Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1655-1658. doi: 10.1109/EMBC48229.2022.9871259.
Atrial Fibrillation (AF) is a kind of arrhythmia, which is a major morbidity factor, and AF can lead to stroke, heart failure and other cardiovascular complications. Electrocardiogram (ECG) is the basic marker to test the condition of heart and it can effectively detect AF condition. Single lead ECG has the practical advantage for being small form factor and it is easy to deploy. With the sophistication of the current deep learning (DL) models, researchers have been able to construct cardiologist-level models to detect different arrhythmias including AF condition detection from single lead short-time ECG signals. However, such models are computationally expensive and require huge memory size for deployment (more than 100 MB to deploy state-of-the-art 34-layer convolutional neural network-based ECG classification model). Such models need to be significantly trimmed with insignificantly loss of its classification performance for deployment in practical applications like single lead ECG classification in wearable and implantable devices. We have found that classical deep learning model compression techniques like pruning, quantization are not capable of substantial model size reduction without compromising on the model performance. In this paper, we propose LTH-ECG, which is our novel goal-driven winning lottery ticket discovery method, where lottery ticket hypothesis (LTH)-based iterative model pruning is used with the aim of over-pruning avoidance. LTH-ECG reduces the model size by 142x times with insignificant loss of classification performance (less than 1 % test F1-score penalty). Clinical Relevance- LTH-ECG will enable practical deployment for remote screening of AF condition using single lead short-time ECG recordings such that patients can on-demand monitor AF condition remotely through wearable ECG sensing devices and report cardiological abnormality to the concerned physician. LTH-ECG acts as an early warning system for effective AF condition screening.
心房颤动（AF）是一种心律失常，是主要的发病因素之一，AF 可导致中风、心力衰竭等心血管并发症。心电图（ECG）是检测心脏状况的基本标志物，可有效检测 AF 状况。单导联 ECG 具有体积小、易于部署的实际优势。随着当前深度学习（DL）模型的日益成熟，研究人员已经能够构建出能够检测不同心律失常（包括 AF 检测）的心脏病专家级模型，这些模型是从单导联短时间 ECG 信号中构建的。然而，此类模型计算成本高，需要大量的内存空间才能部署（部署最先进的基于 34 层卷积神经网络的 ECG 分类模型需要超过 100MB）。此类模型需要进行显著的修剪，在不影响其分类性能的情况下显著减小模型大小，以便在实际应用中部署，如可穿戴式和植入式设备中的单导联 ECG 分类。我们发现，经典的深度学习模型压缩技术，如剪枝、量化，在不影响模型性能的情况下，无法显著减小模型大小。在本文中，我们提出了 LTH-ECG，这是我们的新颖的目标驱动的彩票发现方法，基于彩票假说（LTH）的迭代模型剪枝用于避免过度剪枝。LTH-ECG 将模型大小减小了 142 倍，而分类性能的损失微不足道（测试 F1 分数的惩罚不到 1%）。临床相关性-LTH-ECG 将能够使用单导联短时间 ECG 记录进行远程 AF 筛查的实际部署，以便患者可以通过可穿戴 ECG 感测设备按需远程监测 AF 状况，并将心脏异常报告给相关医生。LTH-ECG 是一种有效的 AF 筛查的早期预警系统。
