This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption is significantly smaller than previous neural network based solutions.
@article{arxiv.1905.02954,
title = {Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices},
author = {Alireza Amirshahi and Matin Hashemi},
journal= {arXiv preprint arXiv:1905.02954},
year = {2019}
}
Comments
Published in IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2019