Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis

This study assessed the hypothesis that blood oxygen saturation (SaO(2)) and electroencephalogram (EEG) recordings could provide complementary information in the diagnosis of the obstructive sleep apnea (OSA) syndrome. We studied 148 patients suspected of suffering from OSA. Classical spectral parameters based on the relative power in specified frequency bands (A(f-band)) or peak amplitudes (PA) were used to characterize the frequency content of SaO(2) and EEG recordings. Additionally, the median frequency (MF) and the spectral entropy (SE) were applied to obtain further spectral information. We applied a forward stepwise logistic regression (LR) procedure with crossvalidation leave-one-out to obtain the optimum spectral feature set. Two features from the oximetric spectral analysis (PA and MFsat) and three features from the EEG spectral analysis (A(delta), A(alpha) and SEeeg) were automatically selected. 91.0% sensitivity, 83.3% specificity and 88.5% accuracy were obtained. These results suggest that MF and SE could provide additional information to classical frequency characteristics commonly used in OSA diagnosis. Additionally, nocturnal SaO(2) and EEG recordings during the whole night could provide complementary information to help in the detection of OSA syndrome.