Abstract
Electrocardiography (ECG), which can trace the electrical activity of the heart noninvasively, is widely used to assess heart health. Accurate interpretation of ECG requires significant amounts of education and training. With the application of deep learning, the accuracy of ECG diagnostic analysis has reached a new high level and even outperforms that of individual cardiologists. And the automated ECG diagnostic model makes it possible for analyzing ECG signals from wearable devices in real time. The common deep learning networks for analyzing ECG are mainly based on convolutional neural networks (CNN), recurrent neural networks (RNN), CNN plus RNN, and some other architectures. This chapter gives a systematical review on the CNN-based, RNN-based, as well as CNN and RNN-based intelligent analysis models for the automated ECG interpretation.
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