IC3: International IEEE Conference on Contemporary Computing Noida India Presented on 10th August 2017. Topic : Recognition of Epilepsy from Non Seizure Electroencephalogram using combination of Linear SVM and Time Domain Attributes.

1. Recognition of Epilepsy from Non-seizure
Electroencephalogram using combination of
Linear SVM and Time Domain Attributes
Authors
Debanshu Bhowmick
Department Of Applied Electronics and Instrumentation Engineering
Academy Of Technology
Atrija Singh
Department Of Electronics and Communication Engineering
Academy Of Technology
Sarini Sanyal
Department of Computer Science and Engineering
Academy of Technology
2017 Tenth International Conference on Contemporary Computing (IC3),IC3 2017

2. • Objective
• Dataset
• Methodology
• Results
• Conclusions and Future Scopes
Outline

3. Objective of the Study
• To develop a unique feature extraction approach to
classify a set of EEG signals into normal and Epileptic
set
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4. Dataset
• Collected from http://epileptologie-
bonn.de/cms/front_content.php?idcat=193&lang=3
• Sampled at 173.61 Hz
• Considered 200 EEG recordings under non-seizure
condition
• The 100 recordings correspond to healthy Subjects while
the rest are associated with diseased(epileptic )Subjects.
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5. Classification Scheme
EEG Signals in Digital Form
Feature Extraction (Time
Domain)
Classifier
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6. Previously Used Approaches on computer
based recognition of Epilepsy
Nigam et al proposal
• Neural-network based detection of Epilepsy
Subasi proposal
• Classification using Wavelet feature extraction and a mixture expert model
Polat et al proposal
• An Artificial immune recognition system with fuzzy resource allocation mechanism classifier,
PCA and FFT method based new hybrid automated identification system
Guler et al proposal
• Adaptive neuro-fuzzy interface system for classification of EEG signals using Wavelet
coefficients
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7. Previously Used Approaches on computer
based recognition of Epilepsy
Dibler et al proposal
• EEG based epilepsy detection using Mixed design approach
Geethanjali et al proposal
• A Time domain feature extraction and classification of EEG
signal for BCI
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8. Proposed Time Domain Multi-Feature Set
Proposed Multi Feature Set
• SET I : Zero crossing(ZC)
• SET II :Mean Absolute Value(MAV)
• SET III :Root Mean Square (RMS)
• SET IV :Waveform Length(WL)
• SET V : ZC + MAV + RMS + WL (OUR
PROPOSED FEATURE SET)
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9. Classifier Used
Linear SVM
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10. Division of Dataset for Classification
• Training : 60%
• Testing : 40%
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11. Results
Classification performance comparison
with Zero Crossing and our proposed
feature set
Classification performance comparison
with Mean Absolute Value and our
proposed feature set
Classification
Accuracy(%)
Linear SVM
(Quadratic
Kernel)
Feature Set Used
Set – I Set- v
Accuracy 85.00 95.00
Sensitivity 84.21 97.50
Specificity 85.71 92.50
Precision 84.21 92.86
Classification
Accuracy(%)
Linear SVM
(Quadratic
Kernel)
Feature Set Used
Set – II Set- v
Accuracy 65.00 95.00
Sensitivity 89.13 97.50
Specificity 32.35 92.50
Precision 64.06 92.86

12. Results(Continued)
Classification Performance Comparison with
Root Mean Square and Our Proposed
Feature Set
Classification Performance Comparison with
Waveform Length and Our Proposed
Feature Set
Classification
Accuracy(%)
Linear SVM
(Quadratic
Kernel)
Feature Set Used
Set – III Set- v
Accuracy 70.00 95.00
Sensitivity 97.37 97.50
Specificity 45.24 92.50
Precision 61.67 92.86
Classification
Accuracy(%)
Linear SVM
(Quadratic
Kernel)
Feature Set Used
Set – IV Set- v
Accuracy 82.50 95.00
Sensitivity 87.50 97.50
Specificity 77.50 92.50
Precision 79.55 92.86

13. Results(continued)
Histogram plot of Confusion Matrix For Linear SVM with Quadratic Kernel.

14. Conclusion and future scopes
1. The study clearly shows the efficiency of our
proposed feature set compared to traditional
techniques
2. This study also shows that an ensemble of time
domain features dominates over a single, individual
feature
3. The idea of the paper can be extended to the analysis
of multiple class neural disorders
4. At the same time, we can also study the feature
based changes that appear in the signals due to
change of clinical signals.

16. References
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Neurosurgery, Neurology and Neurosciences , vol. 26, no. 1, 2004.
2. A. Subasi, "EEG signal classification using wavelet feature extraction," Expert Systems with
Applications, 2006.
3. K. Polat and S. Gunes, "Artificial immune recognition system with fuzzy resource allocation mechanism
classifier, principal component analysis and FFT method based new hybrid automated identification
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2048, 2008
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