基于生成对抗网络的心电数据增强算法研究

In Most of the standard ECG databases that have been published so far have serious category imbalances, which has brought great obstacles to the improvement of the accuracy of ECG classification and recognition. In order to solve this problem, this paper expands the minority samples of the data set by building a generative adversarial network model based on BILSTM and CNN, synthesizing high-quality and complex types of ECG data. The simulation results show that the ECG signal generated by the generated model is close to the real signal in waveform shape and frequency domain characteristics. In order to further verify the validity of the generated data, the CNN classifier is designed to detect the changes of the classification index value accuracy and macro-F1 before and after the expansion of the ECG data set. The experimental results show that the data enhancement algorithm proposed in this paper can effectively improve the ECG Classification accuracy.