Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM

Abstract

Interpretation of the ECG waves plays a vital role in analysis of cardiovascular diseases. Therefore, many semi and fully-automatic approaches using advanced machine learning techniques for the ECG waves detection are being exhaustively investigated by the researchers. Regardless of advanced machine learning or deep learning techniques, present methods lacks generalization, robustness, reliability and real-time implementation. Moreover, none of the existing study had presented the information related to lead variability which is imperative aspects to handle for accurate detection of ECG waves. Hence, in this paper, we are presenting the new approach of ECG wave segmentation called semantic segmentation, a well-known concept in image segmentation. The proposed approach mainly includes; 1) hybrid channel-mix convolutional and bidirectional LSTM which able to extract temporal dependencies as well as short and long-time dependencies in forward and backward time stamps for semantic segmentation. 2) Experiments with channel-mix convolution to handle lead to lead variability, 3) Experiments with noisy dataset to increase the robustness, reliability and generalization. The proposed hybrid model is implemented on the standard publicly available QT database. The remarkable results with high accuracy ranging from 96 to 98.56 % with average and weighted accuracies of 96.72 % and 95.54 % respectively are obtained for segmentation of ECG waves from continuous raw ECG signal. There are 12.28 %, 8.08 % and 5.77 % increments in weighted average accuracy using proposed hybrid model than LSTM, BiLSTM and double BiLSTM respectively.