PhysioZoo ECG documentation

Digital electrocardiography biomarkers to assess cardiac conduction.

Based on the paper S. Gendelman et al., “PhysioZoo ECG: Digital electrocardiography biomarkers to assess cardiac conduction,” 2021 Computing in Cardiology (CinC), 2021, pp. 1-4, doi: 10.23919/CinC53138.2021.9662857.

Introduction

The electrocardiogram (ECG) is a standard tool used in medical practice for identifying cardiac pathologies. Because the necessary expertise to interpret this tracing is not readily available in all medical institutions or at all in some large areas of developing countries, there is a need to create a data-driven approach that can automatically capture the information contained in this physiological time series. The primary objective of this package is to identify and implement clinically important digital ECG biomarkers for the purpose of creating a reference toolbox and software for ECG morphological analysis.

Description

Few steps are required to extract the morphological ECG biomarkers, thos steps are impelemented in the PEBM toolbox:

  1. ECG Signal Preprocessing - Before computing the ECG morphological biomarkers, prefiltering of the raw ECG time series is performed to remove the baseline wander as well as remove high frequency noise. Specifically, the toolbox include a zero phase second-order infinite impulse response bandpass filter with the passband of 0.67Hz - 100Hz to remove baseline wander and high frequency noise. Also, the toolbox include an optional Notch filter that can be set to 50 or 60Hz to remove the power-line interference.

  2. ECG Fiducial Points Detection - The toolbox include the epltd R-peaks algorithem, and the the well-known wavedet algorithm for ECG fiducial points detection.

  3. Engineering of ECG Biomarkers - Using the fiducial points ECG biomarkers are engineered for individual ECG cycles. When a biomarker cannot be engineered because some fiducial points could not be detected by wavedet then the feature was marked as a NaN. For an ECG channel a total of 14 features are extracted from intervals duration and 8 from waves characteristics to describe the ECG morphology.

![alt text](https://github.com/SheinaG/PECG/blob/master/ecg_wth_bio.png?raw=true)

  1. Summary Statistics - For a specified time window the five summary statistics (median, min, max, Ql and Q3) are computed for all ECG biomarkers.

Installation

Available on pip, with the command: pip install pebm

pip project: pip install -i https://test.pypi.org/simple/ pebm

Requirements

Python >= 3.6

numpy == 1.19.4

mne == 0.23.4

scipy == 1.5.2

wfdb == 3.4.0

All the requirements are installed when the toolbox is installed, no need for additional commands.

System Requirements

To run the wavdet fiucial-points detector matlab runtime (MCR) 2021a is requierd. https://www.mathworks.com/products/compiler/matlab-runtime.html

To run the epltd peak detector additional wfdb toolbox is requierd. https://archive.physionet.org/physiotools/wfdb-linux-quick-start.shtml

Documentation