Binary is All You Need: Ultra-Efficient Arrhythmia Detection with a Binary-Only Compressive System

Detecting cardiac arrhythmia is critical for preventing heart attacks, and wearable electrocardiograph (ECG) systems have been developed to address this issue. However, the energy consumption of existing wearable systems is still significant at both the circuit and system levels, posing a challenge for their design. In this paper, we propose a novel ultra-efficient binary-only compressive ECG system for edge cardiac arrhythmia detection, featuring an event-driven level-crossing analog-to-spike converter (ATS) for sensing and a computing-in-memory (CIM) based binarized neural network (BNN) processor for compressive processing. Through system-level co-design, our proposed system achieves high arrhythmia detection accuracy and ultra-low energy consumption. Our simulations using the MIT-BIH dataset show that the proposed system achieves a 90.1% reduction in sampled data points compared to Nyquist sampling. Moreover, our dedicated BNN on a CIM engine delivers 97.03% arrhythmia detection accuracy with energy efficiency as low as 0.17uJ/inference.