Analyzing normalized beta wave power in EEG signals: a comparative study between C4-A1 and EMG1-EMG2 channels for RBD sleep disorder detection

Abstract

Sleep disorders are medical conditions affecting the sleep patterns of individuals or living beings, with some being severe enough to disrupt normal physical, mental, and emotional functioning. This research article discusses the analysis of the attributes and waveforms of electroencephalogram (EEG) signals in humans. The major objective is to present the findings through signal spectrum analysis, highlighting changes through various sleep stages. The objective of this research is to assess the potential effectiveness of EEG patterns in diagnosing sleep disorders, particularly those associated with rapid eye movement behavior disorder. These conditions frequently lead to detectable alterations in the electrical and chemical processes within the brain, which can be analyzed by examining brain signals and images. This research paper utilizes the short time-frequency analysis of power spectrum density (STFAPSD) method on EEG signals to diagnose various types of sleep disorders. Calculated values are normalized and the average power of the spectral signal spectra, relating to EEG wave components (delta: 1-4 Hz; theta: 4-8 Hz; alpha: 8-13 Hz; beta 13--25~30 Hz). These indices are used as diagnoses to discriminate among different types of sleep disturbances. The results comparison performs accurate power spectral density (PSD) estimations for several sleep disorders, which makes this technique highly efficient to analyze a large database in a short time. Importantly, we achieve significantly results when analyzing the normalized beta power of both C4-A1 and EMG1-EMG2 channels during the rapid eye movement (REM) stage in the EEG signal. This observation demonstrates a strong difference in PSD values (beta normalized) between normals and REM sleep behavior disorders (RBDs).