The Role of Morphology and Heart Rate Variability Features in Detecting Arrhythmias from Short ECGs

Abstract

Detecting heart arrhythmias from short Electrocardiogram (ECG) recordings remains challenging since recordings are short and contaminated by noise. ECG morphology features and Heart Rate Variability (HRV) time and frequency domain features are widely used for classifying short ECG recordings. Here we investigate the relative roles of ECG morphology features and HRV time and frequency domain features in classifying short ECG recordings provided by the 2017 PhysioNet/Computing in Cardiology Challenge. The classification is performed separately by four machine learning models: Logistic Regression, Decision Tree, K Nearest Neighbors, and Convolutional Neural Network (CNN). Our best classification score is obtained using the deep learning 1-dimensional CNN model trained on HRV time domain features combined with ECG morphology features. It gives an overall F1 score of 0.70 and 0.73 for the cross validation and hidden test respectively when considering the average classification performance over all 4 categories: Atrial Fibrillation (AF), normal, other arrhythmias, and noisy signal. We found that HRV time domain features play an important role in detecting AF, normal, and other classes, whereas ECG morphology features play a key role in detecting the noisy class. When HRV frequency domain features are combined with HRV time domain features, they do not improve and often degrade classifications of short ECG recordings compared to classifications using only HRV time domain features. Combining ECG morphology features with HRV time domain features leads to a better classification performance for short ECG recordings. Feature-based deep learning could serve as a viable and less expensive approach for ECG classifications.