caa a22 4500 605509808 CHVBK 20210128100643.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s10877-015-9664-0 doi (NATIONALLICENCE)springer-10.1007/s10877-015-9664-0 Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine [Elektronische Daten] [Jing Zhou, Xiao-ming Wu, Wei-jie Zeng] Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p<0.001). SVM was used and obtained high and consistent recognition rate: average classification accuracy reached 95.1% corresponding to the sensitivity 93.2% and specificity 98.6%. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea. Springer Science+Business Media New York, 2015 Sleep apnea syndrome (SAS) nationallicence Electroencephalogram (EEG) nationallicence Detrended fluctuation analysis (DFA) nationallicence Scaling exponents nationallicence Support vector machine (SVM) nationallicence Zhou Jing Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut Wu Xiao-ming Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut Zeng Wei-jie Department of Cardiovascular Medicine, The 421 Hospital of Chinese PLA, 510318, Guangzhou, China aut Journal of Clinical Monitoring and Computing Springer Netherlands 29/6(2015-12-01), 767-772 1387-1307 29:6<767 2015 29 10877 https://doi.org/10.1007/s10877-015-9664-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10877-015-9664-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhou Jing Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut NATIONALLICENCE 700 1- Wu Xiao-ming Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut NATIONALLICENCE 700 1- Zeng Wei-jie Department of Cardiovascular Medicine, The 421 Hospital of Chinese PLA, 510318, Guangzhou, China aut NATIONALLICENCE 773 0- Journal of Clinical Monitoring and Computing Springer Netherlands 29/6(2015-12-01), 767-772 1387-1307 29:6<767 2015 29 10877