1. Muscle Analysis of REM Behaviour Disorder
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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
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3. Cause of Death in North America
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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
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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
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6. Polysomnography
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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
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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 .
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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
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10. Time Analysis of EMG
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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.
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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.
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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
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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
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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%)
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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
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18. Time- Frequency Analysis
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19. Wavelet Type Discrete Wavelet Transform (DWT) Wavelet Decomposition
DWT Decomposition of Normal EMG
DWT Decomposition of Abnormal EMG
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20. Wavelet Type Continued
Continuous Wavelet Transform (CWT)
b – shift coefficient
a – scale coefficient ( ) a b x - Y = 1 ,
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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
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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.
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23. CWT
Step 3: Shift the wavelet to the right and repeat steps 1-2 until you’ve covered the whole signal
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24. CWT Step 4: Scale (stretch) the wavelet and repeat steps 1-3
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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)
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27. Dominant Wavelet Coefficients of Normal and Abnormal for REM stage
Dominant Features
Dominant Wavelet Coefficients of Normal and Abnormal for REM stage
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29. ROC Curve
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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
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31. Questions
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