All-Spiking ECG Analysis for Arrhythmia Classification on Low-Power FPGA

Accurate and energy-efficient classification of cardiac arrhythmias is essential for real-time electrocardiogram (ECG) monitoring in wearable healthcare systems. This work introduces an end-to-end, spike-driven approach for ECG analysis, in which Spiking Neural Network (SNN) address arrhythmia detection on a event-based input. The signal encoding employs delta modulation on the raw ECG waveform as well as its first and second derivatives, capturing richer temporal and morphological features and enhancing classification performance compared to baseline approaches. Heartbeats are classified into five categories, as defined by the AAMI standard, achieving 98.4% accuracy on the MIT-BIH Arrhythmia Database. Unlike traditional methods, our approach removes the need for separate filtering, segmentation, or peak detection algorithms, relying instead on a unified, event-driven architecture. To support this enhanced processing methodology, we have implemented an optimized hardware architecture based on a low-power Lattice iCE40-UltraPlus FPGA. This design eliminates redundant computations by unifying peak detection and classification within the same processing pipeline, reducing power consumption while maintaining low inference times. Performance evaluations indicate an execution time of just 4.05 ms per classification, with energy usage optimized to 36.86 μJ, substantially outperforming existing FPGA-based solutions.