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Volume 17, Issue 2 (Summer 2015)
Advances in Cognitive Sciences 2015, 17(2): 43-55 |
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Hosseini S A, Akbarzadeh Totonchi M R, Naghibi-Sistani M B. Epilepsy Recognition using Chaotic Qualitative and Quantitative Evaluation of EEG Signals. Advances in Cognitive Sciences 2015; 17 (2) :43-55
URL: http://icssjournal.ir/article-1-338-en.html
URL: http://icssjournal.ir/article-1-338-en.html
1- Instructor, Department of Electrical Engineering, Shahrood Branch, Islamic Azad University,Tehran,Iran.
2- Professor, Department of Electrical and Computer Engineering, Ferdowsi University of Mashhad,Mashhad,Iran.
3- Assiastant professor, Department of Electrical Engineering, Ferdowsi University of Mashhad ,Mashhad,Iran.
2- Professor, Department of Electrical and Computer Engineering, Ferdowsi University of Mashhad,Mashhad,Iran.
3- Assiastant professor, Department of Electrical Engineering, Ferdowsi University of Mashhad ,Mashhad,Iran.
Abstract: (3151 Views)
Introduction: Epilepsy is a disease of the central nervous system presenting by sudden convulsive attacks over a period of time.
Method: A key issue in recognition systems is optimal data acquisition as well as accurate labeling based on chaotic qualitative analyses and confirmatory annotations using expert eyes. To this end, chaotic features such as Petrosian fractal dimension, largest Lyapunov exponent and Hurst exponent were used in this investigation. Such features were submitted to the Bayesian classifier in order to have different categories seperated.
Results: Our findings confirmed a chaotic behavior in EEG with minimum embedding dimension reduced in ictal state. Similarly, the complexity of the ictal state was reduced. In addition, our results indicated an average classification accuracy of 99.2% for normal vs. pre-ictal states; the average classification accuracy is 99.7% for the normal vs. ictal states and the average classification accuracy is 97.1% for the pre-ictal vs. ictal states.
Conclusion: Chaotic analysis appears to serve as a useful method in representation and recognition of the brain activities in epileptic states.
Method: A key issue in recognition systems is optimal data acquisition as well as accurate labeling based on chaotic qualitative analyses and confirmatory annotations using expert eyes. To this end, chaotic features such as Petrosian fractal dimension, largest Lyapunov exponent and Hurst exponent were used in this investigation. Such features were submitted to the Bayesian classifier in order to have different categories seperated.
Results: Our findings confirmed a chaotic behavior in EEG with minimum embedding dimension reduced in ictal state. Similarly, the complexity of the ictal state was reduced. In addition, our results indicated an average classification accuracy of 99.2% for normal vs. pre-ictal states; the average classification accuracy is 99.7% for the normal vs. ictal states and the average classification accuracy is 97.1% for the pre-ictal vs. ictal states.
Conclusion: Chaotic analysis appears to serve as a useful method in representation and recognition of the brain activities in epileptic states.
Type of Study: Research |
Subject:
Special
Received: 2015/03/20 | Accepted: 2015/05/19 | Published: 2015/06/22
Received: 2015/03/20 | Accepted: 2015/05/19 | Published: 2015/06/22
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