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This paper presents a fused deep learning algorithm for ECG classification.It takes advantages of the combined convolutional and recurrent neural networkfor ECG classification, and the weight allocation capability of attentionmechanism. The input ECG signals are firstly segmented and normalized, and thenfed into the combined VGG and LSTM network for feature extraction andclassification. An attention mechanism (SE block) is embedded into the corenetwork for increasing the weight of important features. Two databases fromdifferent sources and devices are employed for performance validation, and theresults well demonstrate the effectiveness and robustness of the proposedalgorithm.

SEVGGNet-LSTM: a fused deep learning model for ECG classification