Automatic Sleep Apnea Detection Using Fuzzy Logic

The obstructive sleep apnea (OSA) is one of the most important sleep disorders characterized by obstruction of the respiratory tract and cessation in respiratory flow level. Currently, apnea diagnosis is mainly based on the Polysomnography (PSG) testing during sleeping hours, however, recording the entire signals during nights is a very costly, time-consuming and difficult task. The goal of this study is to provide and validate an automatic algorithm to analyze four PSG-recordings and detect the occurrence of sleep apnea by non-invasive features. Four PSG signals were extracted from oxygen saturation (SaO2), Transitional air flow (Air Flow), abdominal movements during breathing (Abdomen mov.) and movements of the chest (Thoracic mov.). We describe a fuzzy algorithm to compensate the imprecise information about the range of signal loss, regarding the expert opinions. Signal classification is implemented minute-by-minute and for 30 labeled samples of MIT/BIH data sets (acquired from PhysioNet). The obtained data from 18 apnea subjects (11 males and 7 females, mean age 43 years) were categorized in three output signals of apnea, hypopnea and normal breathing. The proposed algorithm shows proficiency in diagnosing OSA with acceptable sensitivity and specificity, respectively 86% and 87%.