This study aimed to classify EMG signals from healthy and neuromuscular disorder patients using an artificial neural network. Features were extracted from raw EMG signals and used to train and test the neural network. The network achieved 75% accuracy on the test data and 76% accuracy when classifying new samples, with an overall error rate of 10.6%. While the results demonstrated the neural network could classify the EMG signals, the authors concluded the accuracy could be improved and suggested applying convolutional neural networks or improved pre-processing in the future.

1. Study of EMG Signal Classification using
Artificial Neural Network
TANVIR RAHMAN
1621311
SUPERVISED BY: DR. MD. KAFIUL ISLAM,
ASSISTANT PROFESSOR, EEE, IUB
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2. Introduction
EMG is a procedure that
evaluates the condition of
muscles and the nervous system
that controls them.
Neural networks are a set of
algorithms, modeled loosely
after the human brain, that are
designed to recognize patterns.
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3. Motivation
To work with Machine
learning and get some
basic knowledge about
classification.
Use classifying technique
and features those were
not used in the recent
works.
Try to create an easier and
faster way to distinguish
neuromuscular disorders
from healthy people.
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4. Objective
To differentiate patients with
neuromuscular disorders
from healthy person using a
neural network in MATLAB.
To establish a classifying
technique for future works
with EMG signals.
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5. Method
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6. Sample Raw EMG Signal
s
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7. Literature Review
Paper Title Method Goal Accuracy
Electromyography (EMG) based Classification of
Neuromuscular Disorders using Multi-Layer
Perceptron
ANN Finding the feature with
best accuracy
83.5%
Electromyogram (EMG) signal detection,
classification of EMG signals and diagnosis of
neuropathy muscle disease.
SVM Classification of
neuropathy and healthy
subject.
N/A
EMG Classification with Ensemble-Empirical-
Mode-Decomposition-Based ICA for Diagnosing
Neuromuscular Disorders
Fast ICA Classification of ALS,
myopathy and healthy
subject.
98%
Identification of EMG signals using discriminant
analysis and SVM classifier
SVM Arm gesture recognition 98%
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8. Reason for choosing ANN
ANN can classify
more than Two-
Class problem
ANN has Better
accuracy than SVM
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9. Neural Network
Neural Network has 3 part:
1. Input
2. Hidden layer
3. Output
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10. Steps to create Neural Network
Collecting data
Extract Features
Training
Testing
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11. Feature extraction
Root mean square Waveform length Variance of EMG
Maximum fractal
length
Modified mean
absolute Value
Enhanced
Wavelength
Difference Absolute
Standard Deviation
Value
Average Amplitude
Change
Variance of FFT
FFT maximum
intensity
Variance of neo
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12. Training
Training part of a neural network has 3 parts:
1. Training
2. Validation
3. Testing(Tested by the network itself)
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13. Samples used for Training
Subjects Muscle type No. of
subjects
No. of
samples
Healthy Biceps brachii 7 80
Patient Biceps brachii 10 80
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14. Samples used for Testing
Subjects Muscle type No. of
subjects
No. of
samples
Healthy Biceps brachii 5 50
Patient Biceps brachii 6 50
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*Rows need to be the same as the number of rows of the input

15. Result of
Training part
Confusion matrix of training,
validation, test and all
confusion matrix
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16. Result
Training-88.4%
Validation-79.2%
Testing(Tested by the network)-75%
Overall-85%
Test(Result after creating an array)-76%
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17. Result of
testing part
Classifying patients after
testing
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18. Result of
testing part
Classifying healthy people
after testing
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19. Result
Analysis
Tested by the
network itself is
75% (test) &
85% (overall)
Accuracy after
creating an
array is 76%.
Error is 1.32%
Overall error is
10.6%
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20. Conclusion
We successfully created an
NN to classify EMG signals.
The accuracy we found is
good but needs improvment.
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21. Future Scope
Applying CNN.
Pre-processing the
signal.
Aim for a
promising
accuracy.
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22. Creating NN manually
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