Background and purposes Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propose the use of a one-dimensional convolutional network (1D-CNN) architecture to build a binary classifier that distinguishes electrocardiogram s (ECGs) between the post-stroke and the stroke-free.

中文翻译：

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使用梯度加权类激活映射的一维卷积神经网络模型分析中风后老年人的心电图

背景和目的中风是继缺血性心脏病之后的全球第二大死亡原因，也是心源性中风的危险因素。因此，我们假设心跳封装了与中风相关的重要信号。随着深度神经网络 (DNN) 的快速发展，它成为破译与中风后患者相关的有趣心跳模式的强大工具。在这项研究中，我们建议使用一维卷积网络 (1D-CNN) 架构来构建一个二元分类器，以区分中风后和无中风之间的心电图 (ECG)。