Matlab toolbox for calculating Heart-Rate Variability metrics on ECG signals
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README.md

mhrv

mhrv is a matlab toolbox for calculating Heart-Rate Variability (HRV) metrics from both ECG signals and RR-interval time series. The toolbox works with ECG data in the PhysioNet [1] WFDB data format.

Features

  • WFDB wrappers and helpers. A small subset of the PhysioNet WFDB tools are wrapped with matlab functions, to allow using them directly from matlab.

    • gqrs - A QRS detection algorithm.
    • rdsamp - For reading PhysioNet signal data into matlab.
    • rdann - For reading PhysioNet annotation data into matlab.
    • wrann - For writing PhysioNet annotation data from matlab datatypes.
    • wfdb_header - Read record metadata from a WFDB header file (.hea).
  • ECG signal processing. Peak detection and RR interval extraction from ECG data in PhysioNet format.

    • rqrs - Detection of R-peaks in ECG signals (based on PhysioNet's gqrs). Configurable for use with both human and animal ECGs.
    • jqrs/wjqrs - An ECG peak-detector based on a modified Pan & Tompkins algorithm and a windowed version.
    • bpfilt- Bandpass filtering for removing noise artifacts from ECG signals.
    • ecgrr - Construction of RR intervals from ECG data in PhysioNet format.
    • qrs_compare - Comparison of QRS detections to reference annotations and calculation of quality measures like Sensitivity, PPV.
  • RR-intervals signal processing. Ectopic beat rejection, frequency filtering, nonlinear dynamic and fractal analysis.

    • filtrr - Filtering of RR interval time series to detect ectopic (out of place) beats.
    • dfa - Detrended Fluctuation Analysis, a method of estimating the fractal scaling exponent of a signal [3].
    • mse - Multiscale Sample Entropy, a measure of the complexity of the signal computed on multiple time scales [4].
    • sample_entropy - Sample Entropy, a measure of the irregularity of a signal.
  • HRV Metrics: Calculating quantitative measures that indicate the activity of the heart based on RR intervals using all standard HRV metrics defined in the literature (see e.g. [2]).

    • hrv_time - Time Domain: AVNN, SDNN, RMSSD, pNNx.
    • hrv_freq - Frequency Domain:
      • Total and normalized power in (configurable) VLF, LF, HF and custom user-defined bands.
      • Spectral power estimation using Lomb, Auto Regressive, Welch and FFT methods.
      • Additional frequency-domain features: LF/HF ratio, LF and HF peak frequencies, power-law scaling exponent (beta).
    • hrv_nonlinear - Nonlinear methods:
      • Short- and long-term scaling exponents (alpha1, alpha2) based on DFA.
      • Sample Entropy and Multiscale sample entropy (MSE).
      • Poincaré plot metrics (SD1, SD2).
    • hrv_fragmentation - Time-domain RR interval fragmentation analysis [5].
  • Configuration: The toolbox is fully configurable with many user-adjustable parameters.

    • The configuration files are in human-readable YAML format which is easy to edit and extend.
    • The user can create custom configurations files based on the defatuls.yml file (only overriding what's required).
    • Custom configuration files can be loaded with a single call which updates the defaults for the entire toolbox. This allows simple, reproducible analysis of different datasets that require different analysis configurations.
    • The settings for any of the functions can either be configured globally with configuration yml files or on a per-call basis with matlab-style key-value argument pairs.
  • Plotting: All toolbox functions support plotting their output for data visualization. The plotting code is separated from the algorithmic code in order to simplify embedding this toolbox in other matlab applications.

  • Top-level analysis functions: These functions work with PhysioNet records and allow streamlined HRV analysis by composing the functions of this toolbox.

    • mhrv - Analyzes a single PhysioNet record (ECG data or annotations), optionally split into multiple analysis windows. Extracts all supported HRV features and optionally generates plots.
    • mhrv_batch - Analyzes many PhysioNet records (ECG data or annotations) which can be further separated into user-defined groups (e.g. Control, Test). Automatically computes HRV metrics per group and generates a comparative summary of the HRV features in each group.

Requirements

  • Matlab with Signal Processing toolbox. Should work on Matlab R2014b or newer.
  • The PhysioNet WFDB tools. The toolbox can install this for you.

Installation

  1. Clone the repo or download the source code.

  2. From MATLAB, run the mhrv_init function. This function will:

    • Check for the presence of the WFDB tools in your system PATH. If WFDB tools are not detected, it will attempt to automatically download them for you into the folder bin/wfdb under the repository root.
    • Set up your MATLAB path to include the code from this toolbox.

Manual WFDB Installation (Optional)

The above steps should be enough to get most users started. If however you don't want mhrv_init to download the WFDB tools for you, or the automatic installation fails for some reason, you can install them yourself.

Once you have the binaries, place them in some folder on your $PATH or somewere under the repo's root folder (bin/wfdb would be a good choice as it's .gitignored) and they will be found and used automatically. Or, if you would like to manually specify a path outside the repo which contains the WFDB binaries (e.g. /usr/local/bin for a homebrew install), you can edit cfg/defaults.yml and set the mhrv.paths.wfdb_path variable to the desired path.

For linux users it's recommended to install from source as the binaries provided on the PhysioNet website are very outdated.

Documentation

Documentation is available on readthedocs.

Usage

Exaple of calculating HRV measures for a PhysioNet record downloaded from PhysioNet (in this case from mitdb):

% Download the mitdb/111 record from PhysioNet to local folder named 'db'
>> download_wfdb_records('mitdb', '111', 'db');

% Run HRV analysis
>> mhrv('db/mitdb/111', 'window_minutes', 15, 'plot', true);

Will give you:

[0.000] >> mhrv: Processing ECG signal from record db/mitdb/111 (ch. 1)...
[0.000] >> mhrv: Signal duration: 00:30:05.000 [HH:mm:ss.ms]
[0.010] >> mhrv: Analyzing window 1 of 2...
[0.010] >> mhrv: [1/2] Detecting QRS end RR intervals...
[0.810] >> mhrv: [1/2] Filtering RR intervals...
[0.840] >> mhrv: [1/2] 1039 NN intervals, 6 RR intervals were filtered out
[0.840] >> mhrv: [1/2] Calculating time-domain metrics...
[0.920] >> mhrv: [1/2] Calculating frequency-domain metrics...
[1.180] >> mhrv: [1/2] Calculating nonlinear metrics...
[1.430] >> mhrv: [1/2] Calculating fragmentation metrics...
[1.490] >> mhrv: Analyzing window 2 of 2...
[1.490] >> mhrv: [2/2] Detecting QRS end RR intervals...
[2.080] >> mhrv: [2/2] Filtering RR intervals...
[2.100] >> mhrv: [2/2] 1057 NN intervals, 8 RR intervals were filtered out
[2.100] >> mhrv: [2/2] Calculating time-domain metrics...
[2.140] >> mhrv: [2/2] Calculating frequency-domain metrics...
[2.240] >> mhrv: [2/2] Calculating nonlinear metrics...
[2.450] >> mhrv: [2/2] Calculating fragmentation metrics...
[2.490] >> mhrv: Building statistics table...
[2.520] >> mhrv: Displaying Results...
               RR      NN      AVNN      SDNN     RMSSD      pNN50       SEM      TOTAL_POWER_LOMB    VLF_POWER_LOMB    LF_POWER_LOMB    HF_POWER_LOMB    LF_NORM_LOMB    HF_NORM_LOMB    LF_TO_HF_LOMB    LF_PEAK_LOMB    HF_PEAK_LOMB      SD1       SD2       alpha1      alpha2      beta      SampEn       PIP        IALS        PSS       PAS  
              ____    ____    ______    ______    ______    _______    _______    ________________    ______________    _____________    _____________    ____________    ____________    _____________    ____________    ____________    _______    ______    ________    ________    _______    _______    _______    _________    ______    ______
    1         1045    1039    858.96    30.961    33.622     14.162    0.96054    333.22              67.098            23.574           242.55           8.8583          91.142          0.097193          0.046667       0.16667          23.786    36.745     0.65937     0.72845    -1.2471      1.835     53.321      0.53468    61.598    12.512
    2         1065    1057    841.86    40.182    32.306     12.784     1.2359    388.66               132.7            32.031           223.93           12.514          87.486           0.14304          0.043333       0.16667          22.855    51.996     0.70064     0.92309    -1.6706     1.6483     52.318      0.52462    57.332    15.137
    Mean      1055    1048    850.41    35.572    32.964     13.473     1.0982    360.94              99.899            27.802           233.24           10.686          89.314           0.12012             0.045       0.16667           23.32    44.371        0.68     0.82577    -1.4588     1.7417     52.819      0.52965    59.465    13.825
    SE          10       9    8.5503    4.6103    0.6578    0.68888     0.1377    27.723              32.801            4.2286           9.3065           1.8278          1.8278          0.022923         0.0016667             0         0.46545    7.6255    0.020637    0.097316    0.21176    0.09336    0.50131    0.0050304    2.1328    1.3126
    Median    1055    1048    850.41    35.572    32.964     13.473     1.0982    360.94              99.899            27.802           233.24           10.686          89.314           0.12012             0.045       0.16667           23.32    44.371        0.68     0.82577    -1.4588     1.7417     52.819      0.52965    59.465    13.825
[2.580] >> mhrv: Generating plots...
[4.930] >> mhrv: Finished processing record db/mitdb/111.

The window_minutes parameter allow splitting the signal into windows and calculating all metrics per window. You can pass in an empty array [] to disable spliting.

Example plots (generated by the example above):

  • ECG R-peak detection Example Peak Detection
  • RR interval time series filtering Example RR filtering
  • Time-domain HRV Metrics Example time domain metrics
  • Spectrum of interval time series Example NN spectrum
  • Nonlinear HRV Metrics Example nonlinear metrics
  • Poincaré plot and ellipse fitting Example poincaré plot

Citing

This toolbox, initially called rhrv, was created as part of my MSc research thesis. It was then renamed and updated to be used as the basis of the PhysioZoo platform for HRV analysis of human and animal data.

To use it in you own research, please cite:

  • Rosenberg, A. A. (2018) ‘Non-invasive in-vivo analysis of intrinsic clock-like pacemaker mechanisms: Decoupling neural input using heart rate variability measurements.’ MSc Thesis. Technion, Israel Institute of Technology.

  • Behar J. A., Rosenberg A. A. et. al. (2018) ‘PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data.’ Frontiers in Physiology.

Similar projects

Several other projects exist with various levels of overlapping functionality and purpose.

Attribution

Some of the code in lib/ was created by others, used here as dependencies. Original author attribution exists in the source files.

Contribution

Feel free to send pull requests or open issues via GitHub.

References

  1. Goldberger, A. L. et al. (2000) ‘PhysioBank, PhysioToolkit, and PhysioNet’, Circulation, 101(23), pp. E215-20.
  2. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996) ‘Heart rate variability. Standards of measurement, physiological interpretation, and clinical use.’, European Heart Journal, 17(3), pp. 354–81.
  3. Peng, C.-K., Hausdorff, J. M. and Goldberger, A. L. (2000) ‘Fractal mechanisms in neuronal control: human heartbeat and gait dynamics in health and disease, Self-organized biological dynamics and nonlinear control.’ Cambridge: Cambridge University Press.
  4. Costa, M. D., Goldberger, A. L. and Peng, C.-K. (2005) ‘Multiscale entropy analysis of biological signals’, Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 71(2), pp. 1–18.
  5. Costa, M. D., Davis, R. B. and Goldberger, A. L. (2017) ‘Heart Rate Fragmentation : A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics’, Frontiers in Physiology, 8(May), pp. 1–13.