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

arXiv: https://arxiv.org/abs/1905.02954 · HTML: https://mlsanity.com/paper/1905.02954/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 7 of 7)

- [An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things](https://mlsanity.com/paper/2106.06498/) (2021)
- [A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning](https://mlsanity.com/paper/2111.00791/) (2021)
- [BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables](https://mlsanity.com/paper/2109.02909/) (2021)
- [A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility](https://mlsanity.com/paper/2503.07276/) (2025)
- [Review of medical data analysis based on spiking neural networks](https://mlsanity.com/paper/2212.02234/) (2022)
- [Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity](https://mlsanity.com/paper/2303.11429/) (2023)
- [Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems](https://mlsanity.com/paper/2312.04840/) (2023)

## Abstract (arXiv)

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.