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This paper presents a novel ECG classification algorithm for real-timecardiac monitoring on ultra low-power wearable devices. The proposed solutionis based on spiking neural networks which are the third generation of neuralnetworks. In specific, we employ spike-timing dependent plasticity (STDP), andreward-modulated STDP (R-STDP), in which the model weights are trainedaccording to the timings of spike signals, and reward or punishment signals.Experiments show that the proposed solution is suitable for real-timeoperation, achieves comparable accuracy with respect to previous methods, andmore importantly, its energy consumption is significantly smaller than previousneural network based solutions.

Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices