Automated arrhythmia classification based on a combination network of CNN and LSTM

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.
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research