FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

Guanhua Ye, Hongzhi Yin, Tong Chen, Hongxu Chen, Lizhen Cui, Xiangliang Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish (US)
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
StatePublished - 2021

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