An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection

TL;DR

This paper introduces an energy-efficient, event-driven neuromorphic ECG system that uses sparse sampling and spike-based neural networks to accurately detect cardiac arrhythmias with significantly reduced data and power consumption.

Contribution

It presents a novel co-designed system combining event-driven compressive sampling with spike-based neural processing for low-power arrhythmia detection.

Findings

Achieves 88.6% data reduction compared to Nyquist sampling.

Attains 93.59% arrhythmia detection accuracy.

Demonstrates significant energy savings in ECG processing.

Abstract

Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an event-driven compressive ECG recording and neuromorphic processing system for cardiac arrhythmia detection. The proposed system achieves low power consumption and high arrhythmia detection accuracy via system level co-design with spike-based information representation. Event-driven level-crossing ADC (LC-ADC) is exploited in the recording system, which utilizes the sparsity of ECG signal to enable compressive recording and save ADC energy during the silent signal period. Meanwhile, the proposed spiking convolutional neural network (SCNN) based neuromorphic arrhythmia detection method is inherently compatible with the spike-based output of LC-ADC, hence realizing accurate detection and low energy consumption at system level. Simulation results show that the proposed system with 5-bit LC-ADC achieves 88.6\% reduction of sampled data points compared with Nyquist sampling in the MIT-BIH dataset, and 93.59\% arrhythmia detection accuracy with SCNN, demonstrating the compression ability of LC-ADC and the effectiveness of system level co-design with SCNN.