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.

Summary

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.