Alvarez Daniel, Hornero Roberto, Marcos J, Del Campo Felix, Lopez Miguel
Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:400-3. doi: 10.1109/IEMBS.2009.5334905.
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.
血氧饱和度（SaO₂）和脑电图（EEG）记录可为阻塞性睡眠呼吸暂停（OSA）综合征的诊断提供补充信息。我们研究了148例疑似患有OSA的患者。基于特定频段的相对功率（A（f频段））或峰值幅度（PA）的经典频谱参数用于表征SaO₂和EEG记录的频率成分。此外，应用中位数频率（MF）和频谱熵（SE）来获取更多频谱信息。我们采用向前逐步逻辑回归（LR）程序并进行留一法交叉验证，以获得最佳频谱特征集。自动选择了来自血氧饱和度频谱分析的两个特征（PA和MFsat）以及来自脑电图频谱分析的三个特征（A（δ）、A（α）和SEeeg）。获得了91.0%的灵敏度、83.3%的特异性和88.5%的准确率。这些结果表明，MF和SE可为OSA诊断中常用的经典频率特征提供额外信息。此外，整夜的夜间SaO₂和EEG记录可为OSA综合征的检测提供补充信息。
