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 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.
Department of Biomedical Engineering, Yonsei University, Wonju, Gangwondo, Korea.
Jong-Uk
JU
Park
Hyo-Ki
HK
Lee
Junghun
J
Lee
Erdenebayar
E
Urtnasan
Hojoong
H
Kim
Kyoung-Joung
KJ
Lee
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010092:Oximetry / Q000379:methods*; D017156:Photoplethysmography / Q000379:methods; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012890:Sleep / Q000502:physiology*; D012891:Sleep Apnea Syndromes / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D060388:Support Vector Machine; D014851:Wakefulness / Q000502:physiology