_:n6921723ffdd64b259fcfb202929b506axb81
<http://schema.org/description>
"Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease\nthat seriously jeopardizes the health of human beings. Polysomnography (PSG),\nthe gold standard of detecting OSA, requires multiple specialized sensors for\nsignal collection, hence patients have to physically visit hospitals and bear\nthe costly treatment for a single detection. Recently, many single-sensor\nalternatives have been proposed to improve the cost efficiency and convenience.\nAmong these methods, solutions based on RR-interval (i.e., the interval between\ntwo consecutive pulses) signals reach a satisfactory balance among comfort,\nportability and detection accuracy. In this paper, we advance RR-interval based\nOSA detection by considering its real-world practicality from energy\nperspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped\non smart wrist-worn wearable devices (e.g., smart watches and wristbands), the\nenergy efficiency of the detection model is crucial to fully support an\novernight observation on patients. This creates challenges as the PPG sensors\nare unable to keep collecting continuous signals due to the limited battery\ncapacity on smart wrist-worn devices. Therefore, we propose a novel Frequency\nExtraction Network (FENet), which can extract features from different frequency\nbands of the input RR-interval signals and generate continuous detection\nresults with downsampled, discontinuous RR-interval signals. With the help of\nthe one-to-multiple structure, FENet requires only one-third of the operation\ntime of the PPG sensor, thus sharply cutting down the energy consumption and\nenabling overnight diagnosis. Experimental results on real OSA datasets reveal\nthe state-of-the-art performance of FENet."
<https://deepai.org/profile/lizhen-cui>