Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of battery-dependent physiological monitoring wearables. This paper presents a novel deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:293 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the Hospital and deploy the solution on commonly used embedded AI chips (i.e., ARM Cortex V8 and Jetson Nano). The proposed framework achieves a 91% seizure detection accuracy using XGBoost, confirming the approach's reliability, practicality, and scalability.

翻译：头部采集的信号（如脑电图、肌电图、眼电图和心电图）在临床诊断、监测及重要脑部疾病治疗中扮演着关键角色。然而，长时间实时传输大量生理信号会消耗大量功耗和时间，限制了依赖电池的生理监测可穿戴设备的实用性。本文提出了一种基于变分自编码器的深度学习框架，用于生理信号压缩，以降低可穿戴设备的计算复杂度和能耗。我们的方法在频谱图数据上实现了1:293的显著压缩比，超越了JPEG2000、H.264、离散余弦变换和霍夫曼编码等先进压缩技术，这些技术在处理生理信号方面表现不佳。我们利用医院真实患者采集的生理信号验证了压缩算法的有效性，并在常用嵌入式AI芯片（如ARM Cortex V8和Jetson Nano）上部署了该方案。所提框架通过XGBoost实现了91%的癫痫发作检测准确率，证实了该方法在可靠性、实用性和可扩展性方面的优势。