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Muscle Analysis of REM Behaviour Disorder

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Presentation on theme: "Muscle Analysis of REM Behaviour Disorder"— Presentation transcript:

1 Muscle Analysis of REM Behaviour Disorder
11/21/2018 Prepared by: Mehrnaz Shokrollahi Supervisor: Dr. Sri Krishnan Clinical Advisor: Dr. Brian Murray

2 Outline Introduction Sleep Literature Review Sleep Stages
Sleep Disorder RBD and Parkinson Signal Processing RLS AR modeling Cepstrum Analysis Wavelet Analysis Classification Result Discussion 11/21/2018

3 Cause of Death in North America
11/21/2018

4 Parkinson Disease Degenerative disease of the brain (central nervous system) Impairs motor skills, speech and other functions ~90% of the Neurons are dead Disturbance in REM sleep: Disturbingly vivid Dreams REM Sleep Behavior Disorder (RBD) Characterized by acting out of dream content Occur years prior to diagnosis 11/21/2018

5 Sleep Sleep Stages Non-Rapid Eye Movement
Stage Wake  Body prepares to sleep Stage 1  Regular Breathing, Decrease in muscle activity, Light Sleep, Stage 2  Slow heart beat, regular breathing, 50% of sleep Stage 3 & 4  Deep Sleep stages, difficult to arouse Rapid Eye Movement Dream occurs, eyes move rapidly back and forth under closed eyelids. Temporarily paralyzed Sleep The basic cause for sleep paralysis during REM happens in the brainstem, the part of the brain that connects the spinal cord with the cerebral hemispheres, and includes the pons, midbrain, and the medulla oblongata. While doctors do not totally understand this complex processes, it has been shown that the brainstem undergoes changes in REM sleep which results in paralysis of the body's voluntary muscles. Certain neurotransmitters, like acetylcholine (Ach), become dormant and do not naturally transmit motor activity to ensure restful, inactive sleep during the most electrically active stage of sleep. In this context, sleep paralysis describes a normal state of sleep, unlike sleep paralysis experienced in narcolepsy, which affects people while they are trying to stay awake 11/21/2018

6 Polysomnography 11/21/2018

7 Sleep Paralysis The basic cause for sleep paralysis during REM happens in the brainstem, the part of the brain that connects the spinal cord with the cerebral hemispheres, and includes the pons, midbrain, and the medulla oblongata. While doctors do not totally understand this complex processes, it has been shown that the brainstem undergoes changes in REM sleep which results in paralysis of the body's voluntary muscles. Certain neurotransmitters, like acetylcholine (Ach), become dormant and do not naturally transmit motor activity to ensure restful, inactive sleep during the most electrically active stage of sleep. In this context, sleep paralysis describes a normal state of sleep, unlike sleep paralysis experienced in narcolepsy, which affects people while they are trying to stay awake 11/21/2018

8 Rapid Eye Movement Behaviour Disorder (RBD)
Loss of muscle control while in REM sleep Acting out dreams Brain damage Neurotransmitters are not blocked, voluntary muscles become tonic Associate with neurodegenerative diseases such as Parkinson Disease. Intimately intertwined with the development of Parkinson’s disease and other disorders . 11/21/2018

9 EMG Processing EMG  Record electrical activity of the muscle
Used clinically for diagnosis of neurological and neuromuscular problem Dataset  One Submental, One mental and a Reference The type of electrodes, position and firm contact with the skin 4 Normal and 4 abnormal subjects 8 hour sleep recordings EMG is performed by means of three electrodes on chin area: one mental, one submental and a reference. This configuration enables to realize the importance of scoring the Stage Rapid Eye Movement (REM). This EMG was acquired from 4 normal and 4 abnormal subjects undergoing overnight polysomnography. The type of electrodes, their position and firm contact with the skin are critical factors in obtaining good EMG recordings 11/21/2018

10 Time Analysis of EMG 11/21/2018

11 Recursive Least Square Algorithm
Signal type Deterministic Non-Deterministic  Stationary Non- Stationary If Stationary If Non-Stationary  The RLS algorithm uses the information contained in all the previous input data to estimate the inverse of the autocorrelation matrix of the input vector. It uses this estimate to properly adjust the tap weights of the filter. 11/21/2018

12 Autoregressive Modeling
All Pole modeling Potentially be correlated with the physiological system producing the signal Linear second moment stationary model Carry out the whole signal characteristics Burg-Lattice method with the order of 25 Modeling techniques such as autoregressive modeling (AR), also referred to as “all pole” modeling, provide parameters which could potentially be correlated with the physiological system producing the signals. The AR model is a linear, second-moment stationary model. AR modeling is compressions where the coefficients carry out the whole signal characteristics. In this project I used the Burg-Lattice method to find the AR coefficients of each segment [6]. The model order used was 25. The models of this order were observed to predict the EMG signal segments very well. 11/21/2018

13 Cepstral Analysis Inverse Fourier Transform of the log power spectrum of the signal Drive Directly from AR coefficients Where cn and αn denote the Cepstral and AR coefficient respectively 11/21/2018

14 Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) Supervised Algorithm Used in statistics and machine learning Find linear combination of features which best separate two or more classes Produces models whose accuracy approaches more complex modern methods 11/21/2018

15 Result Cont’d Normal Or Abnormal Predicted Group Membership Normal
Total Count Normal 491 124 56 285 547 409 % Normal 89.8 30.3 10.2 69.7 100 Table1: Classification of segments REM using AR Coefficients (Overall Accuracy of 79.7%) Normal Or Abnormal Predicted Group Membership Normal Abnormal Total Count Normal 473 131 74 278 547 409 % Normal 86.5 32.0 13.5 68.0 100 Table3: Classification of segments REM using AR Coefficients Table2: Classification of segments REM using Cepstrum Coefficients (Overall Accuracy of 77.3%) 11/21/2018

16 Dominant Features Dominant AR Coefficients of One Normal and One Abnormal for REM stage Dominant Cepstrum Coefficients of Normal and Abnormal for REM stage 11/21/2018

17 11/21/2018

18 Time- Frequency Analysis
11/21/2018

19 Wavelet Type Discrete Wavelet Transform (DWT) Wavelet Decomposition
DWT Decomposition of Normal EMG DWT Decomposition of Abnormal EMG 11/21/2018

20 Wavelet Type Continued
Continuous Wavelet Transform (CWT) b – shift coefficient a – scale coefficient ( ) a b x - Y = 1 , 11/21/2018

21 CWT Reminder: The CWT Is the sum over all time of the signal, multiplied by scaled and shifted versions of the wavelet function Step 1: Take a Wavelet and compare it to a section at the start of the original signal 11/21/2018

22 CWT Step 2: Calculate a number, C, that represents how closely correlated the wavelet is with this section of the signal. The higher C is, the more the similarity. 11/21/2018

23 CWT Step 3: Shift the wavelet to the right and repeat steps 1-2 until you’ve covered the whole signal 11/21/2018

24 CWT Step 4: Scale (stretch) the wavelet and repeat steps 1-3
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25 11/21/2018

26 Result of Wavelet Transform
Normal Or Abnormal Predicted Group Membership Normal Abnormal Total Count Normal 691 143 18 1498 709 1641 % Normal 97.5 8.7 2.5 91.3 100 Table 3: LDA Classification of segments REM using Wavelet Coefficients (Overall Accuracy of 93.1) 11/21/2018

27 Dominant Wavelet Coefficients of Normal and Abnormal for REM stage
Dominant Features Dominant Wavelet Coefficients of Normal and Abnormal for REM stage 11/21/2018

28 11/21/2018

29 ROC Curve 11/21/2018

30 Conclusion The result achieved may
Provide useful information to clinicians Easily applicable measure of disease activity Sensitive to very early neurodegeneration and treatment response The ROC curve of the Wavelet shows a better separablity compared to AR modeling and Cepstrum analysis 11/21/2018

31 Questions 11/21/2018


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