Key result
Proposed VLSI architecture achieves ~99.9% average accuracy for real-time MI classification.
Why the study?
Along with the detection of myocardial infarction, determining the location of the coronary artery blockage in real-time is imperative to provide timely treatment and avoid fatalities.
A novel, power-efficient VLSI architecture enables highly accurate, real-time detection and localization of myocardial infarction for wearable devices.
Answers are grounded in this paper and cite their sources.
May enable low-power 12-class ECG classification in wearables; hypothesis-generating pending clinical validation.
Myocardial infarction (MI) is a cardiac abnormality in which the coronary artery gets blocked, causing millions of fatalities every year. MI has a very high mortality and disability rate; therefore, with the detection of MI, it is also imperative to determine the location of the blockage to provide on–time treatment to avoid any fatality. In this paper, for the first time, a VLSI architecture is proposed that can determine the location of the infarction in real-time. The proposed architecture classifies the electrocardiogram (ECG) into twelve classes and achieves an average accuracy, sensitivity, and specificity of 99.90%, 99.49%, and 99.94%, respectively. Its area utilization is 1.69 mm2 at SCL 180 nm Bulk CMOS technology node, and the power consumption is 268.9 μW at 250 KHz. The low area and power requirements and real-time classification capability make the classifier suitable for wearable devices.
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Parmar et al. (2024) studied Myocardial infarction. VLSI architecture for real-time MI classification was evaluated on Classification of ECG into twelve classes (accuracy, sensitivity, specificity). A proposed VLSI architecture for real-time myocardial infarction classification achieved an average accuracy of 99.90%, sensitivity of 99.49%, and specificity of 99.94%.
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