论文标题
通过参数化的超复杂神经网络有效的基于ECG的房颤检测
Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
论文作者
论文摘要
心房颤动（AF）是最常见的心律失常，与患病等严重疾病的高风险有关。使用心电图（ECG）嵌入具有自动和及时的AF评估的可穿戴设备已证明有望防止威胁生命的情况。尽管深度神经网络在模型性能方面表现出了优势，但它们在可穿戴设备上的使用受到模型性能和复杂性之间的权衡的限制。在这项工作中，我们建议将轻量级卷积神经网络（CNN）与参数化的超复合（pH）层一起基于ECGS进行AF检测。所提出的方法训练小型CNN，从而克服了可穿戴设备上有限的计算资源。我们使用明显较少的模型参数显示了与两个公开可用的ECG数据集上相应的实价CNN相当的性能。 pH模型比其他超复合神经网络更灵活，并且可以在任何数量的输入ECG铅上运行。
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.
