Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 8; pp. 2483 - 2494
• Main Authors: Chen, Tian, Zhang, Jingtao, Xu, Zeju, Redmond, Stephen J., Lovell, Nigel H., Liu, Guanzheng, Wang, Changhong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Apnea; Biomedical monitoring; Coding; Cognition; Cognitive psychology; Complications; Computing time; Energy conservation; Energy efficiency; Feature extraction; Female; Heart rate; Humans; Hyperdimensional computing; Latency; local binary pattern; low power consumption; Machine learning; Male; Medical devices; Medical equipment; Memory; Memory devices; Microprocessors; Middle Aged; Performance evaluation; Photoplethysmography; Photoplethysmography - instrumentation; Photoplethysmography - methods; Signal Processing, Computer-Assisted; Sleep apnea; sleep apnea detection; Sleep Apnea Syndromes - diagnosis; Sleep Apnea Syndromes - physiopathology; Sleep disorders; Synthetic aperture sonar; Temporal lobe; Wearable devices; Wearable Electronic Devices; Wearable technology
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3377270

Objective: 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. Methods: 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. Results: Our method achieved 70.17<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> 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. Conclusion: The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. Significance: This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.