Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. The use of wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Although deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity. In this work, we propose to use lightweight convolutional neural networks (CNNs) with parameterised hypercomplex (PH) layers for AF detection based on ECGs. The proposed approach trains small-scale CNNs, thus overcoming the limited computing resources on wearable devices. We show comparable performance to corresponding real-valued CNNs on two publicly available ECG datasets using significantly fewer model parameters. PH models are more flexible than other hypercomplex neural networks and can operate on any number of input ECG leads.

翻译：房颤是最常见的心律失常，与中风等严重疾病的高风险相关。利用可穿戴设备通过心电图自动及时评估房颤，在预防危及生命的情况方面显示出前景。尽管深度神经网络在模型性能上表现出优越性，但其在可穿戴设备上的应用受到模型性能与复杂度之间权衡的限制。本研究提出使用轻量级卷积神经网络，结合参数化超复数层，基于心电图进行房颤检测。所提出的方法训练小规模卷积神经网络，从而克服可穿戴设备计算资源有限的限制。我们在两个公开心电图数据集上展示了与相应实值卷积神经网络相当的性能，同时使用了显著更少的模型参数。参数化超复数模型比其他超复数神经网络更灵活，可适用于任意数量的输入心电导联。