# Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network

arXiv: https://arxiv.org/abs/2304.01568 · HTML: https://mlsanity.com/paper/2304.01568/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images](https://mlsanity.com/paper/1805.11123/) (2018)
- [An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things](https://mlsanity.com/paper/2106.06498/) (2021)
- [Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization](https://mlsanity.com/paper/2202.12943/) (2022)
- [Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices](https://mlsanity.com/paper/2205.03661/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification](https://mlsanity.com/paper/2406.16928/) (2024)
- [Eventizing Traditionally Opaque Binary Neural Networks as 1-safe Petri net Models](https://mlsanity.com/paper/2602.13128/) (2026)
- [ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification](https://mlsanity.com/paper/2607.09680/) (2026)

## Abstract (arXiv)

Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting in high storage usage and power consumption. This also inevitably increases the difficulty of implementation on wearable Artificial Intelligence-of-Things (AIoT) devices with limited resources. In this study, we proposed a universally applicable ultra-lightweight binary neural network(BNN) that is capable of 5-class and 17-class arrhythmia classification based on ECG signals. Our BNN achieves 96.90% (full precision 97.09%) and 97.50% (full precision 98.00%) accuracy for 5-class and 17-class classification, respectively, with state-of-the-art storage usage (3.76 KB and 4.45 KB). Compared to other binarization works, our approach excels in supporting two multi-classification modes while achieving the smallest known storage space. Moreover, our model achieves optimal accuracy in 17-class classification and boasts an elegantly simple network architecture. The algorithm we use is optimized specifically for hardware implementation. Our research showcases the potential of lightweight deep learning models in the healthcare industry, specifically in wearable medical devices, which hold great promise for improving patient outcomes and quality of life. Code is available on: https://github.com/xpww/ECG_BNN_Net