Selection of Feature for Epilepsy Seizer Detection Using EEG

Manisha Chandani; Arun Kumar

doi:doi:10.11648/j.ijn.20180201.11

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Selection of Feature for Epilepsy Seizer Detection Using EEG

Manisha Chandani, Arun Kumar

Published in International Journal of Neurosurgery (Volume 2, Issue 1)

Received: 8 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.

Published in	International Journal of Neurosurgery (Volume 2, Issue 1)
DOI	10.11648/j.ijn.20180201.11
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), 2018. 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.

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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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ACS 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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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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Selection of Feature for Epilepsy Seizer Detection Using EEG
AU  - Manisha Chandani
AU  - Arun Kumar
Y1  - 2018/04/20
PY  - 2018
N1  - https://doi.org/10.11648/j.ijn.20180201.11
DO  - 10.11648/j.ijn.20180201.11
T2  - International Journal of Neurosurgery
JF  - International Journal of Neurosurgery
JO  - International Journal of Neurosurgery
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2640-1959
UR  - https://doi.org/10.11648/j.ijn.20180201.11
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.
VL  - 2
IS  - 1
ER  -

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Author Information

Manisha Chandani

Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India
Arun Kumar

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

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APA Style

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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ACS 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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Selection of Feature for Epilepsy Seizer Detection Using EEG
AU  - Manisha Chandani
AU  - Arun Kumar
Y1  - 2018/04/20
PY  - 2018
N1  - https://doi.org/10.11648/j.ijn.20180201.11
DO  - 10.11648/j.ijn.20180201.11
T2  - International Journal of Neurosurgery
JF  - International Journal of Neurosurgery
JO  - International Journal of Neurosurgery
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2640-1959
UR  - https://doi.org/10.11648/j.ijn.20180201.11
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.
VL  - 2
IS  - 1
ER  -

Copy | Download