[3H4-OS-10b] OS-10

[3H4-OS-10b-02] Lightweight myocardial infarction estimation using tensor electrocardiogram analysis and machine learning

Osamu Saisho¹, 〇Akihiro Shiozawa², Shingo Tsukada¹, Takuji Oba² (1. Nippon Telegraph and Telephone, 2. NTT DATA Mathematical Systems Inc.)

Keywords:ECG, TCG, Myocardial Infarction, Machine Learning

Many recent studies have reported high accuracy in classifying heart diseases and localizing affected regions using large-scale electrocardiogram (ECG) data with deep neural networks (DNNs). However, these methods often rely on residual networks (ResNet) and deep stacks of one-dimensional convolutions. Such architectures demand substantial computational resources. This paper employs tensor electrocardiogram (TCG) technology to extract ECG shape features. As a result, we achieve a lighter, whitebox model whose accuracy is comparable to blackbox DNN-based approaches. Experimental results show that we achieve a macro AUC of 0.933, which is close to ResNet's 0.937 and better than LSTM's 0.927.