An SNN Based ECG Classifier for Wearable Edge Devices

Abstract

In situ real time monitoring of ECG signal at wearables and implantables such as smart watch, ILR, Pacemaker etc. are crucial for early clinical intervention of Cardio-Vascular diseases. Existing deep learning based techniques are not suitable to run on such low-power, low-memory, battery driven devices. In this paper, we have designed and implemented a reservoir based SNN and a Feed-forward SNN, and compared their performances for ECG pattern classification along with a new Peak-based spike encoder and two other spike encoders. Feed-forward SNN coupled with peak-based encoder is observed to deliver the best performance spending least computational effort and thus minimal power consumption. Therefore, this SNN based system running on Neuromorphic Computing (NC) platforms can be a suitable solution for ECG pattern classification at the wearable edge.