International Journal of Neurosurgery

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Selection of Feature for Epilepsy Seizer Detection Using EEG

Received: 08 January 2018    Accepted: 23 March 2018    Published: 20 April 2018
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Abstract

The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. EEG signal when decomposed into frequency subbands, gives us several statistical features in each band. Some of these features that may be employed for detection of epilepsy are explored in this paper.

DOI 10.11648/j.ijn.20180201.11
Published in International Journal of Neurosurgery (Volume 2, Issue 1, June 2018)
Page(s) 1-7
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), 2024. Published by Science Publishing Group

Keywords

Electroencephalography (EEGs), Epileptic, Seizure

References
[1] Abdulhamit Subasi (2007). “EEG Signal Classification Using Wavelet Feature Extraction and a Mixture of Expert Model”, Expert Systems with Applications 32, pp. no 1084-1093.
[2] N. Kannathal, Min Lim Choo, U. Rajendra Acharya and P. K. Sadasivan (2005). “Entropies for Detection of Epilepsy in EEG”, Computer Methods and Programs in Biomedicine 80, pp. no 187-194.
[3] Tapan Gandhi, Bijay Ketan Panigrahi and Sneh Anand (2011). “A Comparative Study of Wavelet Families for EEG Signal Classification”, Neurocomputing 74, pp no 3051-3057.
[4] R. K. Chaurasiya, N. D. Londhe and S. Ghosh (2015). “Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification” IJECEECE, Vol: 9, pp. 182-186.
[5] R. G. Andrezejak, K. Lehnertz & F. Morman, (2001). Indication 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.
[6] Durka P. J. (2004). Adaptive time-frequency parametrization of epileptic spikes. Physical Review E; 69:051914.
[7] Kumari Pinki & AbhishekVaish (2015). Brainwave based user identification system: A pilot study in robotics environment. Robotics and Autonomous Systems 65, pp. 15-23.
[8] Arun Kumar and Manisha Chandani (2017). “Analysis of EEG Physiological Signal for The Detection of Epileptic Seizure ” i-manager’s Journal on Pattern Recognation, Vol. 4, pp. 1-9.
[9] Subasi A (2007). “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Syst Appl., vol. 32 (4), pp. 1084-1093.
[10] Meenakshi, Dr. R. K Singh, Prof. A. K Singh (2014). “Frequency Analysis of Healthy & Epileptic Seizure in EEG using Fast Fourier Transform” International Journal of Engineering Research and General Science Volume 2, Issue 4, pp- 683-691.
[11] Welch P D (1967). “The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms,” IEEE Trans Audio Electroacoust., vol. AU-15, pp. 70-73.
[12] Arun Kumar and Manisha Chandani (2017)“Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network ” International Journal of Neurologic Physsical Therapy Vol. 3., pp- 38-43.
[13] Abdulhamit Subasi (2007). “EEG Signal Classification Using Wavelet Feature Extraction and a Mixture of Expert Model”, Expert Systems with Applications 32, pp. no 1084-1093.
[14] N. Kannathal, Min Lim Choo, U. Rajendra Acharya and P. K. Sadasivan (2005). “Entropies for Detection of Epilepsy in EEG”, Computer Methods and Programs in Biomedicine 80, pp. no 187-194.
[15] Tapan Gandhi, Bijay Ketan Panigrahi and Sneh Anand (2011). “A Comparative Study of Wavelet Families for EEG Signal Classification”, Neurocomputing 74, pp no 3051-3057.
Author Information
  • Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India

  • Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India

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    Manisha Chandani, Arun Kumar. (2018). Selection of Feature for Epilepsy Seizer Detection Using EEG. International Journal of Neurosurgery, 2(1), 1-7. https://doi.org/10.11648/j.ijn.20180201.11

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    Manisha Chandani; Arun Kumar. Selection of Feature for Epilepsy Seizer Detection Using EEG. Int. J. Neurosurg. 2018, 2(1), 1-7. doi: 10.11648/j.ijn.20180201.11

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    AMA Style

    Manisha Chandani, Arun Kumar. Selection of Feature for Epilepsy Seizer Detection Using EEG. Int J Neurosurg. 2018;2(1):1-7. doi: 10.11648/j.ijn.20180201.11

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  • @article{10.11648/j.ijn.20180201.11,
      author = {Manisha Chandani and Arun Kumar},
      title = {Selection of Feature for Epilepsy Seizer Detection Using EEG},
      journal = {International Journal of Neurosurgery},
      volume = {2},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.ijn.20180201.11},
      url = {https://doi.org/10.11648/j.ijn.20180201.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijn.20180201.11},
      abstract = {The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. EEG signal when decomposed into frequency subbands, gives us several statistical features in each band. Some of these features that may be employed for detection of epilepsy are explored in this paper.},
     year = {2018}
    }
    

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    AU  - Arun Kumar
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    AB  - The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. EEG signal when decomposed into frequency subbands, gives us several statistical features in each band. Some of these features that may be employed for detection of epilepsy are explored in this paper.
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