascular data sets to measure the similarity of two distinct time series. {SampEn} agreed with theory much more closely than {ApEn} over a broad range of conditions. The improved accuracy of {SampEn} statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.},
	language = {en},
	number = {6},
	urldate = {2014-07-15},
	journal = {American Journal of Physiology - Heart and Circulatory Physiology},
	author = {Richman, Joshua S. and Moorman, J. Randall},
	month = jun,
	year = {2000},
	pages = {H2039--H2049},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/BR239UV5/Richman and Moorman - 2000 - Physiological time-series analysis using approxima.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/NJVK9KA2/H2039.html:text/html}
}

@article{ferenets_comparison_2006,
	title = {Comparison of entropy and complexity measures for the assessment of depth of sedation},
	volume = {53},
	issn = {0018-9294},
	doi = {10.1109/TBME.2006.873543},
	abstract = {Entropy and complexity of the electroencephalogram ({EEG}) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested methods, Shannon entropy ({ShEn}), spectral entropy, approximate entropy ({ApEn}), Lempel-Ziv complexity ({LZC}), and Higuchi fractal dimension ({HFD}) are then applied to the {EEG} signal recorded during sedation in the intensive care unit ({ICU}). It is shown that the applied measures behave in a different manner when compared to clinical depth of sedation score the Ramsay score. {ShEn} tends to increase while the other tested measures decrease with deepening sedation. {ApEn}, {LZC}, and {HFD} are highly sensitive to the presence of high-frequency components in the {EEG} signal.},
	number = {6},
	journal = {{IEEE} Transactions on Biomedical Engineering},
	author = {Ferenets, R. and Lipping, T. and Anier, A and Jantti, V. and Melto, S. and Hovilehto, S.},
	month = jun,
	year = {2006},
	keywords = {Adult, Aged, Aged, 80 and over, Algorithms, Anesthesia, Anesthetics, approximate entropy, Biomedical engineering, Biomedical measurements, Biomedical monitoring, Brain, Complexity, Condition monitoring, Conscious Sedation, depth of anesthesia, depth of sedation, Diagnosis, Computer-Assisted, {EEG}, electroencephalogram, Electroencephalography, Entropy, Fractals, Higuchi fractal dimension, Humans, Information technology, intensive care unit, Lempel-Ziv complexity, medical signal processing, Middle Aged, Pattern Recognition, Automated, probability, probability distribution, Propofol, Ramsay score, sedation, Shannon entropy, spectral entropy, surrogate data, Testing, Therapy, Computer-Assisted},
	pages = {1067--1077},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/A4RNRXAH/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/GUQNB3V4/Ferenets et al. - 2006 - Comparison of entropy and complexity measures for .pdf:application/pdf}
}

@article{pang_unexpected_2009,
	title = {An unexpected role for {TASK}-3 potassium channels in network oscillations with implications for sleep mechanisms and anesthetic action},
	volume = {106},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/106/41/17546},
	doi = {10.1073/pnas.0907228106},
	abstract = {{TASK} channels are acid-sensitive and anesthetic-activated members of the family of two-pore-domain potassium channels. We have made the surprising discovery that the genetic ablation of {TASK}-3 channels eliminates a specific type of theta oscillation in the cortical electroencephalogram ({EEG}) resembling type {II} theta (4–9 Hz), which is thought to be important in processing sensory stimuli before initiating motor activity. In contrast, ablation of {TASK}-1 channels has no effect on theta oscillations. Despite the absence of type {II} theta oscillations in the {TASK}-3 knockout ({KO}) mice, the related type I theta, which has certain neuronal pathways in common and is involved in exploratory behavior, is unaffected. In addition to the absence of type {II} theta oscillations, the {TASK}-3 {KO} animals show marked alterations in both anesthetic sensitivity and natural sleep behavior. Their sensitivity to halothane, a potent activator of {TASK} channels, is greatly reduced, whereas their sensitivity to cyclopropane, which does not activate {TASK}-3 channels, is unchanged. The {TASK}-3 {KO} animals exhibit a slower progression from their waking to sleeping states and, during their sleeping period, their sleep episodes as well as their {REM} theta oscillations are more fragmented. These results imply a previously unexpected role for {TASK}-3 channels in the cellular mechanisms underlying these behaviors and suggest that endogenous modulators of these channels may regulate theta oscillations.},
	language = {en},
	number = {41},
	urldate = {2014-08-07},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Pang, Daniel S. J. and Robledo, Christian J. and Carr, David R. and Gent, Thomas C. and Vyssotski, Alexei L. and Caley, Alex and Zecharia, Anna Y. and Wisden, William and Brickley, Stephen G. and Franks, Nicholas P.},
	month = oct,
	year = {2009},
	pmid = {19805135},
	keywords = {{EEG}, knockout, {REM}, theta, wavelet},
	pages = {17546--17551},
	file = {Pang et al. - 2009 - An unexpected role for TASK-3 potassium channels i.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/8KZIMINA/Pang et al. - 2009 - An unexpected role for TASK-3 potassium channels i.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/6T9B5PDA/17546.html:text/html}
}

@article{vyazovskiy_nrem_2014,
	title = {{NREM} and {REM} Sleep: Complementary Roles in Recovery after Wakefulness},
	volume = {20},
	issn = {1073-8584, 1089-4098},
	shorttitle = {{NREM} and {REM} Sleep},
	url = {http://nro.sagepub.com/cgi/doi/10.1177/1073858413518152},
	doi = {10.1177/1073858413518152},
	language = {en},
	number = {3},
	urldate = {2014-08-15},
	journal = {The Neuroscientist},
	author = {Vyazovskiy, V. V. and Delogu, A.},
	month = jun,
	year = {2014},
	pages = {203--219},
	file = {203.full.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/TS4XJWSG/203.full.pdf:application/pdf}
}

@inproceedings{putnam_design_1997,
	title = {Design of fractional delay filters using convex optimization},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=625616},
	urldate = {2014-08-20},
	booktitle = {Applications of Signal Processing to Audio and Acoustics, 1997. 1997 {IEEE} {ASSP} Workshop on},
	publisher = {{IEEE}},
	author = {Putnam, William and Smith, Julius},
	year = {1997},
	pages = {4--pp},
	file = {optfir.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/FIUT47VC/optfir.pdf:application/pdf}
}

@article{higuchi_approach_1988-1,
	title = {Approach to an irregular time series on the basis of the fractal theory},
	volume = {31},
	issn = {0167-2789},
	url = {http://www.sciencedirect.com/science/article/pii/0167278988900814},
	doi = {10.1016/0167-2789(88)90081-4},
	abstract = {We present a technique to measure the fractal dimension of the set of points (t, f(t)) forming the graph of a function f defined on the unit interval. First we apply it to a fractional Brownian function [1] which has a property of self-similarity for all scales, and we can get the stable and precise fractal dimension. This technique is also applied to the observational data of natural phenomena. It does not show self-similarity all over the scale but has a different self-similarity across the characteristic time scale. The present method gives us a stable characteristic time scale as well as the fractal dimension.},
	number = {2},
	urldate = {2014-08-22},
	journal = {Physica D: Nonlinear Phenomena},
	author = {Higuchi, T.},
	month = jun,
	year = {1988},
	pages = {277--283},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/48XVXEDN/Higuchi - 1988 - Approach to an irregular time series on the basis .pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/QA3NHDEH/0167278988900814.html:text/html}
}

@article{kemp_european_2003,
	title = {European data format ‘plus’ ({EDF}+), an {EDF} alike standard format for the exchange of physiological data},
	volume = {114},
	issn = {1388-2457},
	url = {http://www.sciencedirect.com/science/article/pii/S1388245703001238},
	doi = {10.1016/S1388-2457(03)00123-8},
	abstract = {The European data format ({EDF}) is a widely accepted standard for exchange of electroencephalogram and polysomnogram data between different equipment and labs. But it hardly accommodates other investigations.

{EDF}+ is a more flexible but still simple format which is compatible to {EDF} except that an {EDF}+ file may contain interrupted recordings. Also, {EDF}+ supports time-stamped annotations for the storage of events such as text annotations, stimuli, averaged signals, electrocardiogram parameters, apnoeas and so on.

When compared to {EDF}, {EDF}+ can not only store annotations but also electromyography, evoked potentials, electroneurography, electrocardiography and many more types of investigations. Further improvements over {EDF} include the use of standard electrode names. {EDF}+ is so much like {EDF} that existing {EDF} viewers still display the signals in {EDF}+ files. Software development is limited mainly to implementing the annotations.

{EDF}+ offers a format for a wide range of neurophysiological investigations which can become a standard within a few years.},
	number = {9},
	urldate = {2014-08-24},
	journal = {Clinical Neurophysiology},
	author = {Kemp, Bob and Olivan, Jesus},
	month = sep,
	year = {2003},
	keywords = {Quantitative analysis European data format},
	pages = {1755--1761},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/H9RU3657/Kemp and Olivan - 2003 - European data format ‘plus’ (EDF+), an EDF alike s.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/IASEUCDT/S1388245703001238.html:text/html}
}

@article{bezanson_julia:_2012,
	title = {Julia: A Fast Dynamic Language for Technical Computing},
	shorttitle = {Julia},
	url = {http://arxiv.org/abs/1209.5145},
	abstract = {Dynamic languages have become popular for scientific computing. They are generally considered highly productive, but lacking in performance. This paper presents Julia, a new dynamic language for technical computing, designed for performance from the beginning by adapting and extending modern programming language techniques. A design based on generic functions and a rich type system simultaneously enables an expressive programming model and successful type inference, leading to good performance for a wide range of programs. This makes it possible for much of the Julia library to be written in Julia itself, while also incorporating best-of-breed C and Fortran libraries.},
	urldate = {2014-09-01},
	journal = {{arXiv}:1209.5145 [cs]},
	author = {Bezanson, Jeff and Karpinski, Stefan and Shah, Viral B. and Edelman, Alan},
	month = sep,
	year = {2012},
	note = {{arXiv}: 1209.5145},
	keywords = {Computer Science - Computational Engineering, Finance, and Science, Computer Science - Programming Languages, D.3.2},
	file = {arXiv\:1209.5145 PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/MFZAMNRJ/Bezanson et al. - 2012 - Julia A Fast Dynamic Language for Technical Compu.pdf:application/pdf;arXiv.org Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/T5U8C56J/1209.html:text/html}
}

@article{deng_time_2013,
	title = {A time series forest for classification and feature extraction},
	volume = {239},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025513001473},
	doi = {10.1016/j.ins.2013.02.030},
	abstract = {A tree-ensemble method, referred to as time series forest ({TSF}), is proposed for time series classification. {TSF} employs a combination of entropy gain and a distance measure, referred to as the Entrance (entropy and distance) gain, for evaluating the splits. Experimental studies show that the Entrance gain improves the accuracy of {TSF}. {TSF} randomly samples features at each tree node and has computational complexity linear in the length of time series, and can be built using parallel computing techniques. The temporal importance curve is proposed to capture the temporal characteristics useful for classification. Experimental studies show that {TSF} using simple features such as mean, standard deviation and slope is computationally efficient and outperforms strong competitors such as one-nearest-neighbor classifiers with dynamic time warping.},
	urldate = {2014-09-06},
	journal = {Information Sciences},
	author = {Deng, Houtao and Runger, George and Tuv, Eugene and Vladimir, Martyanov},
	month = aug,
	year = {2013},
	keywords = {Decision tree, Ensemble, Entrance gain, Interpretability, Large margin, Time series classification},
	pages = {142--153},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/BXEXSJIK/Deng et al. - 2013 - A time series forest for classification and featur.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/EUM7FHPD/S0020025513001473.html:text/html}
}

@article{oropesa_sleep_1999,
	title = {Sleep stage classification using wavelet transform and neural network},
	url = {http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1999/tr-99-008.pdf},
	urldate = {2014-09-06},
	journal = {International computer science institute},
	author = {Oropesa, Edgar and Cycon, Hans L. and Jobert, Marc},
	year = {1999},
	file = {tr-99-008.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/6WN3IK7J/tr-99-008.pdf:application/pdf}
}

@article{pan_transition-constrained_2012,
	title = {A transition-constrained discrete hidden Markov model for automatic sleep staging},
	volume = {11},
	copyright = {2012 Pan et al.; licensee {BioMed} Central Ltd.},
	issn = {1475-925X},
	url = {http://www.biomedical-engineering-online.com/content/11/1/52/abstract},
	doi = {10.1186/1475-925X-11-52},
	abstract = {Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic ({PSG}) recordings including electroencephalograms ({EEGs}), electrooculograms ({EOGs}) and electromyograms ({EMGs}), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen \& Kales (R\&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable.
{PMID}: 22908930},
	language = {en},
	number = {1},
	urldate = {2014-09-06},
	journal = {{BioMedical} Engineering {OnLine}},
	author = {Pan, Shing-Tai and Kuo, Chih-En and Zeng, Jian-Hong and Liang, Sheng-Fu},
	month = aug,
	year = {2012},
	pmid = {22908930},
	keywords = {Discrete Hidden Markov Model ({DHMM}), Electroencephalogram ({EEG}), Electromyogram ({EMG}), Electrooculogram ({EOG}), Sleep staging},
	pages = {52},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/FH73M75Q/Pan et al. - 2012 - A transition-constrained discrete hidden Markov mo.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/FJHCM4XE/52.html:text/html}
}

@article{fraiwan_automated_2012,
	title = {Automated sleep stage identification system based on time–frequency analysis of a single {EEG} channel and random forest classifier},
	volume = {108},
	issn = {0169-2607},
	url = {http://www.sciencedirect.com/science/article/pii/S0169260711003105},
	doi = {10.1016/j.cmpb.2011.11.005},
	abstract = {In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine ({AASM}) is presented. The propose approach employs time–frequency analysis and entropy measures for feature extraction from a single electroencephalograph ({EEG}) channel. Three time–frequency techniques were deployed for the analysis of the {EEG} signal: Choi–Williams distribution ({CWD}), continuous wavelet transform ({CWT}), and Hilbert–Huang Transform ({HHT}). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time–frequency representation of the {EEG} signal using Renyi's entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time–frequency distributions: {CWD}, {CWT}, and {HHT}. The {CWT} time–frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.},
	number = {1},
	urldate = {2014-09-06},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Fraiwan, Luay and Lweesy, Khaldon and Khasawneh, Natheer and Wenz, Heinrich and Dickhaus, Hartmut},
	month = oct,
	year = {2012},
	keywords = {Automated sleep stage identification, Choi–Williams distribution ({CWD}), Continuous wavelet transform ({CWT}), Hilbert–Huang Transform ({HHT}), Random forest classifier, Time–frequency analysis},
	pages = {10--19},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/XVJJ73Q6/Fraiwan et al. - 2012 - Automated sleep stage identification system based .pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/VTV9P9V9/S0169260711003105.html:text/html}
}

@article{subasi_eeg_2007,
	title = {{EEG} signal classification using wavelet feature extraction and a mixture of expert model},
	volume = {32},
	issn = {0957-4174},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417406000844},
	doi = {10.1016/j.eswa.2006.02.005},
	abstract = {Mixture of experts ({ME}) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization ({EM}) algorithm has been introduced to the {ME} network structure for detection of epileptic seizure. The detection of epileptiform discharges in the {EEG} is an important component in the diagnosis of epilepsy. {EEG} signals were decomposed into the frequency sub-bands using discrete wavelet transform ({DWT}). Then these sub-band frequencies were used as an input to a {ME} network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the “gating function”. The invariant transformations of the {ME} probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed {ME} network structure has some potential in detecting epileptic seizures. The {ME} network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.},
	number = {4},
	urldate = {2014-09-06},
	journal = {Expert Systems with Applications},
	author = {Subasi, Abdulhamit},
	month = may,
	year = {2007},
	keywords = {Discrete wavelet transform ({DWT}), Electroencephalogram ({EEG}), Epileptic seizure, Expectation-Maximization ({EM}) algorithm, Mixture of experts},
	pages = {1084--1093},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/2E2X37I9/Subasi - 2007 - EEG signal classification using wavelet feature ex.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/6HEA8I9B/S0957417406000844.html:text/html}
}

@article{kannathal_entropies_2005,
	title = {Entropies for detection of epilepsy in {EEG}},
	volume = {80},
	issn = {0169-2607},
	url = {http://www.sciencedirect.com/science/article/pii/S0169260705001525},
	doi = {10.1016/j.cmpb.2005.06.012},
	abstract = {Summary
The electroencephalogram ({EEG}) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the {EEG} signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The aim of this work is to compare the different entropy estimators when applied to {EEG} data from normal and epileptic subjects. The results obtained indicate that entropy estimators can distinguish normal and epileptic {EEG} data with more than 95\% confidence (using t-test). The classification ability of the entropy measures is tested using {ANFIS} classifier. The results are promising and a classification accuracy of about 90\% is achieved.},
	number = {3},
	urldate = {2014-09-06},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Kannathal, N. and Choo, Min Lim and Acharya, U. Rajendra and Sadasivan, P. K.},
	month = dec,
	year = {2005},
	keywords = {{ANFIS} classifier, approximate entropy, electroencephalogram, epilepsy, Kolmogorov entropy, Renyi entropy, spectral entropy},
	pages = {187--194},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/GMT9R4GB/Kannathal et al. - 2005 - Entropies for detection of epilepsy in EEG.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/BWZFHAW3/S0169260705001525.html:text/html}
}

@article{srinivasan_artificial_2005,
	title = {Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features},
	volume = {29},
	issn = {0148-5598, 1573-689X},
	url = {http://link.springer.com/article/10.1007/s10916-005-6133-1},
	doi = {10.1007/s10916-005-6133-1},
	abstract = {Electroencephalogram ({EEG}) signal plays an important role in the diagnosis of epilepsy. The long-term {EEG} recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of {EEG} data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the {EEG} data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the {EEG} signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6\% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.},
	language = {en},
	number = {6},
	urldate = {2014-09-06},
	journal = {Journal of Medical Systems},
	author = {Srinivasan, V. and Eswaran, C. and Sriraam, {and} N.},
	month = dec,
	year = {2005},
	keywords = {artificial neural network, {EEG}, epilepsy, Health Informatics \& Health Administration, seizure, Statistics for Life Sciences, Medicine, Health Sciences, time-domain and frequency-domain features},
	pages = {647--660},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/CPI6PC9U/Srinivasan et al. - 2005 - Artificial Neural Network Based Epileptic Detectio.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/MTH69M9C/10.html:text/html}
}

@article{sezer_employment_2012,
	title = {Employment and Comparison of Different Artificial Neural Networks for Epilepsy Diagnosis from {EEG} Signals},
	volume = {36},
	issn = {0148-5598, 1573-689X},
	url = {http://link.springer.com/article/10.1007/s10916-010-9480-5},
	doi = {10.1007/s10916-010-9480-5},
	abstract = {In this study, it has been intended to analyze Electroencephalography ({EEG}) signals by Wavelet Transform ({WT}) for diagnosis of epilepsy, to employ various Artificial Neural Networks ({ANNs}) for the signals’ automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three {EEG} signals have been decomposed into frequency sub bands by {WT} and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis ({PCA}) method has been applied when necessary and these feature vectors have been classified by five different {ANNs} as either epileptic or healthy. The performance evaluation has been carried out by conducting {ROC} analysis for the used {ANN} models that and their comparisons have also been included.},
	language = {en},
	number = {1},
	urldate = {2014-09-06},
	journal = {Journal of Medical Systems},
	author = {Sezer, Esma and Işik, Hakan and Saracoğlu, Esra},
	month = feb,
	year = {2012},
	keywords = {Artificial Neural Networks ({ANN}), Electroencephalography ({EEG}), epilepsy, Health Informatics, Principal Component Analysis ({PCA}), Roc analysis, Statistics for Life Sciences, Medicine, Health Sciences, Wavelet transform},
	pages = {347--362},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/57S2W9UA/Sezer et al. - 2012 - Employment and Comparison of Different Artificial .pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/7GT4XCRD/10.html:text/html}
}

@article{subasi_automatic_2005,
	title = {Automatic recognition of vigilance state by using a wavelet-based artificial neural network},
	volume = {14},
	issn = {0941-0643, 1433-3058},
	url = {http://link.springer.com/article/10.1007/s00521-004-0441-0},
	doi = {10.1007/s00521-004-0441-0},
	abstract = {In this study, 5-s long sequences of full-spectrum electroencephalogram ({EEG}) recordings were used for classifying alert versus drowsy states in an arbitrary subject. {EEG} signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron ({MLP}), was used for the classification of {EEG} signals. A multilayer perceptron neural network ({MLPNN}) trained with the Levenberg–Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the {MLPNN} inputs, spectral analysis of {EEG} signals was performed using the discrete wavelet transform ({DWT}) technique. The {MLPNN} was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3\% alert, 96.6\% drowsy, and 90\% sleep. The classification results showed that the {MLPNN} trained with the Levenberg–Marquardt algorithm was effective for discriminating the vigilance state of the subject.},
	language = {en},
	number = {1},
	urldate = {2014-09-06},
	journal = {Neural Computing \& Applications},
	author = {Subasi, Abdulhamit and Kiymik, M. Kemal and Akin, Mehmet and Erogul, Osman},
	month = mar,
	year = {2005},
	keywords = {Alert, Discrete wavelet transform ({DWT}), Drowsy, {EEG}, Levenberg–Marquardt algorithm, Multilayer perceptron neural network ({MLPNN}), Sleep},
	pages = {45--55},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/8IV3HXJR/Subasi et al. - 2005 - Automatic recognition of vigilance state by using .pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/BCIJC5CE/10.html:text/html}
}

@article{doroshenkov_classification_2007,
	title = {Classification of human sleep stages based on {EEG} processing using hidden Markov models},
	volume = {41},
	issn = {0006-3398, 1573-8256},
	url = {http://link.springer.com/article/10.1007/s10527-007-0006-5},
	doi = {10.1007/s10527-007-0006-5},
	language = {en},
	number = {1},
	urldate = {2014-09-06},
	journal = {Biomedical Engineering},
	author = {Doroshenkov, L. G. and Konyshev, V. A. and Selishchev, S. V.},
	month = jan,
	year = {2007},
	keywords = {Biomaterials, Biomedical engineering, Engineering, general},
	pages = {25--28},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/U4UE59XC/Doroshenkov et al. - 2007 - Classification of human sleep stages based on EEG .pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/K35N9IQJ/10.html:text/html}
}

@article{sabeti_entropy_2009,
	title = {Entropy and complexity measures for {EEG} signal classification of schizophrenic and control participants},
	volume = {47},
	issn = {0933-3657},
	url = {http://www.sciencedirect.com/science/article/pii/S0933365709000530},
	doi = {10.1016/j.artmed.2009.03.003},
	abstract = {{SummaryObjective}
In this paper, electroencephalogram ({EEG}) signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of classifying the two groups.
Materials and methods
For each case, 20 channels of {EEG} are recorded. Several features including Shannon entropy, spectral entropy, approximate entropy, Lempel–Ziv complexity and Higuchi fractal dimension are extracted from {EEG} signals. Leave-one (participant)-out cross-validation is used for reliable estimate of the separability of the two groups. The training set is used for training the two classifiers, namely, linear discriminant analysis ({LDA}) and adaptive boosting (Adaboost). Each classifier is assessed using the test dataset.
Results
A classification accuracy of 86\% and 90\% is obtained by {LDA} and Adaboost respectively. For further improvement, genetic programming is employed to select the best features and remove the redundant ones. Applying the two classifiers to the reduced feature set, a classification accuracy of 89\% and 91\% is obtained by {LDA} and Adaboost respectively. The proposed technique is compared and contrasted with a recently reported method and it is demonstrated that a considerably enhanced performance is achieved.
Conclusion
This study shows that {EEG} signals can be a useful tool for discrimination of the schizophrenic and control participants. It is suggested that this analysis can be a complementary tool to help psychiatrists diagnosing schizophrenic patients.},
	number = {3},
	urldate = {2014-09-06},
	journal = {Artificial Intelligence in Medicine},
	author = {Sabeti, Malihe and Katebi, Serajeddin and Boostani, Reza},
	month = nov,
	year = {2009},
	keywords = {Complexity, {EEG} classification, Entropy, Features selection, Schizophrenic},
	pages = {263--274},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/SRB4P4TQ/Sabeti et al. - 2009 - Entropy and complexity measures for EEG signal cla.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/8SMN27RS/S0933365709000530.html:text/html}
}

@article{kouchaki_tensor_2014,
	title = {Tensor based Singular Spectrum Analysis for Automatic Scoring of Sleep {EEG}},
	volume = {Early Access Online},
	issn = {1534-4320},
	doi = {10.1109/TNSRE.2014.2329557},
	abstract = {A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis ({SSA}) algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition ({SVD}). As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been effectively exploited to highlight the subspace of interest. As an important application, sleep {EEG} has been analysed and the stages of sleep for the subjects in normal condition, with sleep restriction, and with sleep extension have been accurately estimated and compared with the results of sleep scoring by clinical experts.},
	journal = {{IEEE} Transactions on Neural Systems and Rehabilitation Engineering},
	author = {Kouchaki, S. and Sanei, S. and Arbon, E. and Dijk, D.},
	year = {2014},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/K5BVIC9B/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/N6RFNC2W/Kouchaki et al. - 2014 - Tensor based Singular Spectrum Analysis for Automa.pdf:application/pdf}
}

@article{sunagawa_faster:_2013,
	title = {{FASTER}: an unsupervised fully automated sleep staging method for mice},
	volume = {18},
	copyright = {© 2013 The Authors Genes to Cells © 2013 by the Molecular Biology Society of Japan and Wiley Publishing Asia Pty Ltd},
	issn = {1365-2443},
	shorttitle = {{FASTER}},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/gtc.12053/abstract},
	doi = {10.1111/gtc.12053},
	abstract = {Identifying the stages of sleep, or sleep staging, is an unavoidable step in sleep research and typically requires visual inspection of electroencephalography ({EEG}) and electromyography ({EMG}) data. Currently, scoring is slow, biased and prone to error by humans and thus is the most important bottleneck for large-scale sleep research in animals. We have developed an unsupervised, fully automated sleep staging method for mice that allows less subjective and high-throughput evaluation of sleep. Fully Automated Sleep {sTaging} method via {EEG}/{EMG} Recordings ({FASTER}) is based on nonparametric density estimation clustering of comprehensive {EEG}/{EMG} power spectra. {FASTER} can accurately identify sleep patterns in mice that have been perturbed by drugs or by genetic modification of a clock gene. The overall accuracy is over 90\% in every group. 24-h data are staged by a laptop computer in 10 min, which is faster than an experienced human rater. Dramatically improving the sleep staging process in both quality and throughput {FASTER} will open the door to quantitative and comprehensive animal sleep research.},
	language = {en},
	number = {6},
	urldate = {2014-09-06},
	journal = {Genes to Cells},
	author = {Sunagawa, Genshiro A. and Séi, Hiroyoshi and Shimba, Shigeki and Urade, Yoshihiro and Ueda, Hiroki R.},
	month = jun,
	year = {2013},
	pages = {502--518},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/6UAUGN62/Sunagawa et al. - 2013 - FASTER an unsupervised fully automated sleep stag.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ASM44TWW/full.html:text/html}
}

@article{yu_machine_2011,
	title = {A Machine Learning Approach to Classify Vigilance States in Rats},
	volume = {38},
	issn = {0957-4174},
	url = {http://dx.doi.org/10.1016/j.eswa.2011.02.076},
	doi = {10.1016/j.eswa.2011.02.076},
	abstract = {Identifying mammalian vigilance states has recently become an important topic in biological science research. The vigilance states are usually categorized in at least three states, including slow wave sleep ({SWS}), rapid eye movement sleep ({REM}), and awakening. To identify different vigilance states, even a well-trained expert must spend a lot of time analyzing a mass of physiological recording data. This study proposes an automatic vigilance stages classification method for analyzing {EEG} signals in rats. The {EEG} signals were transferred by fast Fourier transform before extracting features. These extracted features were then used as training patterns to construct the proposed classification system. The proposed classification system contains two functional units. The first unit is principle component analysis ({PCA}) method, which is used to project the high dimensional features into the lower dimensional subspace. The second unit is the k-nearest neighbor (k-{NN}) method, which identifies the physiological state in each {EEG} signal epoch. Based on the results of analyzing 810 epochs of {EEG} signal, the proposed classification method achieves satisfactory classification accuracy for vigilance states. Based on machine-learning algorithms, the classifier learns to approach the configuration that best fits the categorization task. Therefore, additional training in searching best parameters and thresholds can be avoided. Moreover, the {PCA} algorithm projects data instances into a 3-D space, making it possible to visualize state-changing dynamics. Experimental results show that the proposed machine-learning based classifier performs better than conventional vigilance state classification algorithms. The results also suggest that it is possible to identify the vigilance states with only {EEG} signals using the proposed pattern recognition technique.},
	number = {8},
	urldate = {2014-09-06},
	journal = {Expert Syst. Appl.},
	author = {Yu, Zong-En and Kuo, Chung-Chih and Chou, Chien-Hsing and Yen, Chen-Tung and Chang, Fu},
	month = aug,
	year = {2011},
	keywords = {{EEG}, k-{NN}, Machine learning, Pattern classification, {PCA}, Vigilance stages},
	pages = {10153--10160}
}

@misc{cribari-neto_beta_2009,
	type = {Paper},
	title = {Beta Regression in R},
	url = {http://epub.wu.ac.at/726/},
	abstract = {The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises. (author´s abstract)},
	language = {en},
	urldate = {2014-09-06},
	author = {Cribari-Neto, Francisco and Zeileis, Achim},
	year = {2009},
	keywords = {beta regression / rates / proportions / R},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4WHB8W6D/Cribari-Neto and Zeileis - 2009 - Beta Regression in R.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/W47WH3DP/726.html:text/html}
}

@article{vyssotski_miniature_2006,
	title = {Miniature Neurologgers for Flying Pigeons: Multichannel {EEG} and Action and Field Potentials in Combination With {GPS} Recording},
	volume = {95},
	copyright = {Copyright © 2006 by the American Physiological Society},
	issn = {0022-3077, 1522-1598},
	shorttitle = {Miniature Neurologgers for Flying Pigeons},
	url = {http://jn.physiology.org/content/95/2/1263},
	doi = {10.1152/jn.00879.2005},
	abstract = {To study the neurophysiology of large-scale spatial cognition, we analyzed the neuronal activity of navigating homing pigeons. This is not possible using conventional radio-telemetry suitable for short distances only. Therefore we developed a miniaturized data logger (“neurologger”) that can be carried by a homing pigeon on its back, in conjunction with a micro-global position system ({GPS}) logger recording the spatial position of the bird. In its present state, the neurologger permits recording from up to eight single-ended or differential electrodes in a walking or flying pigeon. Inputs from eight independent channels are preamplified, band-pass filtered, and directed to an eight-channel, 10-bit analog-digital converter of the microcontroller storing data on a “Multimedia” or “Secure Digital” card. For electroencephalography ({EEG}), the logger permits simultaneous recordings of up to eight channels during maximally 47 h, depending on memory, while single unit activity from two channels can be stored over 9 h. The logger permits single unit separation from recorded multiunit signals. The neurologger with {GPS} represents a better alternative to telemetry that will eventually permit to record neuronal activity during cognitive and innate behavior of many species moving freely in their habitats but will also permit automated high-throughput screening of {EEG} in the laboratory.},
	language = {en},
	number = {2},
	urldate = {2014-09-07},
	journal = {Journal of Neurophysiology},
	author = {Vyssotski, Alexei L. and Serkov, Andrei N. and Itskov, Pavel M. and Dell'Omo, Giacomo and Latanov, Alexander V. and Wolfer, David P. and Lipp, Hans-Peter},
	month = feb,
	year = {2006},
	pages = {1263--1273},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/S9VR7KN7/Vyssotski et al. - 2006 - Miniature Neurologgers for Flying Pigeons Multich.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/PX7NNQNT/1263.html:text/html}
}

@article{piscopo_sleep_2009,
	title = {Sleep and its possible role in learning: a phylogenetic view},
	volume = {1},
	shorttitle = {Sleep and its possible role in learning},
	url = {http://mail.bfresh.us/2009/v1s/af/36/36.pdf},
	urldate = {2014-09-07},
	journal = {Front Biosci},
	author = {Piscopo, Stefania},
	year = {2009},
	pages = {437--447},
	file = {36.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/JUECBDIP/36.pdf:application/pdf}
}

@article{siegel_clues_2005,
	title = {Clues to the functions of mammalian sleep},
	volume = {437},
	copyright = {© 2005 Nature Publishing Group},
	issn = {0028-0836},
	url = {http://www.nature.com/nature/journal/v437/n7063/full/nature04285.html},
	doi = {10.1038/nature04285},
	abstract = {The functions of mammalian sleep remain unclear. Most theories suggest a role for non-rapid eye movement ({NREM}) sleep in energy conservation and in nervous system recuperation. Theories of {REM} sleep have suggested a role for this state in periodic brain activation during sleep, in localized recuperative processes and in emotional regulation. Across mammals, the amount and nature of sleep are correlated with age, body size and ecological variables, such as whether the animals live in a terrestrial or an aquatic environment, their diet and the safety of their sleeping site. Sleep may be an efficient time for the completion of a number of functions, but variations in sleep expression indicate that these functions may differ across species.},
	language = {en},
	number = {7063},
	urldate = {2014-09-07},
	journal = {Nature},
	author = {Siegel, Jerome M.},
	month = oct,
	year = {2005},
	pages = {1264--1271},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/5N5A5R8P/Siegel - 2005 - Clues to the functions of mammalian sleep.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/RX6RHEUH/nature04285.html:text/html}
}

@article{imeri_how_2009,
	title = {How (and why) the immune system makes us sleep},
	volume = {10},
	copyright = {© 2009 Nature Publishing Group},
	issn = {1471-003X},
	url = {http://www.nature.com/nrn/journal/v10/n3/full/nrn2576.html},
	doi = {10.1038/nrn2576},
	abstract = {Good sleep is necessary for physical and mental health. For example, sleep loss impairs immune function, and sleep is altered during infection. Immune signalling molecules are present in the healthy brain, where they interact with neurochemical systems to contribute to the regulation of normal sleep. Animal studies have shown that interactions between immune signalling molecules (such as the cytokine interleukin 1) and brain neurochemical systems (such as the serotonin system) are amplified during infection, indicating that these interactions might underlie the changes in sleep that occur during infection. Why should the immune system cause us to sleep differently when we are sick? We propose that the alterations in sleep architecture during infection are exquisitely tailored to support the generation of fever, which in turn imparts survival value.},
	language = {en},
	number = {3},
	urldate = {2014-09-07},
	journal = {Nature Reviews Neuroscience},
	author = {Imeri, Luca and Opp, Mark R.},
	month = mar,
	year = {2009},
	pages = {199--210},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/7C2WFWHF/Imeri and Opp - 2009 - How (and why) the immune system makes us sleep.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/TXXRSFPD/nrn2576.html:text/html}
}

@article{aserinsky_regularly_1953,
	title = {Regularly occurring periods of eye motility, and concomitant phenomena, during sleep},
	volume = {118},
	url = {http://www.lphslibrary.org/uploads/7/2/9/6/7296009/aserinsky_eye_motility2.pdf},
	number = {3062},
	urldate = {2014-09-07},
	journal = {Science},
	author = {Aserinsky, Eugene and Kleitman, Nathaniel},
	year = {1953},
	pages = {273--274},
	file = {aserinsky_eye_motility2.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/PA32PCUC/aserinsky_eye_motility2.pdf:application/pdf}
}

@article{loomis_distribution_1938,
	title = {Distribution of disturbance-patterns in the human electroencephalogram with special reference to sleep.},
	journal = {Journal of Neurophysiology},
	author = {Loomis, Alfred L and Harvey, E Newton and Hobart, Garret A},
	year = {1938}
}

@article{smith_paradoxical_1982,
	title = {Paradoxical sleep at selective times following training is necessary for learning},
	volume = {29},
	issn = {0031-9384},
	url = {http://www.sciencedirect.com/science/article/pii/0031938482902682},
	doi = {10.1016/0031-9384(82)90268-2},
	abstract = {Rats were trained in a two-way shuttle shock avoidance task, 20 trials per day for 5 consecutive days. In Experiment 1, groups were subjected to selective paradoxical sleep deprivation ({PSD}) for daily 4 hour periods. It was found that only {PSD} applied between 9–12 hours or 17–20 hours after the daily training sessions resulted in acquisition deficits. These vulnerable time periods coincided with polygraphically observed {PS} increases above normal levels following identical training procedures in previous studies. In Experiment 2, animals were only allowed {PS} during the 9–12 hour and 17–20 hour post training time “windows” observed in Experiment 1. For the remaining time each day, they were subjected to {PSD}. Acquisition was found to be unimparied, despite this severe treatment. However, any other equivalent length {PSD} regime which overlapped with either of the time “windows” resulted in severe retardation of task acquisition. Results were believed to provide strong support for the sleep-learning hypothesis.},
	number = {3},
	urldate = {2014-09-07},
	journal = {Physiology \& Behavior},
	author = {Smith, Carlyle and Butler, Stephen},
	month = sep,
	year = {1982},
	keywords = {Active avoidance task, Paradoxical sleep, Paradoxical sleep deprivation, Paradoxical sleep time “windows”},
	pages = {469--473},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/M8IPHCPE/Smith and Butler - 1982 - Paradoxical sleep at selective times following tra.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/RDQRRHAX/0031938482902682.html:text/html}
}

@article{toth_animal_2013,
	title = {Animal Models of Sleep Disorders},
	volume = {63},
	abstract = {Problems with sleep affect a large part of the general population, with more than half of all people in the United States reporting difficulties with sleep or insufficient sleep at various times and about 40 million affected chronically. Sleep is a complex physiologic process that is influenced by many internal and environmental factors, and problems with sleep are often related to specific personal circumstances or are based on subjective reports from the affected person. Although human subjects are used widely in the study of sleep and sleep disorders, the study of animals has been invaluable in developing our understanding about the physiology of sleep and the underlying mechanisms of sleep disorders. Historically, the use of animals for the study of sleep disorders has arguably been most fruitful for the condition of narcolepsy, in which studies of dogs and mice revealed previously unsuspected mechanisms for this condition. The current overview considers animal models that have been used to study 4 of the most common human sleep disorders—insomnia, narcolepsy, restless legs syndrome, and sleep apnea—and summarizes considerations relevant to the use of animals for the study of sleep and sleep disorders. Animal-based research has been vital to the elucidation of mechanisms that underlie sleep, its regulation, and its disorders and undoubtedly will remain crucial for discovering and validating sleep mechanisms and testing interventions for sleep disorders.},
	number = {2},
	journal = {Comparative Medicine},
	author = {Toth, Linda A and Bhargava, Pavan},
	month = apr,
	year = {2013},
	pages = {91--104},
	file = {IngentaConnect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/3BUT6I87/Toth and Bhargava - 2013 - Animal Models of Sleep Disorders.pdf:application/pdf}
}

@article{haustein_automatic_1986,
	title = {Automatic analysis overcomes limitations of sleep stage scoring},
	volume = {64},
	issn = {0013-4694},
	url = {http://www.sciencedirect.com/science/article/pii/0013469486901616},
	doi = {10.1016/0013-4694(86)90161-6},
	abstract = {A computer programme for the automatic analysis of the sleep {EEG} and {EMG} is presented. An {EEG} parameter is derived which is based on the joint frequency-amplitude distribution of the {EEG} activity. This newly developed parameter stresses the dynamic development of sleep, composed of alternating phases of {EEG} synchronization and desynchronization. While synchronization develops slowly, the opposite phase of {EEG} activity, desynchronization, is more rapid. A particular advantage of the {EEG} parameter is its continuous scale which results in high resolution. Thus, the parameter reflects gradual {EEG} changes which a visual analyser would have to classify into the same sleep stage. In addition to the {EEG}, the {EMG} is analysed and two parameters are extracted, representing the mean muscle tone and transient {EMG} activation, respectively. There is a close temporal relationship between the {EEG} and the {EMG} with a maximum of transient {EMG} activity during the phase of {EEG} desynchronization. The properties of the automatic analysis were compared to the visual analysis on a sample of 12 all-night sleep records. The results show that the {EEG} parameter also agrees sufficiently with the traditional sleep scoring method and therefore is a valid descriptor of the time course of sleep.},
	number = {4},
	urldate = {2014-09-07},
	journal = {Electroencephalography and Clinical Neurophysiology},
	author = {Haustein, Werner and Pilcher, June and Klink, Joachim and Schulz, Hartmut},
	month = oct,
	year = {1986},
	keywords = {automatic analysis, {EEG} desynchronization, {EEG} synchronization, Electromyogram ({EMG}), Sleep},
	pages = {364--374},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/A87U5ZPW/Haustein et al. - 1986 - Automatic analysis overcomes limitations of sleep .pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/3S5MPJKK/0013469486901616.html:text/html}
}

@article{chouvet_automatic_1980,
	title = {An automatic sleep classifier for laboratory rodents},
	volume = {4},
	copyright = {(c) 2012 {APA}, all rights reserved},
	issn = {0340-0905(Print)},
	abstract = {Developed a system for on-line automatic sleep scoring of laboratory rats and mice. The system is flexible in regard to its adaptability for different strains and allows compact storage of data for off-line statistical processing following long-term continuous recordings. Methodological considerations and an example of automatic sleep scoring on a rat are presented. (33 ref)},
	number = {1},
	journal = {Waking \& Sleeping},
	author = {Chouvet, Guy and Odet, Pascal and Valatx, Jean-Louis and Pujol, Jean-François},
	year = {1980},
	keywords = {*Automated Information Processing, *Computer Applications, *Sleep, Mice, Rats},
	pages = {9--31},
	file = {APA PsycNET Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/X3KQNPT2/1981-20051-001.html:text/html}
}

@article{robert_review_1998,
	title = {Review of neural network applications in sleep research},
	volume = {79},
	issn = {0165-0270},
	url = {http://www.sciencedirect.com/science/article/pii/S0165027097001787},
	doi = {10.1016/S0165-0270(97)00178-7},
	abstract = {To find a better automated sleep-wake staging system for human analyses of numerous polygraphic records is an interesting challenge in sleep research. Over the last few decades, many automated systems have been developed but none are universally applicable. Improvements in computer technology coupled with artificial neural networks based systems (connectionist models) are responsible for new data processing approaches. Despite extensive use of connectionist models in biological data processing, in the past, the field of sleep research appeared to have neglected this approach. Only a few sleep-wake staging systems based on neural network technology have been developed. This paper reviews the current use of artificial neural networks in sleep research. Following a brief presentation of neural network technology, each of the existing system is described and attention drawn to the heterogeneity of the different processing approaches in sleep research. The high performances observed with systems based on neural networks highlight the need to integrate these tools into the field of sleep research.},
	number = {2},
	urldate = {2014-09-07},
	journal = {Journal of Neuroscience Methods},
	author = {Robert, Claude and Guilpin, Christian and Limoge, Aymé},
	month = feb,
	year = {1998},
	keywords = {Automated sleep staging, Biological data processing, Neural network, Review, Sleep},
	pages = {187--193},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ZCGPSK2E/Robert et al. - 1998 - Review of neural network applications in sleep res.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/XXVR5KZZ/S0165027097001787.html:text/html}
}

@article{langkvist_sleep_2012,
	title = {Sleep stage classification using unsupervised feature learning},
	volume = {2012},
	url = {http://dl.acm.org/citation.cfm?id=2387786},
	urldate = {2014-09-07},
	journal = {Advances in Artificial Neural Systems},
	author = {Längkvist, Martin and Karlsson, Lars and Loutfi, Amy},
	year = {2012},
	pages = {5},
	file = {140.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/UNR4SQHT/140.pdf:application/pdf}
}

@article{breiman_random_2001,
	title = {Random Forests},
	volume = {45},
	issn = {0885-6125, 1573-0565},
	url = {http://link.springer.com/article/10.1023/A%3A1010933404324},
	doi = {10.1023/A:1010933404324},
	abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund \& R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.},
	language = {en},
	number = {1},
	urldate = {2014-09-07},
	journal = {Machine Learning},
	author = {Breiman, Leo},
	month = oct,
	year = {2001},
	keywords = {Artificial Intelligence (incl. Robotics), Automation and Robotics, Classification, Computing Methodologies, Ensemble, Language Translation and Linguistics, regression, Simulation and Modeling},
	pages = {5--32},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/WHZ9ZUXN/Breiman - 2001 - Random Forests.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/CGJK3UW9/A1010933404324.html:text/html}
}

@article{ahmed_empirical_2010,
	title = {An Empirical Comparison of Machine Learning Models for Time Series Forecasting},
	volume = {29},
	issn = {0747-4938},
	url = {http://dx.doi.org/10.1080/07474938.2010.481556},
	doi = {10.1080/07474938.2010.481556},
	abstract = {In this work we present a large scale comparison study for the major machine learning models for time series forecasting. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). There have been very few, if any, large scale comparison studies for machine learning models for the regression or the time series forecasting problems, so we hope this study would fill this gap. The models considered are multilayer perceptron, Bayesian neural networks, radial basis functions, generalized regression neural networks (also called kernel regression), K-nearest neighbor regression, {CART} regression trees, support vector regression, and Gaussian processes. The study reveals significant differences between the different methods. The best two methods turned out to be the multilayer perceptron and the Gaussian process regression. In addition to model comparisons, we have tested different preprocessing methods and have shown that they have different impacts on the performance.},
	number = {5-6},
	urldate = {2014-09-07},
	journal = {Econometric Reviews},
	author = {Ahmed, Nesreen K. and Atiya, Amir F. and Gayar, Neamat El and El-Shishiny, Hisham},
	month = aug,
	year = {2010},
	pages = {594--621},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/CS2E44WX/Ahmed et al. - 2010 - An Empirical Comparison of Machine Learning Models.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/DU27ESXK/07474938.2010.html:text/html}
}

@incollection{dietterich_machine_2002,
	series = {Lecture Notes in Computer Science},
	title = {Machine Learning for Sequential Data: A Review},
	copyright = {©2002 Springer-Verlag Berlin Heidelberg},
	isbn = {978-3-540-44011-6, 978-3-540-70659-5},
	shorttitle = {Machine Learning for Sequential Data},
	url = {http://link.springer.com/chapter/10.1007/3-540-70659-3_2},
	abstract = {Statistical learning problems in many fields involve sequential data. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. The paper also discusses some open research issues.},
	language = {en},
	number = {2396},
	urldate = {2014-09-07},
	booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
	publisher = {Springer Berlin Heidelberg},
	author = {Dietterich, Thomas G.},
	editor = {Caelli, Terry and Amin, Adnan and Duin, Robert P. W. and Ridder, Dick de and Kamel, Mohamed},
	month = jan,
	year = {2002},
	keywords = {Artificial Intelligence (incl. Robotics), Image Processing and Computer Vision, pattern recognition, Probability and Statistics in Computer Science},
	pages = {15--30},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/3MMH9QV8/Dietterich - 2002 - Machine Learning for Sequential Data A Review.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/M43MHVHZ/3-540-70659-3_2.html:text/html}
}

@article{ding_querying_2008,
	title = {Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures},
	volume = {1},
	issn = {2150-8097},
	shorttitle = {Querying and Mining of Time Series Data},
	url = {http://dx.doi.org/10.14778/1454159.1454226},
	doi = {10.14778/1454159.1454226},
	abstract = {The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic.},
	number = {2},
	urldate = {2014-09-07},
	journal = {Proc. {VLDB} Endow.},
	author = {Ding, Hui and Trajcevski, Goce and Scheuermann, Peter and Wang, Xiaoyue and Keogh, Eamonn},
	month = aug,
	year = {2008},
	pages = {1542--1552},
	file = {ACM Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4ZWGUMKN/Ding et al. - 2008 - Querying and Mining of Time Series Data Experimen.pdf:application/pdf}
}

@article{prabhakar_application_2002,
	title = {Application of discrete wavelet transform for detection of ball bearing race faults},
	volume = {35},
	issn = {0301-679X},
	url = {http://www.sciencedirect.com/science/article/pii/S0301679X02000634},
	doi = {10.1016/S0301-679X(02)00063-4},
	abstract = {Bearing race faults have been detected by using discrete wavelet transform ({DWT}). Vibration signals from ball bearings having single and multiple point defects on inner race, outer race and the combination faults have been considered for analysis. The impulses in vibration signals due to bearing faults are prominent in wavelet decompositions. It is found that the impulses appear periodically with a time period corresponding to characteristic defect frequencies. It has been shown that {DWT} can be used as an effective tool for detecting single and multiple faults in the ball bearings.},
	number = {12},
	urldate = {2014-09-07},
	journal = {Tribology International},
	author = {Prabhakar, S. and Mohanty, A. R. and Sekhar, A. S},
	month = dec,
	year = {2002},
	keywords = {Bearing faults, Discrete wavelet transform, Impulses},
	pages = {793--800},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ZVJN2954/Prabhakar et al. - 2002 - Application of discrete wavelet transform for dete.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/2Q8IAH4V/S0301679X02000634.html:text/html}
}

@book{r_core_team_r:_2014,
	address = {Vienna, Austria},
	title = {R: A Language and Environment for Statistical Computing},
	url = {http://www.R-project.org/},
	publisher = {R Foundation for Statistical Computing},
	author = {{R Core Team}},
	year = {2014}
}

@article{liaw_classification_2002,
	title = {Classification and Regression by {randomForest}},
	volume = {2},
	url = {http://CRAN.R-project.org/doc/Rnews/},
	number = {3},
	journal = {R News},
	author = {Liaw, Andy and Wiener, Matthew},
	year = {2002},
	pages = {18--22}
}

@misc{cribari-neto_beta_2009-1,
	type = {Paper},
	title = {Beta Regression in R},
	url = {http://epub.wu.ac.at/726/},
	abstract = {The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises. (author´s abstract)},
	language = {en},
	urldate = {2014-09-07},
	author = {Cribari-Neto, Francisco and Zeileis, Achim},
	year = {2009},
	keywords = {beta regression / rates / proportions / R},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/SFIXHT8D/Cribari-Neto and Zeileis - 2009 - Beta Regression in R.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/CRJM66W9/726.html:text/html}
}

@book{bates_lme4:_2014,
	title = {lme4: Linear mixed-effects models using Eigen and S4},
	url = {http://CRAN.R-project.org/package=lme4},
	author = {Bates, Douglas and Maechler, Martin and Bolker, Ben and Walker, Steven},
	year = {2014},
	note = {R package version 1.1-6}
}

@article{walt_numpy_2011,
	title = {The {NumPy} Array: A Structure for Efficient Numerical Computation},
	volume = {13},
	issn = {1521-9615},
	shorttitle = {The {NumPy} Array},
	url = {http://scitation.aip.org/content/aip/journal/cise/13/2/10.1109/MCSE.2011.37},
	doi = {10.1109/MCSE.2011.37},
	abstract = {In the Python world, {NumPy} arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, {NumPy} performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.},
	number = {2},
	urldate = {2014-09-07},
	journal = {Computing in Science \& Engineering},
	author = {Walt, Stéfan van der and Colbert, S. Chris and Varoquaux, Gaël},
	month = mar,
	year = {2011},
	pages = {22--30},
	file = {Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/57CTZUA2/MCSE.2011.html:text/html}
}

@article{morin_shining_2012,
	title = {Shining Light into Black Boxes},
	volume = {336},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/content/336/6078/159},
	doi = {10.1126/science.1218263},
	language = {en},
	number = {6078},
	urldate = {2012-04-22},
	journal = {Science},
	author = {Morin, A. and Urban, J. and Adams, P. D and Foster, I. and Sali, A. and Baker, D. and Sliz, P.},
	month = apr,
	year = {2012},
	note = {Cited by 0031},
	pages = {159--160},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/EMF7U69C/Morin et al. - 2012 - Shining Light into Black Boxes.pdf:application/pdf}
}

@article{crick_can_2014,
	title = {"Can I Implement Your Algorithm?": A Model for Reproducible Research Software},
	shorttitle = {"Can I Implement Your Algorithm?},
	url = {http://arxiv.org/abs/1407.5981},
	abstract = {The reproduction and replication of novel scientific results has become a major issue for a number of disciplines. In computer science and related disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it in a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair, and widely available benchmark sets present another barrier. In this paper, we outline several suggestions to address these issues, driven by specific examples from a range of scientific domains. Finally, based on these suggestions, we propose a new open platform for scientific software development which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering.},
	urldate = {2014-09-07},
	journal = {{arXiv}:1407.5981 [cs]},
	author = {Crick, Tom and Hall, Benjamin A. and Ishtiaq, Samin},
	month = jul,
	year = {2014},
	note = {{arXiv}: 1407.5981},
	keywords = {Computer Science - Computational Engineering, Finance, and Science, Computer Science - Software Engineering},
	file = {arXiv\:1407.5981 PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/38AESUGS/Crick et al. - 2014 - Can I Implement Your Algorithm A Model for Rep.pdf:application/pdf;arXiv.org Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/AM6C33JP/1407.html:text/html}
}

@article{cortes_support-vector_1995,
	title = {Support-vector networks},
	volume = {20},
	issn = {0885-6125, 1573-0565},
	url = {http://link.springer.com/article/10.1007/BF00994018},
	doi = {10.1007/BF00994018},
	abstract = {Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.},
	language = {en},
	number = {3},
	urldate = {2014-09-07},
	journal = {Machine Learning},
	author = {Cortes, Corinna and Vapnik, Vladimir},
	month = sep,
	year = {1995},
	keywords = {Artificial Intelligence (incl. Robotics), Automation and Robotics, Computing Methodologies, efficient learning algorithms, Language Translation and Linguistics, neural networks, pattern recognition, polynomial classifiers, radial basis function classifiers, Simulation and Modeling},
	pages = {273--297},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/829ZSSC5/Cortes and Vapnik - 1995 - Support-vector networks.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/VWWP3J8M/10.html:text/html}
}

@article{strobl_conditional_2008,
	title = {Conditional variable importance for random forests},
	volume = {9},
	copyright = {2008 Strobl et al; licensee {BioMed} Central Ltd.},
	issn = {1471-2105},
	url = {http://www.biomedcentral.com/1471-2105/9/307/abstract},
	doi = {10.1186/1471-2105-9-307},
	abstract = {Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.
{PMID}: 18620558},
	language = {en},
	number = {1},
	urldate = {2014-09-07},
	journal = {{BMC} Bioinformatics},
	author = {Strobl, Carolin and Boulesteix, Anne-Laure and Kneib, Thomas and Augustin, Thomas and Zeileis, Achim},
	month = jul,
	year = {2008},
	pmid = {18620558},
	pages = {307},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/8AJKCSXB/Strobl et al. - 2008 - Conditional variable importance for random forests.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/RW9V8GNG/307.html:text/html}
}

@article{strobl_bias_2007,
	title = {Bias in random forest variable importance measures: Illustrations, sources and a solution},
	volume = {8},
	copyright = {2007 Strobl et al; licensee {BioMed} Central Ltd.},
	issn = {1471-2105},
	shorttitle = {Bias in random forest variable importance measures},
	url = {http://www.biomedcentral.com/1471-2105/8/25/abstract},
	doi = {10.1186/1471-2105-8-25},
	abstract = {Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories.
{PMID}: 17254353},
	language = {en},
	number = {1},
	urldate = {2014-09-07},
	journal = {{BMC} Bioinformatics},
	author = {Strobl, Carolin and Boulesteix, Anne-Laure and Zeileis, Achim and Hothorn, Torsten},
	month = jan,
	year = {2007},
	pmid = {17254353},
	pages = {25},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/WM52SWXQ/Strobl et al. - 2007 - Bias in random forest variable importance measures.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/IUKRPUSB/25.html:text/html}
}

@article{boulesteix_overview_2012,
	title = {Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics},
	volume = {2},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/widm.1072/full},
	number = {6},
	urldate = {2014-09-07},
	journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	author = {Boulesteix, Anne-Laure and Janitza, Silke and Kruppa, Jochen and König, Inke R.},
	year = {2012},
	pages = {493--507},
	file = {TR.pdf:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/DEPZHA5C/TR.pdf:application/pdf}
}

@article{pang_gene_2012,
	title = {Gene selection using iterative feature elimination random forests for survival outcomes},
	volume = {9},
	issn = {1545-5963},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3495190/},
	doi = {10.1109/TCBB.2012.63},
	abstract = {Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis. Additionally, we have shown the advantages of our approach over single-gene based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to best utilize the information available from microarray data with survival outcomes.},
	number = {5},
	urldate = {2014-09-07},
	journal = {{IEEE}/{ACM} transactions on computational biology and bioinformatics / {IEEE}, {ACM}},
	author = {Pang, Herbert and George, Stephen L. and Hui, Ken and Tong, Tiejun},
	year = {2012},
	pmid = {22547432},
	pmcid = {PMC3495190},
	pages = {1422--1431},
	file = {PubMed Central Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/NQ283F9K/Pang et al. - 2012 - Gene selection using iterative feature elimination.pdf:application/pdf}
}

@article{menze_comparison_2009,
	title = {A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data},
	volume = {10},
	issn = {1471-2105},
	url = {http://link.springer.com/article/10.1186/1471-2105-10-213},
	doi = {10.1186/1471-2105-10-213},
	abstract = {Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task.},
	language = {en},
	number = {1},
	urldate = {2014-09-07},
	journal = {{BMC} Bioinformatics},
	author = {Menze, Bjoern H. and Kelm, Michael B. and Masuch, Ralf and Himmelreich, Uwe and Bachert, Peter and Petrich, Wolfgang and Hamprecht, Fred A.},
	month = dec,
	year = {2009},
	keywords = {Algorithms, bioinformatics, Combinatorial Libraries, Computational Biology/Bioinformatics, Computer Appl. in Life Sciences, Microarrays},
	pages = {1--16},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/DGGXNN85/Menze et al. - 2009 - A comparison of random forest and its Gini importa.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/K2WMFQM7/10.html:text/html}
}

@article{cao_support_2003,
	title = {Support vector machine with adaptive parameters in financial time series forecasting},
	volume = {14},
	issn = {1045-9227},
	doi = {10.1109/TNN.2003.820556},
	abstract = {A novel type of learning machine called support vector machine ({SVM}) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of {SVM} in financial time series forecasting. The feasibility of applying {SVM} in financial forecasting is first examined by comparing it with the multilayer back-propagation ({BP}) neural network and the regularized radial basis function ({RBF}) neural network. The variability in performance of {SVM} with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into {SVM}. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, {SVM} outperforms the {BP} neural network in financial forecasting, and there are comparable generalization performance between {SVM} and the regularized {RBF} neural network. Furthermore, the free parameters of {SVM} have a great effect on the generalization performance. {SVM} with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard {SVM} in financial forecasting.},
	number = {6},
	journal = {{IEEE} Transactions on Neural Networks},
	author = {Cao, L. J. and Tay, F.E.H.},
	month = nov,
	year = {2003},
	keywords = {adaptive estimation, adaptive parameters, backpropagation, Chicago Mercantile Market, Contracts, economic forecasting, financial time series forecasting, forecasting theory, generalisation (artificial intelligence), Machine learning, Mechanical engineering, multilayer backpropagation neural network, Multi-layer neural network, neural networks, pattern recognition, Predictive models, radial basis function networks, radial basis function neural network, regression estimation, Risk management, stock markets, Support vector machine, support vector machines, time series},
	pages = {1506--1518},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/W8QVWHIB/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/KRBEJE22/Cao and Tay - 2003 - Support vector machine with adaptive parameters in.pdf:application/pdf}
}

@article{chapotot_automated_2010,
	title = {Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules},
	volume = {24},
	copyright = {Copyright © 2009 John Wiley \& Sons, Ltd.},
	issn = {1099-1115},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/acs.1147/abstract},
	doi = {10.1002/acs.1147},
	abstract = {The classification of sleep–wake stages suffers from poor standardization in scoring criteria and heterogeneous conditioning of polysomnographic signals. To improve applicability of fully automated sleep staging, we have designed a formal classification framework to rigorously (1) select robust candidate features, (2) emulate artificial neural network classifiers, and (3) assign sleep–wake stages using flexible decision rules. An extensive database of 48 {PSG} records scored in 20 s epochs by two independent clinicians was used. A small subset of 2 s elementary epochs representative of each stages with unequivocal expert scores was selected to form a limited set of learning exemplars. From 16 statistical, spectral and non-linear candidate features extracted in 2 s epochs from {EEG} and {EMG} signals, a sequential forward search selected an optimal set of five features with a 22\% error rate. Multiple layer perceptrons were trained from this optimal feature set while classification accuracy was assessed using the unequivocal instance subset. A simple majority vote among 10 consecutive classifier outputs ensured a final scoring resolution comparable to that of the experts. Poor classification performance was obtained for movement time, wakefulness, and intermediate sleep stages with a 36±15\% error rate (Cohen's kappa 0.48±0.18). In contrast, deep and paradoxical sleep was classified with an 82\% accuracy not far from inter-expert expert agreement (83±3\%). Significant improvements should be expected using a larger learning set compensating for a high inter-individual variability, and decision rules incorporating more domain-knowledge. Copyright © 2009 John Wiley \& Sons, Ltd.},
	language = {en},
	number = {5},
	urldate = {2014-09-07},
	journal = {International Journal of Adaptive Control and Signal Processing},
	author = {Chapotot, Florian and Becq, Guillaume},
	month = may,
	year = {2010},
	keywords = {data mining, decision rule, Machine learning, Polysomnography, signal processing, Sleep},
	pages = {409--423},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/CI49RXNK/Chapotot and Becq - 2010 - Automated sleep–wake staging combining robust feat.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/KF8V6KA5/abstract.html:text/html}
}

@inproceedings{ebrahimi_automatic_2008,
	title = {Automatic sleep stage classification based on {EEG} signals by using neural networks and wavelet packet coefficients},
	doi = {10.1109/IEMBS.2008.4649365},
	abstract = {Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as {EEG}, {EMG}, {ECG} and {EOG} are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that {EEG} signals are similar in Stage1 of non-rapid eye movement ({NREM}) sleep and rapid eye movement ({REM}) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage1 + {REM}, Stage 2 and Slow Wave Stage based on the {EEG} signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 ± 4.5\%, a of sensitivity 84.2+3.9\% and an accuracy of 93.0 ± 4.0\%.},
	booktitle = {30th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society, 2008. {EMBS} 2008},
	author = {Ebrahimi, F. and Mikaeili, Mohammad and Estrada, E. and Nazeran, H.},
	month = aug,
	year = {2008},
	keywords = {Algorithms, Artificial neural networks, Diagnosis, Computer-Assisted, Electrocardiography, Electroencephalography, Electromyography, Electrooculography, Humans, Inspection, Medical treatment, neural networks, Neural Networks (Computer), Oscillometry, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Sleep, Sleep Stages, Wavelet packets},
	pages = {1151--1154},
	file = {IEEE Xplore Abstract Record:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/XCTB4SQK/abs_all.html:text/html;IEEE Xplore Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/ANZ45BUX/Ebrahimi et al. - 2008 - Automatic sleep stage classification based on EEG .pdf:application/pdf}
}

@article{diekelmann_memory_2010,
	title = {The memory function of sleep},
	volume = {11},
	copyright = {© 2010 Nature Publishing Group},
	issn = {1471-003X},
	url = {http://www.nature.com/nrn/journal/v11/n2/full/nrn2762.html},
	doi = {10.1038/nrn2762},
	abstract = {Sleep has been identified as a state that optimizes the consolidation of newly acquired information in memory, depending on the specific conditions of learning and the timing of sleep. Consolidation during sleep promotes both quantitative and qualitative changes of memory representations. Through specific patterns of neuromodulatory activity and electric field potential oscillations, slow-wave sleep ({SWS}) and rapid eye movement ({REM}) sleep support system consolidation and synaptic consolidation, respectively. During {SWS}, slow oscillations, spindles and ripples — at minimum cholinergic activity — coordinate the re-activation and redistribution of hippocampus-dependent memories to neocortical sites, whereas during {REM} sleep, local increases in plasticity-related immediate-early gene activity — at high cholinergic and theta activity — might favour the subsequent synaptic consolidation of memories in the cortex.},
	language = {en},
	number = {2},
	urldate = {2014-09-08},
	journal = {Nature Reviews Neuroscience},
	author = {Diekelmann, Susanne and Born, Jan},
	month = feb,
	year = {2010},
	pages = {114--126},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/6S2UIPTX/Diekelmann and Born - 2010 - The memory function of sleep.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/FVMUBM98/nrn2762.html:text/html}
}

@article{rodriguez_support_2005,
	series = {{AI}-2004, Cambridge, England, 13th-15th December 2004},
	title = {Support vector machines of interval-based features for time series classification},
	volume = {18},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705105000432},
	doi = {10.1016/j.knosys.2004.10.007},
	abstract = {In previous works, a time series classification system has been presented. It is based on boosting very simple classifiers, formed only by one literal. The used literals are based on temporal intervals.

The obtained classifiers were simply a linear combination of literals, so it is natural to expect some improvements in the results if those literals were combined in more complex ways. In this work we explore the possibility of using the literals selected by the boosting algorithm as new features, and then using a {SVM} with these metafeatures. The experimental results show the validity of the proposed method.},
	number = {4–5},
	urldate = {2014-09-08},
	journal = {Knowledge-Based Systems},
	author = {Rodríguez, Juan José and Alonso, Carlos J. and Maestro, José A.},
	month = aug,
	year = {2005},
	keywords = {Boosting, Kernel methods, Time series classification},
	pages = {171--178},
	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/I9EZDS2J/Rodríguez et al. - 2005 - Support vector machines of interval-based features.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4B4QII39/S0950705105000432.html:text/html}
}

@article{pack_novel_2007,
	title = {Novel method for high-throughput phenotyping of sleep in mice},
	volume = {28},
	copyright = {Copyright © 2007 the American Physiological Society},
	issn = {1094-8341, 1531-2267},
	url = {http://physiolgenomics.physiology.org/content/28/2/232},
	doi = {10.1152/physiolgenomics.00139.2006},
	abstract = {Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram ({EEG}) and electromyogram ({EMG}) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of ≥40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by {EEG}/{EMG} recording. The average agreement over 8,640 10-s epochs in 24 h is 92\% (n = 7 mice) with agreement in individual mice being 88–94\%. Average {EEG}/{EMG} determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep and {EEG}/{EMG}-defined sleep was only 1.0 min (95\% confidence interval for mean bias −0.06 to +2.6 min). The standard deviation of differences (precision) was 7.5 min per 2-h interval with 95\% limits of agreement ranging from −13.7 to +15.7 min. Although bias significantly varied by time of day (P = 0.0007), the magnitude of time-of-day differences was not large (average bias during lights on and lights off was +5.0 and −3.0 min per 2-h interval, respectively). This method has applications in chemical mutagenesis and for studies of molecular changes in brain with sleep/wakefulness.},
	language = {en},
	number = {2},
	urldate = {2014-09-08},
	journal = {Physiological Genomics},
	author = {Pack, Allan I. and Galante, Raymond J. and Maislin, Greg and Cater, Jacqueline and Metaxas, Dimitris and Lu, Shan and Zhang, Lin and Smith, Randy Von and Kay, Timothy and Lian, Jie and Svenson, Karen and Peters, Luanne L.},
	month = feb,
	year = {2007},
	pmid = {16985007},
	note = {Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram ({EEG}) and electromyogram ({EMG}) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of ≥40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by {EEG}/{EMG} recording. The average agreement over 8,640 10-s epochs in 24 h is 92\% ( n = 7 mice) with agreement in individual mice being 88–94\%. Average {EEG}/{EMG} determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep and {EEG}/{EMG}-defined sleep was only 1.0 min (95\% confidence interval for mean bias −0.06 to +2.6 min). The standard deviation of differences (precision) was 7.5 min per 2-h interval with 95\% limits of agreement ranging from −13.7 to +15.7 min. Although bias significantly varied by time of day ( P = 0.0007), the magnitude of time-of-day differences was not large (average bias during lights on and lights off was +5.0 and −3.0 min per 2-h interval, respectively). This method has applications in chemical mutagenesis and for studies of molecular changes in brain with sleep/wakefulness.},
	pages = {232--238},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/XMMFF5TN/Pack et al. - 2007 - Novel method for high-throughput phenotyping of sl.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/66MD7FI2/232.html:text/html}
}

@article{mieda_orexin_2004,
	title = {Orexin peptides prevent cataplexy and improve wakefulness in an orexin neuron-ablated model of narcolepsy in mice},
	volume = {101},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/101/13/4649},
	doi = {10.1073/pnas.0400590101},
	abstract = {Narcolepsy-cataplexy is a neurological disorder associated with the inability to maintain wakefulness and abnormal intrusions of rapid eye movement sleep-related phenomena into wakefulness such as cataplexy. The vast majority of narcoleptic-cataplectic individuals have low or undetectable levels of orexin (hypocretin) neuropeptides in the cerebrospinal fluid, likely due to specific loss of the hypothalamic orexin-producing neurons. Currently available treatments for narcolepsy are only palliative, symptom-oriented pharmacotherapies. Here, we demonstrate rescue of the narcolepsy-cataplexy phenotype of orexin neuron-ablated mice by genetic and pharmacological means. Ectopic expression of a prepro-orexin transgene in the brain completely prevented cataplectic arrests and other abnormalities of rapid eye movement sleep in the absence of endogenous orexin neurons. Central administration of orexin-A acutely suppressed cataplectic behavioral arrests and increased wakefulness for 3 h. These results indicate that orexin neuron-ablated mice retain the ability to respond to orexin neuropeptides and that a temporally regulated and spatially targeted secretion of orexins is not necessary to prevent narcoleptic symptoms. Orexin receptor agonists would be of potential value for treating human narcolepsy.},
	language = {en},
	number = {13},
	urldate = {2014-09-08},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	author = {Mieda, Michihiro and Willie, Jon T. and Hara, Junko and Sinton, Christopher M. and Sakurai, Takeshi and Yanagisawa, Masashi},
	month = mar,
	year = {2004},
	pmid = {15070772},
	pages = {4649--4654},
	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4Q3NPZ2M/Mieda et al. - 2004 - Orexin peptides prevent cataplexy and improve wake.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/DGBKJEEK/4649.html:text/html}
}