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

This study has been completed.
Sponsor:
Collaborator:
Greek State Scholarship Foundation
Information provided by:
Aristotle University Of Thessaloniki
ClinicalTrials.gov Identifier:
NCT01161381
First received: July 12, 2010
Last updated: NA
Last verified: December 2005
History: No changes posted
  Purpose

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.


Condition Intervention
Obstructive Sleep Apnea Syndrome (OSAS)
Device: Estimation of nonlinear indices from Polysomnography

Study Type: Observational
Study Design: Observational Model: Case-Only
Time Perspective: Prospective
Official Title: Evaluation Of Patients With Suspected Obstructive Sleep Apnea - Hypopnea Syndrome Using Two Models Based on Nonlinear Analysis Of Respiratory Signals

Resource links provided by NLM:


Further study details as provided by Aristotle University Of Thessaloniki:

Primary Outcome Measures:
  • nonlinear dynamics of respiratory signals [ Time Frame: One night ] [ Designated as safety issue: No ]
    calculation of nonlinear parameters (DFA, LLE, APEN) from recorded respiratory biosignals (nasal airflow, thoracic movement and SpO2) during sleep.


Enrollment: 100
Study Start Date: November 2005
Study Completion Date: December 2009
Primary Completion Date: December 2009 (Final data collection date for primary outcome measure)
Groups/Cohorts Assigned Interventions
Normal
Subjects that underwent night polysomnography with an observed Apnea-Hypopnea Index (AHI) < 5.
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.
Other Name: polysomnography device: Somnologica 7000, Flaga; Iceland
OSAHS patients
Subjects that underwent night polysomnography with an observed Apnea-Hypopnea Index (AHI) > 5.
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.
Other Name: polysomnography device: Somnologica 7000, Flaga; Iceland

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.

  Eligibility

Ages Eligible for Study:   18 Years to 75 Years
Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   Yes
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.

Criteria

Inclusion Criteria:

  • symptoms compatible with OSAHS
  • voluntary participation

Exclusion Criteria:

  • presence of dementia
  • neuromuscular disorders
  • overlap syndrome
  • severe cardiac problems
  Contacts and Locations
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the Contacts provided below. For general information, see Learn About Clinical Studies.

Please refer to this study by its ClinicalTrials.gov identifier: NCT01161381

Locations
Greece
Sleep Unit of "G. Papanikolaou" General Hospital
Exochi, Greece, GR57010
Sponsors and Collaborators
Aristotle University Of Thessaloniki
Greek State Scholarship Foundation
Investigators
Principal Investigator: Evangelos K Kaimakamis, MD, MSc Aristotle University Of Thessaloniki
Study Chair: Nikolaos Maglaveras, PhD Aristotle University Of Thessaloniki
  More Information

Publications:
Responsible Party: Lab of Medical Informatics, Aristotle University of Thessaloniki
ClinicalTrials.gov Identifier: NCT01161381     History of Changes
Other Study ID Numbers: EK1001
Study First Received: July 12, 2010
Last Updated: July 12, 2010
Health Authority: Greece: Ethics Committee

Keywords provided by Aristotle University Of Thessaloniki:
OSAHS
nonlinear analysis
polysomnography
biosignal analysis
DFA
APEN
LLE

Additional relevant MeSH terms:
Apnea
Sleep Apnea Syndromes
Sleep Apnea, Obstructive
Respiration Disorders
Respiratory Tract Diseases
Signs and Symptoms, Respiratory
Signs and Symptoms
Sleep Disorders, Intrinsic
Dyssomnias
Sleep Disorders
Nervous System Diseases

ClinicalTrials.gov processed this record on August 28, 2014