2021/01/08 by Guanhua Ye, Hongzhi Yin, Tong Chen +3 · 40 citations
Computer Science · Engineering · Mathematics · Medicine · #Apnea #Artificial intelligence #Cardiology #Computer science #Computer vision #Continuous monitoring #Embedded system #Engineering #Feature extraction #Filter (signal processing) #Indoor and Outdoor Localization Technologies #Internal medicine #Interval (graph theory) #Mathematics #Medicine #Non-Invasive Vital Sign Monitoring #Obstructive Sleep Apnea Research #Obstructive sleep apnea #Photoplethysmogram #Polysomnography #Real-time computing #Sleep apnea #Software portability #Wearable computer #Wearable technology #cs.HC #eess.SP
paper · pdf · doi:10.1109/jbhi.2021.3050113
published in IEEE Journal of Biomedical and Health Informatics 25(8), 2848-2856 (Institute of Electrical and Electronics Engineers) · To appear in JBHI
arxiv created 2021/01/08 · arxiv updated 2021/01/11 · openalex publication_date 2021/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy. In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RR-interval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-of-the-art performance of FENet.