Sleep Apnea Severity Estimation From Tracheal Movements Using a Deep Learning Model

Sleep Apnea Severity Estimation From Tracheal Movements Using a Deep Learning Model

Objective: Sleep apnea is a chronic respiratory disorder and its standard assessment requires full night in-laboratory polysomnography (PSG). However, PSG is expensive, time-consuming, and inconvenient. Thus, there is a need to monitor sleep apnea with more convenient wearable devices. The objective...

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Bibliographic Details
Main Authors: Maziar Hafezi, Nasim Montazeri, Shumit Saha, Kaiyin Zhu, Bojan Gavrilovic, Azadeh Yadollahi, Babak Taati
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Accelerometer
apnea hypopnea index
deep learning
physiological features
wearable devices
sleep apnea monitoring
Online Access:https://ieeexplore.ieee.org/document/8968336/

Internet

https://ieeexplore.ieee.org/document/8968336/

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