Neuromorphic Computing for Long-Term Cardiac Health: A Review of Spiking Neural Networks in Low-Power Wearable Electronics

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Abstract
The integration of Artificial Intelligence (AI) into Internet of Things (IoT) medical devices has revolutionized arrhythmia monitoring. However, the high computational and power demands of traditional Deep Learning (DL) models pose significant challenges for long-term, battery-operated smart electronics. Spiking Neural Networks (SNNs), inspired by the biological efficiency of the human brain, offer a promising solution. This paper reviews the intersection of SNNs, low-power IoT hardware, and biomedical signal processing. I examine the transition from frame-based to event-driven processing, and discuss the hardware–software co-design necessary for next-generation cardiac wearables.

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