Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study

Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study

Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-mod...

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Bibliographic Details
Main Authors: Jae Won Choi, Dong Hyun Kim, Dae Lim Koo, Yangmi Park, Hyunwoo Nam, Ji Hyun Lee, Hyo Jin Kim, Seung-No Hong, Gwangsoo Jang, Sungmook Lim, Baekhyun Kim
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
obstructive sleep apnea
polysomnography
radar
deep learning
convolutional recurrent neural network
Online Access:https://www.mdpi.com/1424-8220/22/19/7177

Internet

https://www.mdpi.com/1424-8220/22/19/7177

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