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. Yet, contrary to heart rate variability measures, a field which has seen the development of standards, advanced toolboxes and software, very little open tools exist for ECG morphological analysis. The primary objective of this work was to identify and implement clinically important digital ECG biomarkers for the purpose of creating a reference toolbox and software for ECG morphological analysis.
The epltd algorithm was used for R-peak detection. We used a zero-phase filter with passband 0.67Hz - 100Hz to remove baseline wander and high frequency noise. We used a Notch filter at 50/60Hz to remove the power-line interference. ECG fiducial points were detected using the well-known open source wavedet algorithm. A total of 22 biomarkers were engineered including 14 extracted from intervals and segments duration and 8 from waves characteristics.
The result of this work consists of a Python toolbox termed
pebm" and its user interface termedPhysioZoo ECG” for data visualization and analysis. The software is available at physiozoo.com under a GNU GPL licence. The pebm toolbox may be used to provide new physiological information on cardiac conduction as well as used as a source of readily handcrafted features for training machine learning models.