Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

Abstract

Cardiac arrhythmia is associated with abnormal electrical activities of the heart, which can be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and non-invasive nature, the ECG has been widely used for detecting arrhythmias and there is an urgent need for automatic ECG detection. Up to date, some algorithms have been proposed for automatic classification of cardiac arrhythmias based on the features of the ECG; however, their stratification rate is still poor due to unreliable features of signal characteristics or limited generalization capability of the classifier, and therefore, it remains a challenge for automatic diagnosis of arrhythmias. In this paper, we propose a new method for automatic classification of arrhythmias based on deep neural networks (DNNs). The two DNN models constitutive of residual convolutional modules and bidirectional long short-term memory (LSTM) layers are trained to extract features from raw ECG signals. The extracted features are concatenated to form a feature vector which is trained to do the final classification. The algorithm is evaluated based on the test set of China Physiological Signal Challenge (CPSC) dataset with F1 measure regarded as the harmonic mean between the precision and recall. The resulting overall F1 score is 0.806, FAF score is 0.914 for atrial fibrillation (AF), FBlock score is 0.879 for block, FPC and FST scores are 0.801 and 0.742 for premature contraction and ST-segment change, which demonstrates a good performance that may have potential practical applications.