多融合网络：基于深度神经网络的房颤检测
MultiFusionNet: Atrial Fibrillation Detection With Deep Neural Networks.
作者信息
Tran Luan, Li Yanfang, Nocera Luciano, Shahabi Cyrus, Xiong Li
机构信息
University of Southern California, Los Angeles, CA, USA.
Emory University, Atlanta, GA, USA.
出版信息
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:654-663. eCollection 2020.
Atrial fibrillation (AF) is the most common cardiac arrhythmia as well as a significant risk factor in heart failure and coronary artery disease. AF can be detected by using a short ECG recording. However, discriminating atrial fibrillation from normal sinus rhythm, other arrhythmia and strong noise, given a short ECG recording, is challenging. Towards this end, we propose MultiFusionNet, a deep learning network that uses a multiplicative fusion method to combine two deep neural networks trained on different sources of knowledge, i.e., extracted features and raw data. Thus, MultiFusionNet can exploit the relevant extracted features to improve upon the utilization of the deep learning model on the raw data. Our experiments show that this approach offers the most accurate AF classification and outperforms recently published algorithms that either use extracted features or raw data separately. Finally, we show that our multiplicative fusion method for combining the two sub-networks outperforms several other combining methods.
心房颤动（AF）是最常见的心律失常，也是心力衰竭和冠状动脉疾病的重要危险因素。使用短程心电图记录可以检测到房颤。然而，给定一段短程心电图记录，要将房颤与正常窦性心律、其他心律失常以及强噪声区分开来具有挑战性。为此，我们提出了MultiFusionNet，这是一种深度学习网络，它使用乘法融合方法来组合两个基于不同知识源（即提取的特征和原始数据）训练的深度神经网络。因此，MultiFusionNet可以利用相关的提取特征来提高深度学习模型对原始数据的利用率。我们的实验表明，这种方法提供了最准确的房颤分类，并且优于最近发表的分别使用提取特征或原始数据的算法。最后，我们表明用于组合两个子网络的乘法融合方法优于其他几种组合方法。
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