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(ECG)信号监测心律失常对人类健康有着重要意义。随着深度学习的发展,出现了许多基于深度学习的ECG分类算法。然而,大多数现有算法在高精度的前提下使用复杂模型,从而导致存储使用量和耗电量高。这也不可避免地增加了在具有有限资源的可穿戴物联网(AIoT)设备上实现的难度。本研究提出了一种普适的超轻量级二值神经网络模型(BNN),它能够基于ECG信号实现5类和17类心律失常分类。我们的BNN在5类和17类分类方面分别实现了96.90%(全精度97.09%)和97.50%(全精度98.00%)的准确性,存储使用量为目前的最优水平(3.76KB和4.45KB)。与其他二值化方法相比,我们的方法在同时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分类方面实现了最佳准确率,并拥有优雅简约的网络架构。我们使用的算法专门针对硬件实现进行了优化。我们的研究展示了轻量级深度学习模型在医疗行业的潜力,特别是在可穿戴医疗设备上,这些设备有望改善患者的治疗效果和生活质量。代码可在以下网址上获得:https://github.com/xpww/ECG_BNN_Net