Chen Tian, Zhang Jingtao, Xu Zeju, Redmond Stephen J, Lovell Nigel H, Liu Guanzheng, Wang Changhong
IEEE Trans Biomed Eng. 2024 Aug;71(8):2483-2494. doi: 10.1109/TBME.2024.3377270. Epub 2024 Jul 18.
Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection.
In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices.
Our method achieved 70.17 % accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor.
The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency.
This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.
睡眠呼吸暂停综合征（SAS）是一种常见的睡眠障碍，已被证明是导致严重神经认知和心血管后遗症的重要因素。鉴于当前的诊断策略受限于笨重的医疗设备和高昂的检查费用，大量病例未被诊断出来。为了实现对SAS的大规模筛查，可穿戴式光电容积脉搏波描记法（PPG）技术已被用作早期检测工具。然而，现有算法能耗大且需要大量内存资源，这被认为是进一步推广用于SAS检测的可穿戴设备的主要障碍。
本文提出了一种基于超维计算（HDC）的节能型SAS检测方法。受认知心理学中组块现象作为提高工作记忆效率的记忆机制的启发，我们提出了一种结合HDC的一维块局部二值模式（1D-BlockLBP）编码方案，以保留可穿戴PPG设备脉搏率信号的主要动态和时间特征。
我们的方法在睡眠呼吸暂停片段检测中达到了70.17%的准确率，与传统机器学习方法相当。此外，我们的方法在ARM Cortex-M4处理器上实现了高达67倍的内存占用降低、68倍的延迟减少和93倍的节能。
HDC中超向量运算的简单性和新颖的1D-BlockLBP编码有效地保留了脉搏率信号特征，计算效率高。
这项工作为基于家庭的长期睡眠呼吸暂停监测提供了一种可扩展的解决方案，提高了持续患者护理的可行性。
