Abstract：As an important clinical application of computer, automatic classification of arrhythmia can effectivelyassist in the diagnosis of cardiovascular diseases. However, the sample imbalance in experiments seriously affects the classification accuracy. At present, the mainstream method to solve the problem of sample imbalance is counter neural network, but it has some problems such as unstable training and mode collapse, and only relies on data for learning, which is lack of certain physiological significance. Therefore, this paper proposed a sample equalization method based on the time-series cardiac model to generate ECG data. The experiment was carried out on the 12 lead dataset provided by China physiological signal challenge (CPSC) in 2018. The deep residual network was used as the classification network to train each lead, and the lead fusion was realized by XGBoost algorithm. After sample equalization, F1 scores of all types were improved, especially in left bundle branch block (LBBB), ST segment depression (STD) and ST segment elevation (STE), which was increased from 0.706, 0.684 and 0.524 to 0.832, 0.809 and 0.618, respectively. In order to verify the universality of this method, PTB dataset was tested independently, and the classification accuracy reached 97.42%. The experimental results showed that the generation of simulation data based on the sequential heart model effectively improved the imbalance of experimental samples.
徐永红, 王金萍, 马佳越. 基于时序心脏模型样本均衡方法的心律失常分类[J]. 中国生物医学工程学报, 2022, 41(3): 301-309.
Xu Yonghong, Wang Jinping, Ma Jiayue. Arrhythmia Classification Based on Time-Series Cardiac Model Sample Equalization. Chinese Journal of Biomedical Engineering, 2022, 41(3): 301-309.
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