To detect the sleep apnea electroencephalogram (EEG) signal, several feature extraction and optimisation methods were investigated in this chapter. The sleep apnea signals were acquired in this experiment. This chapter researched on the abnormalities in EEG for those who suffered from sleep apnea. The statistical correlation measurement of the EEG brain signals was analysed mainly to identify the abnormalities and specific features of patients who suffered from sleep apnea. The features and characteristics of the EEG signals were measured using the fundamental section of the Hilbert-Huang transform (HHT) decomposition method to breakdown EEG sleep apnea data into finite and smaller components. Based on this research, the fundamental empirical mode decomposition (EMD), bivariate decomposition and white noise-based ensemble EMD (EEMD) to the targeted EEG data method were investigated to obtain instantaneous frequency data. All these three methods were used to analyse the extracted sleep apnea EEG signals and features.

Chapter Contents:

• 3.1 Introduction
• 3.2 Literature review
• 3.3 Research methodology
• 3.4 Experimental results
• 3.4.1 The experimental setup of sleep apnea study
• 3.4.2 Effect of forebody
• 3.4.3 Performance analysis using index of orthogonality
• 3.4.4 Extracting sleep bands using wavelet
• 3.4.5 Extracting sleep bands using EMD
• 3.5 Conclusion
• References

Inspec keywords: medical disorders; white noise; statistical analysis; Hilbert transforms; electroencephalography; feature extraction; sleep; medical signal processing; brain

Other keywords: EEG signal detection; sleep apnea EEG signal extraction; noise-based ensemble EMD; EEG sleep apnea data; statistical correlation measurement; EEG brain signals; bivariate decomposition; feature extraction method; fundamental empirical mode decomposition; sleep apnea; feature optimisation; Hilbert-Huang Transform decomposition method

Subjects: Digital signal processing; Electrical activity in neurophysiological processes; Electrodiagnostics and other electrical measurement techniques; Probability theory, stochastic processes, and statistics; Pattern recognition; Biology and medical computing; Probability and statistics; Probability and statistics; Signal processing and detection; Image recognition; Bioelectric signals