title: Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.
abstract: BACKGROUND
  Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder.
  
  
  METHODS
  Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI.
  
  
  RESULTS
  A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA.
  
  
  CONCLUSIONS
  We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome.
  
  
  CONCLUSIONS
  Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology.
  
  
  BACKGROUND
  ClinicalTrials.gov NCT01161381.
authors.affiliation: Lab of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: Evangelos
authors.initials: E
authors.lastname: Kaimakamis
authors.affiliation: Sleep Unit, Pulmonary Department, General Hospital "G. Papanikolaou," Thessaloniki, Greece.
authors.forename: Venetia
authors.initials: V
authors.lastname: Tsara
authors.affiliation: Lab of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: Charalambos
authors.initials: C
authors.lastname: Bratsas
authors.affiliation: Pulmonary Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: Lazaros
authors.initials: L
authors.lastname: Sichletidis
authors.affiliation: 1st Cardiology Department, General Hospital "AHEPA," Thessaloniki, Greece.
authors.forename: Charalambos
authors.initials: C
authors.lastname: Karvounis
authors.affiliation: Lab of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Maglaveras
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D017286:Polysomnography; D012372:ROC Curve; D056152:Respiratory Rate; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology; D018709:Statistics, Nonparametric; D055815:Young Adult