Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter

This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep stat...

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Bibliographic Details
Published in:Physiological measurement 2015-09, Vol.36 (9), p.2009-2025
Main Authors: Park, Jong-Uk, Lee, Hyo-Ki, Lee, Junghun, Urtnasan, Erdenebayar, Kim, Hojoong, Lee, Kyoung-Joung
Format: Article
Language:English
Subjects:
Algorithms
Diagnosis, Computer-Assisted - methods
Female
Humans
Male
Middle Aged
Oximetry - methods
oxygen saturation
photoplethysmogram
Photoplethysmography - methods
Regression Analysis
Sensitivity and Specificity
Severity of Illness Index
severity of SDB
Sleep - physiology
Sleep Apnea Syndromes - classification
Sleep Apnea Syndromes - diagnosis
Sleep Apnea Syndromes - physiopathology
sleep apnea/hypopnea
sleep/wake states
Support Vector Machine
Wakefulness - physiology
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Summary:This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter.
ISSN:0967-3334
1361-6579
DOI:10.1088/0967-3334/36/9/2009