HEPLAB is an EEGLAB extension for the automatic detection of cardiac-related events from the raw ECG signal. Users can choose among three different algorithms for R wave detection and one algorithm for T wave detection. An intuitive Graphical User Interface (GUI) displays cardiac events and allows users to manually correct for artifacts. Events can then be exported to EEGLAB's EEG structure to facilitate the posterior analysis of the Heartbeat Evoked Potential (HEP). Although HEPLAB was originally designed as an EEGLAB extension it can also be used as a standalone Matlab toolbox.
How to cite
Perakakis, P. (2019). HEPLAB: a Matlab graphical interface for the preprocessing of the heartbeat-evoked potential (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.2649943
Use with EEGLAB
To install HEPLAB download the latest release from the Github repository and include the unzipped folder in the plugins folder of your EEGLAB distribution. Next time you run EEGLAB by typing
eeglab at the command window, HEPLAB's main folder and subfolders will be automatically included in your Matlab path.
If you wish to use HEPLAB as a standalone toolbox to detect cardiac events and export them for further processing with a different analysis software, you simply have to add manually HEPLAB's main folder and subfolders to Matlab's path.
Note that if you are using HEPLAB as a standalone toolbox, to start the GUI you simply have to type:
heplab to Matlab's command window. The program will then prompt a dialogue box that will let you select a .mat file. This file needs to contain two variables. An
ecgvariable with the raw ECG signal and an
srate variable with the sampling rate. When you are finished with the event detection and artifact correction, instead of using the SAVE EVENTS button, you will have to export the event information using the Save HEP structure menu option.
To open HEPLAB's GUI first you need to import your continuous data into EEGLAB using one of the available options depending on your data format. At the moment, HEPLAB only works with continous data so make sure you take this into account in your analysis workflow and perform cardiac event detection before segmenting your data into epochs.
Once your data is loaded, click on the Tools tab at EEGLAB's main window. You will see HEPLAB as one of the available tools. Clicking on HEPLAB first lets you select your ECG channel and then opens the main GUI:
Once your ECG channel is loaded, you can use the slider to scroll through the signal. The Location box always indicates the starting time point (in seconds) of the data segment displayed in the plot.
By default the ECG signal is displayed in 10-sec windows. You can change the size of the window to display larger —or smaller— portions of the signal by adjusting the Win size parameter. Use the two arrows to navigate through the signal jumping in Win size increments.
The Scale option can be used to multiply the entire signal by a user-defined parameter. This can be useful to invert the signal (type
-1 in the Scale box) or to adjust its amplitude to facilitate visual inspection and artifact correction. By default HEPLAB plots the signal's normalized amplitude in the [0,1] range, obtained by:
ecg = (ecg-min(ecg))/(max(ecg)-min(ecg));
Note that if you accidentally multiply by
0, the ECG signal will be erased and you will have to reload it from the EEG structure or a HEP variable (see Menu options bellow).
The Filter option applies a 2nd order Butterworth filter to the ECG signal. You can select the low and high-cutoff frecuency (in Hz) in the corresponding boxes. Depending on the presence of low and/or fast frecuencies in the original ECG signal, filtering can significantly improve the efficiency of the peak detection algorithms and it is therefore highly recommended before their application.
Note that filtering permanently modifies the data. To recover the original pre-filtered signal you will need to reload it from EEGLAB, or from a .mat file (see Menu options bellow).
The PREVIEW IBIs button opens a new Matlab figure displaying the interbeat intervals (IBIs) resulting from the marked cardiac events. This is very helpful for detecting artifacts that may have escaped a first visual inspection. See the Test case bellow for an example.
The SAVE EVENTS button is used after cardiac-event detection and artifact correction to add the resulting events in EEGLAB's event structure.
Load ECG from eeglab
This option can be used to reload the original ECG signal from EEGLAB. Note that this will permanently erase any previously detected cardiac events. To avoid loing your work, either add the events in the EEG structure using the SAVE EVENTS button or save the entire HEP structure in a .mat file using the Save HEP structure menu option.
Load ECG from .mat file
This option can be used to load an ECG signal contained as a variable in a .mat file. This file must include an
ecgvariable with the raw ECG signal and an
srate variable with the sampling rate.
Use this option to remove all cardiac event marks. Note that applying any peak detection algorithm also removes previously detected events.
Save HEP structure
This option lets you select a filename and save the entire HEP structure, including the raw ECG signal and the cardiac event information.
Load HEP structure
If you have previously saved a file containing a HEP structure, you can use this option to select the file and load all HEP parameters, inlcuding the ECG signal and the cardiac events.
Detect R waves
- ecglab fast and ecglab slow are two very similar peak detection algorithms included in the ECGLAB, developed by the group of João Luiz Azevedo de Carvalho at the University of Brasilia, DF, Brasil. Details about the algorithm are given in Carvalho et al., 2002.
- Pan-Tompkin implements the Pan & Tompkins, 1985 algorith using the function
BioSigKit.PanTompkins()that is distributed with the BioSigKit toolbox, developed by Hooman Sedghamiz.
Detect T waves
- An implementation of the Multilevel Teager Energy Operator described in Sedghamiz & Santonocito, 2015. The algorithm used in HEPLAB is a modified version of the function
BioSigKit.MTEO_qrstAlg()that is distributed with the BioSigKit toolbox, developed by Hooman Sedghamiz.
We will demonstrate a test case using the sample data in EEG1.set, included in the HEPLAB package.
After loading ECG1.set click on Tools>HEPLAB. In the dialogue box select the last channel that contains the ECG signal:
The selected ECG signal is displayed at the main GUI:
To remove the low and high frecuencies from the particularly signal we apply a filter with 3Hz low, and 30Hz high cutoff frecuency:
Next, we detect the R wave peaks by applying the ecglab fast algorithm:
For this particular signal the fast algorithm works perfect, but there are cases where the other two available algorithms may produce better results. You can easily go through the different algorithms to decide the one that is more suitable for each particular signal.
The detected R wave peaks are marked by red circles. When the Win size is 10 seconds or smaller, next to each red mark you can read the index and the value of each interbeat interval. In this example, the second red mark designates the first interval, which is the difference between the second and the first peak, with a value of 1079 ms: 1079.
We can increase Win size and use the slider or the arrows to rapidly scroll through larger segments of the data.
To demonstate artifact correction, we deliberately remove the R wave peak of the 80th interval:
We can now use the PREVIEW IBIs button to inspect the interval series resulting form the R wave peak detection process:
In this matlab figure we can use the cross tool to mark the problematic interval and identify its index. Here it is the 79th interval since for the representation of the IBI series the first interval is always removed.
We can now go back to the main GUI and manually add a mark on the 80th interval by clicking on top of it. A new inspection of the IBI series indicates that there are no more artifacts in the peak detection process:
Please note that the inspection of the IBI series is an additional measure that should not substitute the careful visual inspection of the entire ECG signal.
A similar process can be used for the detection of the T wave:
Once the peak detection process is concluded, we can use the SAVE EVENTS button to add the detected cardiac events to the EEG structure and save the result as a new dataset. A simple visual inspection of the channel data in EEGLAB shows that the cardiac events have been imported successfully:
It is now fairly easy to use common EEGLAB functions for epoching, baseline removal, artifact rejection and averaging, in order to obtain the Heartbeat Evoked Potential.
HEPLAB saves the structure variables HEP and hepgui in the Matlab workspace. These two structures contain all necessary variables for the execution of the program. The structure hepgui contains all gui objects. The HEP variable contains the ECG signal, the sample rate and other useful parameters.
When you execute the heplab.m script from the command window, the program looks for a HEP variable in the workspace and if it does not exist it opens a dialogue box to let you select a .mat file containing the ecg and srate variables. If a HEP variable is found in the workspace heplab.m will open the GUI using the data in contained within this variable.
The peak detection functions are normally called from the GUI but advanced users can use them directly from the command window or from within their scripts. Please see the Codemap to identify the functions and the help in each one of them for usage instructions. Note that their use without visual inspection and artifact correction is strongly discouraged!
|--- heplab.m <-------- Main module, GUI and Command line |--- eegplugin_heplab.m <-------- Load as EEGLAB plugin |--- Functions <-------- List of scripts and functions |--- heplab_about.m <-------- Contact and license information |--- heplab_calculate_IBIs.m <-------- Calculates IBIs, called by heplab_ecgplot.m |--- heplab_chansel.m <-------- GUI to select ECG channel, called by pop_heplab.m |--- heplab_ecg_filt.m <-------- Applies a Butterworth filter, called by heplab.m |--- heplab_ecgplot.m <-------- Plots the ECG signal, called by heplab.m |--- heplab_edit_events.m <-------- Manualy edit cardiac events, called by heplab.m |--- heplab_fastdetect.m <-------- ECGLAB fast peak detection, called by heplab.m |--- heplab_load_ECG.m <-------- Load ECG from .mat file, called by heplab.m |--- heplab_load_HEP.m <-------- Load HEP data from .mat file, called by heplab.m |--- heplab_pan_tompkin.m <-------- Pan-Tompking peak detection, called by heplab.m |--- heplab_preview_IBIs.m <-------- Plot IBIs, called by heplab.m |--- heplab_save_events.m <-------- Add events to EEG structure, called by heplab.m |--- heplab_slowdetect.m <-------- ECGLAB slow peak detection, called by heplab.m |--- heplab_T_detect_MTEO.m <-------- T wave detection, called by heplab.m |--- heplab_pop_heplab.m <-------- Initiate HEP variables |--- SampleData <-------- Sample data folder |--- EEG1.set <-------- EEGLAB set file used in the test case |--- Documentation <-------- Manual and relevant articles |--- LICENCE <-------- GPL License