SEVGGNet-LSTM: A Fused Deep Learning Model for ECG Classification

With the dramatic progress of smart sensing and wearable device, continuous and real-time acquisition of electrocardiograph (ECG) tends to be realized in a convenient way. Data mining of ECG signals has therefore been extensively researched, among which ECG classification is a hot topic. This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, and the weight allocation capability of attention mechanism. The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features. Two databases from different sources and devices are employed for performance validation, and the results well demonstrate the effectiveness and robustness of the proposed algorithm for classifying ECG signals obtained from wearable ECG devices and professional medical equipment.