Neuromorphic Heart Rate Monitors: Neural State Machines for Monotonic Change Detection

TL;DR

This paper introduces a neuromorphic neural state machine for low-power, real-time detection of monotonic heart rate changes, enhancing continuous health monitoring especially for vulnerable populations.

Contribution

It presents a novel neuromorphic implementation of a neural state machine to detect monotonic HR changes, improving energy efficiency and real-time processing capabilities.

Findings

Successful encoding of health states in neuromorphic hardware

Effective detection of monotonic HR increases in ECG data

Potential for long-lasting, always-on wearable health monitors

Abstract

Detecting monotonic changes in heart rate (HR) is crucial for early identification of cardiac conditions and health management. This is particularly important for dementia patients, where HR trends can signal stress or agitation. Developing wearable technologies that can perform always-on monitoring of HRs is essential to effectively detect slow changes over extended periods of time. However, designing compact electronic circuits that can monitor and process bio-signals continuously, and that can operate in a low-power regime to ensure long-lasting performance, is still an open challenge. Neuromorphic technology offers an energy-efficient solution for real-time health monitoring. We propose a neuromorphic implementation of a Neural State Machine (NSM) network to encode different health states and switch between them based on the input stimuli. Our focus is on detecting monotonic state switches in electrocardiogram data to identify progressive HR increases. This innovative approach promises significant advancements in continuous health monitoring and management.