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：通过心电图信号合理且有效地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，基于深度学习的各种心电图分类算法层出不穷。然而，现有算法大多以高精度换取复杂模型，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设备上实现的难度。本研究提出了一种通用的超轻量二值神经网络，能够基于心电图信号进行5类和17类心律失常分类。我们的二值神经网络在5类和17类分类中分别实现了96.90%（全精度为97.09%）和97.50%（全精度为98.00%）的准确率，并达到了最优的存储占用（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类分类中达到了最优准确率，并拥有简洁优雅的网络架构。我们所使用的算法针对硬件实现进行了专门优化。这项研究展示了轻量级深度学习模型在医疗健康行业（特别是可穿戴医疗设备）中的潜力，有望显著改善患者的治疗效果与生活质量。代码已开源：https://github.com/xpww/ECG_BNN_Net