Automated arrhythmia classification based on a combination network of CNN and LSTM. (March 2020) - Chen, Chen; Hua, Zhengchun; Zhang, Ruiqi; Liu, Guangyuan; Wen, Wanhui
Highlights: The study presents a method for classification of six types of arrhythmia signals. A combination network of convolutional neural network (CNN) and long short-term memory (LSTM) were used. Major feature extraction, feature selection, and classification steps are merged in the deep network. Five-fold cross validation and independent validation with different databases warrants good generalization for system. Abstract: Arrhythmia is an abnormal heartbeat rhythm, and its prevalence increases with age. An electrocardiogram (ECG) is a standard tool for detecting cardiac activity. However, because of the low amplitude, complexity, and non-linearity of the ECG signal, it is difficult to manually perform a rapid and accurate classification. Therefore, an automatic system that can identify different abnormal heartbeats from a large amount of ECG data should be developed for use in the healthcare field. This study proposed an approach based on deep learning that combined convolutional neural networks (CNNs) and long short-term memory networks (LSTM) to automatically identify six types of ECG signals: normal (N) sinus rhythm segments, atrial fibrillation (AFIB), ventricular bigeminy (B), pacing rhythm (P), atrial flutter (AFL), and sinus bradycardia (SBR). The proposed network applied a multi-input structure to process 10 s ECG signal segments and corresponding RR intervals from the MIT-BIH arrhythmia database. With a five-fold cross-validation strategy, this network achieved 99.32 % accuracy. Then, the diversity of the subjects was increased in the training data by supplementing database, improving the previous network model. The method was validated using two additional databases, which are independent of the training database of the network. For the new N and AFIB in additional databases, the proposed method achieved an average accuracy of 97.15 %. The results showed that the proposed model had robust generalization performance and could be used as an auxiliary tool to help clinicians diagnose arrhythmia after training with a larger database.