基于互信息的多导联心电图排序方法.

The studies of automatic Electrocardiograph(ECG) classification based on convolutional neural networks all extract features from the ECG with the default 12-lead sequence, ignore the influence of lead sequence on feature extraction of convolutional network. To solve the problem, this study proposes a 2-end increasing sorting method based on mutual information, which uses mutual information to measure the correlation between leads. According to the correlation between leads and the characteristics of two-dimensional convolution, the adjacent connections of closely related leads are sorted. The experimental results show that the multi-lead ECG sorting method has achieved remarkable results on three databases and three convolutional network classification models.F1, accuracy, recall, accuracy, and Jacquard's coefficient of the proposed method increases by 0.011,0.009,0.007,0.014, and 0.013, while Hamming's loss decreases by 0.002. [ABSTRACT FROM AUTHOR]