TY  - JOUR
T1  - Cardiovascular Disease Prediction using Single-Lead ECG Data
AU  - Park, Chaeyoon 
AU  - Joo, Gihun 
AU  - Ji, Suhwan 
AU  - Park, Junbeom 
AU  - Baek, Junho 
AU  - Im, Hyeonseung 
JO  - Journal of KIISE, JOK
PY  - 2024
DA  - 2024/1/14
DO  - 10.5626/JOK.2024.51.10.928
KW  - cardiovascular disease
KW  - electrocardiogram
KW  - convolutional neural network
KW  - wearable
healthcare device
AB  - 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.