QCSNN: A Memory-Efficient Spiking Neural Network for On-Device ECG-Based Arrhythmia Detection

This study introduces a novel Quantized Convolutional Spiking Neural Network (QCSNN) architecture for real-time arrhythmia detection from electrocardiogram (ECG) signals, designed for deployment on memory-constrained wearable healthcare devices. Unlike conventional models that rely on cloud-based processing or require substantial computational resources, our approach enables on-device inference through a two-stage, fully quantized spiking neural network trained with surrogate gradient descent. The proposed QCSNN classifies ECG signals into four clinically relevant categories—Normal, Supraventricular Ectopic Beat (SVEB), Ventricular Ectopic Beat (VEB), and Fusion (F)—achieving sensitivities of 88.97%, 83.51%, 96.4%, and 82.06%, respectively, and outperforming existing benchmarks while reducing memory usage by 76.2%. Trained with quantization-aware learning and leaky integrate-and-fire (LIF) spiking neurons, this architecture enables accurate, energy-efficient arrhythmia detection within the power and memory limits of smartwatches and portable monitors. This work advances scalable edge artificial intelligence (AI) for digital health, supporting continuous, real-time cardiac monitoring and early intervention.