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

NCT ID: NCT01161381

Last Updated: 2010-07-13

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 100 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2005-11-30
• Study Completion Date: 2009-12-31

Brief 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.

Conditions

Obstructive Sleep Apnea Syndrome (OSAS)

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Study Groups

Normal

Subjects that underwent night polysomnography with an observed Apnea-Hypopnea Index (AHI) \< 5.

Estimation of nonlinear indices from Polysomnography

Intervention Type: 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.

OSAHS patients

Subjects that underwent night polysomnography with an observed Apnea-Hypopnea Index (AHI) \> 5.

Estimation of nonlinear indices from Polysomnography

Intervention Type: 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.

Interventions

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.

Intervention Type: DEVICE

Other Intervention Names

polysomnography device: Somnologica 7000, Flaga; Iceland

Eligibility Criteria

Inclusion Criteria

• symptoms compatible with OSAHS
• voluntary participation

Exclusion Criteria

• presence of dementia
• neuromuscular disorders
• overlap syndrome
• severe cardiac problems

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Greek State Scholarship Foundation

UNKNOWN

Sponsor Role: collaborator

Aristotle University Of Thessaloniki

OTHER

Sponsor Role: lead

Responsible Party

Aristotle University of Thessaloniki

Principal Investigators

Evangelos K Kaimakamis, MD, MSc

Role: PRINCIPAL_INVESTIGATOR

Aristotle University Of Thessaloniki

Nikolaos Maglaveras, PhD

Role: STUDY_CHAIR

Aristotle University Of Thessaloniki

Locations

Sleep Unit of "G. Papanikolaou" General Hospital

Exochi, , Greece

Countries

Greece

References

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. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3465-9. doi: 10.1109/IEMBS.2009.5334605.

Reference Type: RESULT

PMID: 19964987 (View on PubMed)

Kaimakamis E, Tsara V, Bratsas C, Sichletidis L, Karvounis C, Maglaveras N. Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals. PLoS One. 2016 Mar 3;11(3):e0150163. doi: 10.1371/journal.pone.0150163. eCollection 2016.

Reference Type: DERIVED

PMID: 26937681 (View on PubMed)

Other Identifiers

EK1001

Identifier Type: -

Identifier Source: org_study_id