Evaluation Of Patients With Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) Based on Nonlinear Analysis Of Respiratory Signals

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01161381
• Other study ID #: EK1001
• Status: Completed
• Phase: N/A
• First received: July 12, 2010
• Last updated: July 12, 2010
• Start date: November 2005
• Est. completion date: December 2009

Study information

• Verified date: December 2005
• Source: Aristotle University Of Thessaloniki
• Contact: n/a
• Is FDA regulated: No
• Health authority: Greece: Ethics Committee
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 100
• Est. completion date: December 2009
• Est. primary completion date: December 2009
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Both
• Age group: 18 Years to 75 Years

Eligibility

Inclusion Criteria:

• symptoms compatible with OSAHS

• voluntary participation

Exclusion Criteria:

• presence of dementia

• neuromuscular disorders

• overlap syndrome

• severe cardiac problems

Study Design

Observational Model: Case-Only, Time Perspective: Prospective

Related Conditions & MeSH terms

• Apnea
• Obstructive Sleep Apnea Syndrome (OSAS)
• Sleep Apnea Syndromes
• Sleep Apnea, Obstructive
• Syndrome

Intervention

Device:
• Estimation of nonlinear indices from Polysomnography
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.

Locations

Country	Name	City	State
Greece	Sleep Unit of "G. Papanikolaou" General Hospital	Exochi

Sponsors (2)

Lead Sponsor	Collaborator
Aristotle University Of Thessaloniki	Greek State Scholarship Foundation

Country where clinical trial is conducted

Greece, 

References & Publications (1)

Kaimakamis E, Bratsas C, Sichletidis L, Karvounis C, Maglaveras N. Screening of patients with Obstructive Sleep Apnea Syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep. Conf Proc IEEE Eng Med Biol Soc. 2009;200 — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	nonlinear dynamics of respiratory signals	calculation of nonlinear parameters (DFA, LLE, APEN) from recorded respiratory biosignals (nasal airflow, thoracic movement and SpO2) during sleep.	One night	No