An SNN-Inspired Area- and Power-Efficient VLSI Architecture of Myocardial Infarction Classifier for Wearable Devices

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 brief, 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
$mm^{2}$
at SCL
$180~nm$
Bulk CMOS technology node, and the power consumption is
$268.9 ~\mu W$
at 250 KHz. The low area and power requirements and real-time classification capability make the classifier suitable for wearable devices.