Analysis of electromyogram in rapid eye movement sleep
thesisposted on 08.06.2021, 09:35 by Mehrnaz Shokrollahi
The aim of this study is to analyze Electromyogram (EMG) signals in Rapid Eye Movement (REM) sleep using different techniques to detect the level of normality and abnormality of normal and abnormal (patients with a lack of REM sleep atonia) subjects and predict the development of Parkinson’s disease in abnormal subjects. Quantitative elctromyogram (EMG) signal analysis in the frequency domain using classical power spectrum analysis techniques have been well documented over the past decade. Yet none of these [sic] work have been done on EMG during Rapid Eye Movement (REM) Stage of sleep. In this work three techniques for classifying chin movement via EMG signals during sleep is presented. Three methods (Autoregressive modeling, Cepstrum Analysis and Wavelet Analysis) for extracting features from EMG signal during sleep and a classification algorithm (Linear Discriminant Analysis (LDA)) were analyzed and compared. EMG data are used to detect and describe different disease processes affecting sleep. Rapid Eye Movement Behavior Disorder (RBD) is an example of EMG abnormality in which patients lose their muscle control while in REM stage of sleep resulting in physically acting out their dreams. An adaptive segmentation based on Recursive Least Square (RLS) algorithm was analyzed. This algorithm was used to segment the non-stationary EMG signal into locally stationary components, which were then autoregressive modeled using the Burg-Lattice method. The cepstral measurements described was used and applied to modify the coefficients computed from the autoregressive (AR) model. Yet due to the nature of the EMG, frequency analysis cannot be used to approximate a signal whose properties change over time. To address this problem a time varying feature representation is necessary for analysis to extract useful infomration from the signal. As a consequence Wavelet coefficients were computed using discrete and continuous wavelet transforms. Furthermore, the classification performance of the above three feature sets were investigated for the two classes (Normal and Abnormal). Results showed wavelet analysis compared to AR modeling and cepstrum analysis is a better assessment in finding EMG abnormalities during sleep. However, these methods may be useful in distinguishing EMG patterns that predict the emergence of Parkinson disease in humans.