Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks - Li, Zicong; Zhang, Henggui (Frontiers in Cardiovascular Medicine, 2021)
Introduction: Electrocardiograms (ECG) provide information about the electrical activity of the heart which is useful for diagnosing cardiac abnormal functions such as arrhythmias. Recently, several algorithms based on advanced structures of neural networks have been proposed for auto-detecting cardiac arrhythmias, but their performance still needs to be further improved. This study aimed to develop an auto-detection algorithm, which extracts valid features from 12-lead ECG for classifying multiple types of cardiac states. Method: The proposed algorithm consists of the following components: i) a pre-processing component that utilises the frame blocking method to split an ECG recording into frames with a uniform length for all considered ECG recordings; and ii) a binary classifier based on ResNet, which is combined with the attention-based bidirectional long-short term memory (BiLSTM) model. Result: The developed algorithm was trained and tested on ECG data of 9-types of cardiac states, fulfilling a task of multi-label classification. It achieved an averaged F1-score and AUC at 0.908 and 0.974 respectively. Conclusion: The frame blocking and BiLSTM model represented an improved algorithm compared to others in the literature for auto-detecting and classifying multi-types of cardiac abnormalities.