Obstructive Sleep Apnea Syndrome (OSAS) Clinical Trial
Official title:
Evaluation Of Patients With Suspected Obstructive Sleep Apnea - Hypopnea Syndrome Using Two Models Based on Nonlinear Analysis Of Respiratory Signals
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.
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.
;
Observational Model: Case-Only, Time Perspective: Prospective
| Status | Clinical Trial | Phase | |
|---|---|---|---|
| Terminated |
NCT01195064 -
Endothelial Function Study Before Cardiovascular Surgery
|