Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector. Many automated healthcare services rely on two crucial biological signals, namely ECG and EEG, which reflect the activity of the heart and brain, respectively. Although deep neural networks are considered the primary way to process and analyze these signals, the very tight energy and computational power constraints in wearable devices are far below the computational, energy, and memory bandwidth demands of DNN models, thereby impeding the deployment of deep learning in many practical wearable services. This paper investigates the feasibility of deploying state-of-the-art DNN models in resource-constrained wearable devices. Notably, we explore the trade-off between accuracy and computational complexity of DNNs when parameter quantization and electrode reduction methods are used. Our investigation centers on several state-of-the-art DNN models designed for EEG signal analysis, specifically for detecting epileptic seizures. Our findings demonstrate that, when applied judiciously, these techniques can significantly reduce the complexity of the DNNs under consideration with minimal adverse effects on accuracy. These results reveal the explicit trade-offs between accuracy and complexity reduction encountered when adapting DNN-based online EEG analysis for wearable devices.

翻译：可穿戴医疗设备是物联网领域增长最快的分支。众多自动化医疗服务依赖于两种关键的生物信号——心电图与脑电图，分别反映心脏与大脑的活动状态。尽管深度神经网络被视为处理分析这些信号的主流方法，但可穿戴设备中极其严苛的能量与计算能力约束，远低于深度神经网络模型在计算、能耗与内存带宽方面的需求，从而阻碍了深度学习在诸多实际可穿戴服务中的部署。本文研究了在资源受限的可穿戴设备上部署先进深度神经网络模型的可行性。我们重点探讨了在采用参数量化与电极缩减方法时，深度神经网络的准确率与计算复杂度之间的权衡关系。研究聚焦于多种专为脑电图信号分析设计的先进深度神经网络模型，特别是用于癫痫发作检测的场景。研究结果表明，在合理应用的前提下，这些技术能够显著降低所考察深度神经网络的复杂度，同时将准确率损失控制在极小范围内。这些结果揭示了将基于深度神经网络的在线脑电图分析适配至可穿戴设备时，在准确率与复杂度缩减之间存在的显性权衡关系。