Atrial Fibrillation Detector from ECG on Wearable Edge Devices using Spiking Neural Networks

Real-time monitoring of ECG signals in wearable and implanted edge devices, such as smart watches, ILRs, pace-makers etc. is essential for early clinical intervention in Cardio-Vascular illnesses. Existing deep learning-based algorithms are not suitable for such battery-powered, low-power, low-memory devices. In this paper, a new peak-based Spiking Hamilton (SH) encoder is proposed and its performance for classifying Atrial Fibrillation (AF) patterns in ECG signal is tested and compared with three existing encoders by deploying reservoir-based SNN and a feed-forward SNN and on five well known ECG dataset. Feed-forward SNN, coupled with proposed SH encoder, achieves best performance while requiring the least amount of computational effort and power. This system running on Neuromorphic Computing (NC) platforms could therefore be a good option for classifying ECG patterns at the wearable edge.