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.

References

• V. Gupta, M. Mittal, and V. Mittal, “An Efficient Low Computational Cost Method of R-Peak Detection,” Wirel. Pers. Commun., vol. 118, no. 1, pp. 359–381, 2021, doi: 10.1007/s11277-020-08017-3.
• G. A. Roth et al., “Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study,” J. Am. Coll. Cardiol., vol. 76, no. 25, pp. 2982–3021, 2020, doi: 10.1016/j.jacc.2020.11.010.
• F. feng Li et al., “Pulse signal analysis of patients with coronary heart diseases using Hilbert-Huang transformation and time- domain method,” Chin. J. Integr. Med., vol. 21, no. 5, pp. 355– 360, 2015, doi: 10.1007/s11655-015-2173-7.
• B. Lenka, “Electrocardiogram Signals Using Hilbert- Huang Transform,” pp. 1156–1159, 2015.
• F. J. Neumann et al., “2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes,” Eur. Heart J., vol. 41, no. 3, pp. 407–477, 2020, doi: 10.1093/eurheartj/ehz425.
• A. Karagiannis and P. Constantinou, “Noise-assisted data processing with empirical mode decomposition in biomedical signals,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 1, pp. 11– 18, 2011, doi: 10.1109/TITB.2010.2091648.
• D. Sadhukhan, S. Pal, and M. Mitra, “Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG Data,” IEEE Trans. Instrum. Meas., vol. 67, no. 10, pp. 2303–2313, 2018, doi: 10.1109/TIM.2018.2816458.
• E. S. Jayachandran, P. Joseph K., and R. Acharya U., “Analysis of myocardial infarction using discrete wavelet transform,” J. Med. Syst., vol. 34, no. 6, pp. 985–992, 2010, doi: 10.1007/s10916-009- 9314-5.
• A. K. Dohare, V. Kumar, and R. Kumar, “Detection of myocardial infarction in 12 lead ECG using support vector machine,” Appl. Soft Comput. J., vol. 64, pp. 138–147, 2018, doi: 10.1016/j.asoc.2017.12.001.
• M. Arif, I. A. Malagore, and F. A. Afsar, “Detection and localization of myocardial infarction using K-nearest neighbor classifier,” J. Med. Syst., vol. 36, no. 1, pp. 279–289, 2012, doi: 10.1007/s10916-010-9474-3.
• L. Sun, Y. Lu, K. Yang, and S. Li, “ECG analysis using multiple instance learning for myocardial infarction detection,” IEEE Trans. Biomed. Eng., vol. 59, no. 12, pp. 3348–3356, 2012, doi: 10.1109/TBME.2012.2213597.
• P. C. Chang, J. J. Lin, J. C. Hsieh, and J. Weng, “Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models,” Appl. Soft Comput. J., vol. 12, no. 10, pp. 3165–3175, 2012, doi: 10.1016/j.asoc.2012.06.004.
• S. Critcher and T. J. Freeborn, “Multi-Site Impedance Measurement System based on MAX30001 Integrated-Circuit,” pp. 381–386, 2020.
• U. G. Gangkofner, P. S. Pradhan, and D. W. Holcomb, “Optimizing the High-Pass Filter Addition Technique for Image Fusion,” vol. 74, no. 9, pp. 1107–1118, 2008.
• J. Karki, “Active Low-Pass Filter Design,” no. February, pp. 1–23, 2023.
• https://www.mathworks.com/help/wavelet/ref/imodwt.html
• https://www.mathworks.com/help/wavelet/ref/modwt.html