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

Abstract

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.

arXiv commentPublished in IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2019

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Cited by in corpus (6)

• Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor
• RANC: Reconfigurable Architecture for Neuromorphic Computing
• BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables
• Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
• Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies
• A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility