Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach

Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach

<italic>Goal:</italic> The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs an...

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Bibliographic Details
Main Authors: Nicolo La Porta, Stefano Scafa, Michela Papandrea, Filippo Molinari, Alessandro Puiatti
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Sleep-related breathing disorders
sleep apnea
obstructive sleep apnea
machine learning
polysomnography
Online Access:https://ieeexplore.ieee.org/document/10770579/

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https://ieeexplore.ieee.org/document/10770579/

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