Enhanced Method for Extracting Features of Respiratory Signals and Detection of Obstructive Sleep Apnea Using Threshold Based Automatic Classification Algorithm

Obstructive Sleep Apnea is a frequent disorder with detrimental health, performance and safety effects. The diagnosis of the disorder is cumbersome and expensive. New methods for screening and diagnosis are needed. The method we describe in this work is based on detection of four main features of respiratory signal. The automatic signal classification starts by extracting signal features from a 1 minute data segment through autoregressive modeling (AR) and other techniques. Four features are: signal energy, zero crossing frequency, dominant frequency estimated by AR and strength of dominant frequency based on AR. These features are then compared to threshold values and introduced to a series of conditions to determine the signal category for each specific epoch. The threshold values for the parameters were determined through experiment. KeywordsSleep Apnea, Motion Artifact, Energy Index, Respiration rate, Dominant frequency, Strength of Dominant frequency, Zero Crossing.