Exploration of Depth Algorithm Applied to Time-Frequency Image Processing Method of ECG Signal

Abstract

The classification of arrhythmia is of great significance for the prevention and treatment of heart disease. Based on the deep learning algorithm, it has excellent performance in image classification and recognition. The ECG signal is divided into two cases of abnormal interval and abnormal amplitude to perform signal image classification. The time-domain abnormal signal is directly processed into a two-dimensional image set, and the time domain information of the amplitude abnormal signal is Fourier transformed to obtain a two-dimensional time-frequency image set, and different image sets are migrated to VGG16 After the model is reduced by the PCA algorithm, it can clearly distinguish between normal ECG signals and ECG signals with abnormal intervals or amplitude abnormalities. Finally, after a fine-tuned fully connected layer, the abnormal intervals and amplitudes can be obtained. The accuracy rates of abnormal classification were 96.15% and 92.98%, respectively. After the image processing of the ECG signal, this method can effectively distinguish the abnormal signal from the normal signal.