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

翻译：合理有效地通过心电图信号监测心律失常对人类健康具有重要意义。随着深度学习的发展，基于深度学习的多种心电图分类算法相继出现。然而，现有算法大多以复杂模型换取高精度，导致存储占用和功耗较高，这也不可避免地增加了在资源受限的可穿戴人工智能物联网（AIoT）设备上实现的难度。在本研究中，我们提出了一种普适性超轻量二值神经网络（BNN），能够基于心电图信号进行5分类和17分类心律失常分类。我们的BNN在5分类和17分类任务上分别达到了96.90%（全精度为97.09%）和97.50%（全精度为98.00%）的准确率，并实现了目前最优的存储占用（3.76 KB和4.45 KB）。与其他二值化工作相比，我们的方法在支持两种多分类模式的同时，实现了已知最小的存储空间。此外，我们的模型在17分类任务中达到了最优准确率，并拥有简洁优雅的网络架构。我们所使用的算法专门针对硬件实现进行了优化。本研究展示了轻量级深度学习模型在医疗健康领域，特别是可穿戴医疗设备中的潜力，有望改善患者预后和生活质量。代码已开源：https://github.com/xpww/ECG_BNN_Net