A Hybrid Deep Model for Automatic Arrhythmia Classification based on LSTM Recurrent Networks

Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term Memory (LSTM) layer, is utilized to recognize arrhythmia. Indeed, the segments centered around R-peaks acquired from the previous step are fed into this network to distinguish various arrhythmias. The LSTM part of the proposed network enables modeling the relation among different heartbeats in a sequence. Experiments on the MIT-BIH databases and Creighton university ventricular tachyarrhythmia show the superiority of our proposed method on arrhythmia detection in comparison with the recent methods proposed for this problem. The performance of the proposed model on test samples is 98.93%, 99.78%, and 99.58% respectively in terms of overall accuracy, sensitivity, and specificity for tackling the problem of 4-class arrhythmia classification. Thus, it outperforms other recent methods with a large margin in terms of accuracy and specificity.