Evaluation Of Patients With Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) 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.