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Detection of Myocardial Ischemia from ECG Signal using MAX30001
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 35 |
| Year of Publication: 2025 |
| Authors: Jun Wang, Qurat ul Ain, Iffa Imran, Ateeq Ur Rehman, Rameez Asif, Adil Mustafa |
10.5120/ijca2025924617
|
Jun Wang, Qurat ul Ain, Iffa Imran, Ateeq Ur Rehman, Rameez Asif, Adil Mustafa . Detection of Myocardial Ischemia from ECG Signal using MAX30001. International Journal of Computer Applications. 187, 35 ( Aug 2025), 9-14. DOI=10.5120/ijca2025924617
Abstract
Among commonly occurring medical emergencies—such as heart attacks, arrhythmias, valve diseases, and high blood pressure—myocardial ischemia is a critical condition caused by the partial or complete blockage of the coronary arteries, leading to reduced blood flow and insufficient oxygen supply to the heart muscles. This impairs the heart’s ability to pump blood efficiently and can ultimately result in a heart attack or abnormal heart rhythms. The electrocardiogram (ECG), which records the heart's electrical activity, is a standard tool used by cardiologists to diagnose myocardial infarction (MI). However, manual identification of MI from ECG signals is time-consuming and prone to misinterpretation. This study proposes an automated method for detecting MI patterns in ECG signals using wavelet transformation. The analysis reveals that differences in the height between the PR segment and the J-point can effectively distinguish between normal and MI-affected ECGs. Additionally, significant variations were observed in the J-point, R-peak amplitude, and ST-wave in MI patients compared to healthy individuals, as recorded using the MAX30001 ECG sensor.
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