Lightweight Multi-task Hyperdimensional Computing Framework Driven by Binary Neural Network for Sleep Apnea Detection

• Published in: Biomedical Circuits and Systems Conference pp. 1 - 5
• Main Authors: Chen, Tian, Liu, Yi, Liu, Guanzheng, Wang, Changhong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.10.2024
• Subjects: Binary Neural Network; Computational modeling; Detection algorithms; Hyperdimensional Computing; Integrated circuit modeling; Metric Learning; Monitoring; Multi-task Learning; Multitasking; Neural networks; Sleep apnea; Sleep Apnea Syndrome; Synthetic aperture sonar; Training; Wearable devices
• ISSN: 2766-4465
• DOI: 10.1109/BioCAS61083.2024.10798240

Sleep apnea syndrome (SAS) is a prevalent sleep disorder that impacts neurocognitive and cardiovascular health. The need for effective long-term management of SAS in everyday conditions has led to the adoption of wearable devices based on photoplethysmography (PPG) technology as a viable monitoring solution. However, the computational demands of current detection algorithms often surpass the capabilities of low-power, miniaturized wearable devices. To overcome this challenge, this study introduces a novel, lightweight detection algorithm for SAS that utilizes hyperdimensional computing. This algorithm is a multi-task hyperdimensional computing framework that is enhanced by binary neural network and incorporates metric learning. The proposed model demonstrates inference performance with an accuracy of \mathbf{7 5. 7 2 \%}, sensitivity of 73.74%, and specificity of \mathbf{7 6. 4 3 \%}. Compared to conventional machine learning approaches, this model also offers better performance on embedded platforms, achieving up to 25 \times reduction in memory usage, 39 \times decrease in latency, and 64× lower energy consumption on an ARM CortexM4 processor.