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Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine
Jing Zhou1, Xiao-ming Wu2, Wei-jie Zeng3
1Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510640, China. hellozj@scut.edu.cn.
This study introduces a new, simple method using electroencephalogram (EEG) nonlinear analysis to detect sleep apnea syndrome (SAS). Detrended fluctuation analysis (DFA) of EEG signals achieved 95.1% accuracy in identifying sleep apnea patients.
Failed At:
2026-06-19T13:37:16.035107+00:00
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