Multi-lead Branch Multi-class Arrhythmias Classification Based on Selective Kernel Block

The application of artificial intelligence in electrocardiogram (ECG) diagnosis holds substantial significance. Most ECG classification methods concatenate 12-lead ECG into a 2-D matrix for model input. This study proposed a multi-branch and multi-class model for arrhythmias classification. The model utilizes selective kernel block to independently extract features from each lead, which are fed into Bi-LSTM for fusion. Additionally, batch-free normalization module is employed to reduce estimation shift. Finally, the proposed model achieved an accuracy of 0.871 and a macro F1 score of 0.841 in identifying nine types of arrhythmias.