Evaluation Of Patients With Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) Based on Nonlinear Analysis Of Respiratory Signals
Condition: Obstructive Sleep Apnea Syndrome (OSAS)
MeSH terms: Apnea; Sleep Apnea Syndromes; Sleep Apnea, Obstructive; Syndrome
Study type: Observational

Summary: 
      Objective: Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep disorder
      requiring the time and money consuming full polysomnography to be diagnosed. Alternative
      methods for initial evaluation are sought. The investigators aim was the prediction of
      Apnea-Hypopnea Index (AHI) in patients suspected to suffer from OSAHS using two models based
      on nonlinear analysis of three biosignals during sleep.

      Methods: One hundred patients referred to a Sleep Unit underwent full polysomnography. Three
      nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate
      Entropy) were extracted from three biosignals (airflow from a nasal cannula, thoracic
      movement and Oxygen saturation) providing input to a data mining application for the creation
      of predictive models for AHI.
    

Detailed description: 
      Patients referred to the Sleep Unit of a tertiary hospital in northern Greece during the
      years 2005-2008 and who accepted to sign the informed consent form were included in the
      study. One out of every five consecutive patients was selected in order to ensure
      randomization. The study protocol was approved by the ethics committee of the hospital. All
      the subjects reported symptoms consistent with OSAHS and had no significant comorbidities.
      The presence of dementia, neuromuscular disorders, overlap syndrome or severe cardiac
      problems was an exclusion criterion for the participants. The subjects underwent full
      overnight attended polysomnography (Somnologica 7000, Flaga; Iceland) according to standard
      criteria including respiratory recordings of thoracic and abdominal movements, nasal flow by
      pressure cannula, snoring, and arterial oxygen saturation using pulse oximetry. Apnea and
      hypopnea were defined in accordance with standard used criteria. All the recordings were
      manually scored by the same experienced medical doctor.

      Three nonlinear indices (Largest Lyapunov Exponent-LLE, Detrended Fluctuation Analysis-DFA
      and Approximate Entropy-APEN) were extracted from two respiratory signals (nasal cannula
      flow-F and thoracic belt movement-T). The oxygen saturation signal (SpO2) from pulse oximetry
      was also selected. The above signals had a mean duration of 317.5 minutes and were first
      exported in European Data Format (EDF) to be further processed with the use of signal
      processing software (Matlab by Mathworks Inc.) in personal computers. The LLE calculation
      required the use of a command line application by Rosenstein et al as well as a spreadsheet
      program (Microsoft Excel).

      The basic statistical analysis was performed with the use of SPSS for Windows, Version 15.0
      (SPSS Inc, Chicago, Illinois). Correlations between the studied or derived parameters were
      explored with the Pearson's correlation test and differences in the mean observed values
      between the various OSAHS severity groups were analyzed using the Student's t-test. The
      statistical significance level was set at p<0.05. The predictive model was created by
      utilizing the linear regression tool.
    

Intervention name(s): Estimation of nonlinear indices from Polysomnography
Intervention type(s): Device
Intervention description(s): All subjects underwent full night polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) were extracted from three biosignals (airflow from a nasal cannula, thoracic movement and Oxygen saturation) providing input to a data mining application for the creation of predictive models for AHI.
Other intervention name(s): polysomnography device: Somnologica 7000, Flaga; Iceland
Observational model: Case-Only
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Patients referred to the Sleep Unit of a tertiary hospital in northern Greece during the
        years 2005-2008 and who accepted to sign the informed consent form were included in the
        study. One out of every five consecutive patients was selected in order to ensure
        randomization.
      
Keywords: OSAHS; nonlinear analysis; polysomnography; biosignal analysis; DFA; APEN; LLE