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

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.

Study Overview

• Status: Completed
• Conditions: Obstructive Sleep Apnea Syndrome (OSAS)
• Intervention / Treatment: Device: Estimation of nonlinear indices from Polysomnography

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.

• Study Type: Observational
• Enrollment (Actual): 100

Contacts and Locations

Study Locations

• Greece
  • Exochi, Greece, GR57010
    • Sleep Unit of "G. Papanikolaou" General Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• 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.

Description

Inclusion Criteria:

• symptoms compatible with OSAHS
• voluntary participation

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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 Names: 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 Names: polysomnography device: Somnologica 7000, Flaga; Iceland

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Aristotle University Of Thessaloniki
• Collaborators: 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

Publications and helpful links

General Publications

• 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.
• 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.

Study record dates

Study Major Dates

• Study Start: November 1, 2005
• Primary Completion (Actual): December 1, 2009
• Study Completion (Actual): December 1, 2009

Study Registration Dates

• First Submitted: July 12, 2010
• First Submitted That Met QC Criteria: July 12, 2010
• First Posted (Estimate): July 13, 2010

Study Record Updates

• Last Update Posted (Estimate): July 13, 2010
• Last Update Submitted That Met QC Criteria: July 12, 2010
• Last Verified: December 1, 2005

More Information

Terms related to this study

• Keywords: polysomnography; OSAHS; biosignal analysis; nonlinear analysis; DFA; APEN; LLE
• Additional Relevant MeSH Terms: Pathologic Processes; Nervous System Diseases; Respiratory Tract Diseases; Respiration Disorders; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Wake Disorders; Disease; Signs and Symptoms, Respiratory; Sleep Apnea Syndromes; Sleep Apnea, Obstructive; Syndrome; Apnea
• Other Study ID Numbers: EK1001