Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

ABSTRACT:

Introduction

Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder.

Materials and methods

Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining a

SUBMITTER: Kaimakamis E 

PROVIDER: S-EPMC4777493 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature