Key result
A new heartbeat classifier algorithm for portable automatic detection systems demonstrated a sensitivity of 98.7% and specificity of 97.23% for distinguishing ischemic from non-ischemic heartbeats.
Why the study?
There was a need for a new heartbeat classifier for automatic cardiac ischemia detection to aid medical control of patients with cardiac disorders.
A novel heartbeat classifier algorithm demonstrated high sensitivity (98.7%) and specificity (97.23%) for detecting cardiac ischemia with low power consumption, making it suitable for portable automatic detection systems.
This article presents a new heartbeat classifier oriented to an automatic cardiac ischemia detection system aimed at the medical control of the population with cardiac disorders (ahigher risk group of severe COVID-19). The algorithm focuses ondistinguishing ischemic heartbeats from non-ischemic based onmethods and techniques proposed by medical experts in cardiac ischemia diagnoses. The proposed algorithm was tested with 80 electrocardiogram recordings obtained from the Long-term STdatabase of Physionet with a 100,112 heartbeat population. The heartbeat population contains different types of non-ischemic cases. The algorithm exhibits a sensitivity of 98.7 percent and specificity of 97.23 percent. The digital synthesis, in a 180nm CMOS process with a dynamic power consumption of 13.15 microWatt demanding an area of 490x400 micrometers, attests to the algorithm benefits.
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Fuente-Cortes et al. (2021) studied Cardiac ischemia (n=80). Heartbeat classifier algorithm was evaluated on Distinguishing ischemic heartbeats from non-ischemic heartbeats. A new heartbeat classifier algorithm for portable automatic detection systems demonstrated a sensitivity of 98.7% and specificity of 97.23% for distinguishing ischemic from non-ischemic heartbeats.
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