Cardiovascular Disease Prediction using Single-Lead ECG Data

Chaeyoon Park, Gihun Joo, Suhwan Ji, Junbeom Park, Junho Baek, Hyeonseung Im

http://doi.org/10.5626/JOK.2024.51.10.928

The most representative approach to diagnosing cardiovascular disease is to analyze electrocardiogram (ECG), and most ECG data measured in hospitals consist of 12 leads. However, wearable healthcare devices usually measure only single-lead ECG, which has limitations in diagnosing cardiovascular disease. Therefore, in this paper, we conducted a study to predict common cardiovascular diseases such as atrial fibrillation (AF), left bundle branch block (LBBB), and right bundle branch block (RBBB) using a single lead that could be measured with a wearable healthcare device. For experiments, we used a convolutional neural network model and measured its performance using various leads in terms of AUC and F1-score. For AF, LBBB, and RBBB, average AUC values were 0.966, 0.971, and 0.965, respectively, and average F1-scores were 0.867, 0.816, and 0.848, respectively. These experimental results confirm the possibility of diagnosing cardiovascular disease using only a single lead ECG that can be obtained with wearable healthcare devices.