RAPIDEST : A Framework for Obstructive Sleep Apnea Detection

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

  • Xin-Xue Lin
  • Phone Lin
  • En-Hau Yeh
  • Gi-Ren Liu
  • Wan-Ching Lien

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)387-397
Journal / PublicationIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume31
Online published24 Nov 2022
Publication statusPublished - 2023

Link(s)

Abstract

The traditional polysomnography (PSG) examination for Obstructive Sleep Apnea (OSA) diagnosis needs to measure several signals, such as EEG, ECG, EMG, EOG and the oxygen level in blood, of a patient who may have to wear many sensors during sleep. After the PSG examination, the Apnea-Hypopnea Index (AHI) is calculated based on the measured data to evaluate the severity of apnea and hypopnea for the patient. This process is obviously complicated and inconvenient. In this paper, we propose an AI-based framework, called RAre Pattern Identification and DEtection for Sleep-stage Transitions (RAPIDEST), to detect OSA based on the sequence of sleep stages from which a novel rarity score is defined to capture the unusualness of the sequence of sleep stages. More importantly, under this framework, we only need EEG signals, thus significantly simplifying the signal collection process and reducing the complexity of the severity determination of apnea and hypopnea. We have conducted extensive experiments to verify the relationship between the rarity score and AHI and demonstrate the effectiveness of our proposed approach.

Research Area(s)

  • Apnea-Hypopnea Index (AHI), EEG Signal, Machine Learning, Obstructive Sleep Apnea (OSA)

Citation Format(s)

RAPIDEST: A Framework for Obstructive Sleep Apnea Detection. / Lin, Xin-Xue; Lin, Phone; Yeh, En-Hau et al.
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 31, 2023, p. 387-397.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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