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（IoT）领域增长最快的分支。许多自动化医疗服务依赖于两种关键生物信号，即心电图（ECG）和脑电图（EEG），它们分别反映心脏和大脑的活动。尽管深度神经网络被认为是处理和分析这些信号的主要方式，但可穿戴设备中极为严格的能耗与计算能力限制远低于DNN模型对计算、能源及内存带宽的需求，这阻碍了深度学习在众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中部署先进DNN模型的可行性。我们重点探讨了采用参数量化和电极缩减方法时，DNN模型的精度与计算复杂度之间的权衡关系。研究聚焦于几种专为脑电信号分析（特别是癫痫发作检测）设计的先进DNN模型。结果表明，在合理应用的情况下，这些技术能够显著降低所研究DNN模型的复杂度，同时对精度的影响极小。这些发现揭示了在将基于DNN的在线脑电图分析适配至可穿戴设备时，精度与复杂度缩减之间存在的明确权衡关系。