EEG and HRV signal features for automatic sleep staging and apnea detection

Abstract

Sleep is a circadian rhythm essential for human life. Many events occur in the body during this state. In the past, significant efforts have been made to provide clinicians with reliable and less intrusive tools to automatically classify the sleep stages and detect apnea events. A few systems are available in the market to accomplish this task. However, sleep specialists may not have full confidence and trust in such systems due to issues related to their accuracy, sensitivity and specificity. The main objective of this work is to explore possible relationships among sleep stages and apneic events and improve on the accuracy of algorithms for sleep classification and apnea detection. Electroencephalogram (EEG) and Heart Rate Variability (HRV) will be assessed using advanced signal processing approaches such as Detrend Fluctuation Analysis (DFA). In this paper, we present a compendium of features extracted from EEG and Heart Rate Variability (HRV) data acquired from twenty five patients (21 males and 4 females) suffering from sleep apnea (age: 50 ± 10 years, range 28–68 years undergoing polysomnography). Polysomnographic data were available online from the Physionet database. Results show that trends detected by these features could distinguish between different sleep stages at a very significant level (p≪0.01). These features could prove helpful in computer-aided detection of sleep apnea.