Evaluation Of Patients With Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) 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.

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Key information

Conditions

Obstructive Sleep Apnea Syndrome (OSAS) Apnea Dyssomnias Nervous System Diseases Respiration Disorders Respiratory Tract Diseases Sleep Apnea Syndromes Sleep Apnea, Obstructive Sleep Disorders, Intrinsic Sleep Wake Disorders

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Sleep Unit of "G. Papanikolaou" General Hospital

Exochi, GR57010, Greece

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

symptoms compatible with OSAHS
voluntary participation

Exclusion criteria

presence of dementia
neuromuscular disorders
overlap syndrome
severe cardiac problems

Treatment and study plan

Estimation of nonlinear indices from Polysomnography

Device

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 names: polysomnography device: Somnologica 7000, Flaga; Iceland

Primary outcomes

nonlinear dynamics of respiratory signals

Time frame: One night

calculation of nonlinear parameters (DFA, LLE, APEN) from recorded respiratory biosignals (nasal airflow, thoracic movement and SpO2) during sleep.

Sponsors and collaborators

Lead sponsor

Aristotle University Of Thessaloniki

Other

Collaborators

Greek State Scholarship Foundation

Registry information

Official study title

Evaluation Of Patients With Suspected Obstructive Sleep Apnea - Hypopnea Syndrome Using Two Models Based on Nonlinear Analysis Of Respiratory Signals

Important dates

Study start: 2005
Primary completion: 2009
Study completion: 2009
First posted: Jul 13, 2010
Registry last updated: Jul 13, 2010

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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