A cascaded step-temporal attention network for ECG arrhythmia classification

To improve the accuracy of arrhythmia diagnosis and reduce the recheck time, we design a cascaded step-temporal attention network called ArrhythmiaNet to classify 15 categories of arrhythmias on electrocardiogram (ECG) signals. In ArrhythmiaNet, the first level contains a convolution layer with step-attention, which recognizes abnormal heartbeat and provides morphological feature expression. The second level is composed of a gated recurrent unit (GRU) with temporal attention, which mines the temporal correlation of long-term rhythm for abnormal rhythm judgement. To share the feature expression, ArrhythmiaNet was trained by end-to-end multitask learning. In the experiment, ArrhythmiaNet and the comparison algorithms were tested on a dataset (819 training samples and 264 test samples) from MIT-BIH arrhythmia database. The results showed that the accuracy of ArrhythmiaNet was 20.3% higher than that of support vector machine (SVM), Naive Bayesian, gradient boost decision tree (GBDT) and random forest (RF), and 8.2% higher than that of long-term memory network (LSTM) and recurrent neural network (RNN). Compared with 1-dimension convolutional neural network (1D-CNN), ArrhythmiaNet obtained similar overall accuracy, higher recall and precision. Compared to the genetic ensemble of SVM classifiers and evolutionary neural system, ArrhythmiaNet has much lower complexity than them with competitive accuracy. Besides, ArrhythmiaNet has higher interpretability in arrhythmias diagnosis.