Electrocardiogram Signal Classification for Heart Disease Identification - Phung, Minh; Nguyen, Hieu; Tran, Linh (Engineering, Technology & Applied Science Research, 2025)
Cardiovascular diseases are among the leading causes of death worldwide, and diagnosing them based on Electrocardiogram (ECG) signals traditionally requires highly experienced doctors. However, with advancements in artificial intelligence, machine learning models have been increasingly used to assist in diagnosing cardiovascular diseases without the need for direct doctor evaluation. Identifying heart disease through ECG signals presents several challenges, such as measurement noise and the continuous nature of ECG data. In this paper, we propose a method to address these challenges and achieve high diagnostic efficiency. To evaluate our approach, we utilize the MIT-BIH Arrhythmia Database. Experimental results demonstrate that our algorithm can classify ECG signals into five categories with significantly higher accuracy compared to previous methods.