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Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network

Received: 28 June 2017     Accepted: 17 July 2017     Published: 24 October 2017
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Abstract

EEG (Electroencephalogram) is a technique for identifying neurological disorders. There are various neurological disorders like Epilepsy, brain cancer, etc. Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non stationary signals like EEG. In this work, neural network analysis (NNA) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Two types of EEG signals (healthy subject with eye open condition, epileptic) were selected for the analysis. Signals were reprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like mean. Standard deviation, median, entropy, kurtosis and skewness were computed and consequently used for classification of signals. The range of these features in non-epileptic and epileptic group of 80 subjects each from data set is analyzed for data available at the Department of Epileptology, University of Bonn, and the parameters with distinct non-overlapping zone are identified. The results show the promising classification accuracy of nearly 100% in detection of abnormal from normal EEG signals. The main purpose of this new approach is that the computation time of NNA classifier is less to provide better accuracy. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.

Published in International Journal of Neurologic Physical Therapy (Volume 3, Issue 5)
DOI 10.11648/j.ijnpt.20170305.11
Page(s) 38-43
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Electroencephalogram (EEGs), Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), Neural Network Analysis (NNA), Epileptic, Seizure

References
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[2] A. Massimo, “In Memoriam Pierre Gloor 1923–2003: an appreciation”. Epilepsia, vol. -45(7), July 2004, page-882.
[3] M. A. B. Brazier. “A History of the Electrical Activity of the Brain”. The First Half-Century, Macmillan, New York, 1961.
[4] M. D. Alessandro, R. Esteller, G. Vachtsevanos, A. Hinson, A. Echauz, and B. Litt. "Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients". IEEE Transactions on Biomedical Engineering-2003. vol. -50 (5), pp. -603–615.
[5] B. P. Marchant. “Time–frequency analysis for biosystem engineering”. Biosystems Engineering-2003. vol. -85 (3), pp. -261–281.
[6] Subasi, A., EEG signal classification using wavelet function extraction and a mixture of expert model', Expert System with Application, 32, 1084-1093, 2007.
[7] A. Subasi, and M. Ismail Gursoy. “EEG signal classification using PCA, ICA, LDA and support vector machines”. Expert Systems with Applications-2010. vol. 37, pp. -8659–8666.
[8] A. Subasi. “Epileptic seizure detection using dynamic wavelet network”. Expert Systems with Applications-2005. vol. -29, pp.-343–355.
[9] K. Fu, J. Qu, Y. Chai, and Y. Dong, “Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM”. Biomedical Signal Processing and Control-2014, vol. -13, pp. -15–22.
[10] R. G. Andrzejak, K. Lehnertz, F. Mormann, et al. “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state”. Phys. Rev. Ed- 64 (6)-061907.2001.
[11] Durka P. J. Adaptive time-frequency parametrization of epileptic spikes. Physical Review E 2004; 69: 051914.
[12] Kumari Pinki, and Abhishek Vaish. “Brainwave based user identification system: A pilot study in robotics environment.” Robotics and Autonomous Systems 65 (2015): 15-23.
[13] C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to wavelets and wavelet transforms: A primer. Prentice-Hall, Upper Saddle River, NJ, 1998.
[14] Mandeep Singh, SunpreetKaur, Epilepsy, “Frequency Band Separation for Epilepsy Detection Using EEG”, International Journal of Information Technology & Knowledge Management, Vol 6, No. 1, Dec 2012.
[15] Livingstone D. J. Artificial Neural Networks: Methods and Applications (Methods in Molecular Biology). Humana Press, 2008.
[16] Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. Journal of biomedical informatics 2002; 35: 352-359.
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[19] Rezvan Abbas and Mansour Esmaeilpour International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, No. 5, 2017: 33-38.
[20] A. Sharmila P. Geethanjali “DWT-Based Detection of Epileptic Seizure from EEG Signals” IEEE Transuction on Digital Object Identifier vol. 1 2016, pp- 7716 - 7727.
Cite This Article
  • APA Style

    Manisha Chandani, Arun Kumar. (2017). Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network. International Journal of Neurologic Physical Therapy, 3(5), 38-43. https://doi.org/10.11648/j.ijnpt.20170305.11

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    ACS Style

    Manisha Chandani; Arun Kumar. Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network. Int. J. Neurol. Phys. Ther. 2017, 3(5), 38-43. doi: 10.11648/j.ijnpt.20170305.11

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    AMA Style

    Manisha Chandani, Arun Kumar. Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network. Int J Neurol Phys Ther. 2017;3(5):38-43. doi: 10.11648/j.ijnpt.20170305.11

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  • @article{10.11648/j.ijnpt.20170305.11,
      author = {Manisha Chandani and Arun Kumar},
      title = {Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network},
      journal = {International Journal of Neurologic Physical Therapy},
      volume = {3},
      number = {5},
      pages = {38-43},
      doi = {10.11648/j.ijnpt.20170305.11},
      url = {https://doi.org/10.11648/j.ijnpt.20170305.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijnpt.20170305.11},
      abstract = {EEG (Electroencephalogram) is a technique for identifying neurological disorders. There are various neurological disorders like Epilepsy, brain cancer, etc. Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non stationary signals like EEG. In this work, neural network analysis (NNA) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Two types of EEG signals (healthy subject with eye open condition, epileptic) were selected for the analysis. Signals were reprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like mean. Standard deviation, median, entropy, kurtosis and skewness were computed and consequently used for classification of signals. The range of these features in non-epileptic and epileptic group of 80 subjects each from data set is analyzed for data available at the Department of Epileptology, University of Bonn, and the parameters with distinct non-overlapping zone are identified. The results show the promising classification accuracy of nearly 100% in detection of abnormal from normal EEG signals. The main purpose of this new approach is that the computation time of NNA classifier is less to provide better accuracy. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.},
     year = {2017}
    }
    

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    AU  - Manisha Chandani
    AU  - Arun Kumar
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    T2  - International Journal of Neurologic Physical Therapy
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    AB  - EEG (Electroencephalogram) is a technique for identifying neurological disorders. There are various neurological disorders like Epilepsy, brain cancer, etc. Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non stationary signals like EEG. In this work, neural network analysis (NNA) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Two types of EEG signals (healthy subject with eye open condition, epileptic) were selected for the analysis. Signals were reprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like mean. Standard deviation, median, entropy, kurtosis and skewness were computed and consequently used for classification of signals. The range of these features in non-epileptic and epileptic group of 80 subjects each from data set is analyzed for data available at the Department of Epileptology, University of Bonn, and the parameters with distinct non-overlapping zone are identified. The results show the promising classification accuracy of nearly 100% in detection of abnormal from normal EEG signals. The main purpose of this new approach is that the computation time of NNA classifier is less to provide better accuracy. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.
    VL  - 3
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Author Information
  • Department of Electronics & Telecommunication, Chhattisgarh Swami Vivekananda Technical University, Durg, India

  • Department of Electronics & Telecommunication, Chhattisgarh Swami Vivekananda Technical University, Durg, India

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