Application of ECG Signal Denoising and T-wave Automatic Detection Based on Computer Deep Learning

Abstract

To address the problems of time-consuming, laborious and costly ECG expert diagnosis in the long-term massive ECG signal automatic denoising and T-wave automatic detection system, resulting in the difficulty to extract features, as well as the poor adaptability and low accuracy of the abnormal diagnosis model, we proposed an ECG signal denoising and T-wave automatic detection method based on deep learning. The method mainly includes four steps as follows: ECG signal denoising preprocessing, segmentation of ECG signal and unification of sampling points, unsupervised heartbeat feature learning, denoising and T-wave automatic detection. The structure of the deep faith network FCMDBN model and the learning denoising and T-wave automatic detection algorithm are proposed. Based on the MIT-BIH heart rate anomaly database, the simulation experiment shows that the signal diagnosis method proposed in this paper has relatively high adaptability and accuracy compared with the denoising and T-wave automatic detection by artificial design based on traditional ECG features.