Epilepsy is a central nervous system (neurological) disorder that is caused by abnormal pathologic oscillating activity of a group of nerve cells in the brain. The electroencephalographic signals gained from brain electrical activities are mostly used for the diagnosis of neurological diseases. These signals indicate electrical activities in the brain and they contain some data about the brain; however, gaining long-term EEG data with seizure activities specifically in regions lacking medical centers and educated neurologists would be very costly and unpleasant. In this article based on electroencephalogram (EEG) signals, a new method is proposed for the automatic detection of Epilepsy. The aim of this article is to provide a model for the detection of Epilepsy by SVM optimization using genetic algorithm for the classification of EEG data. SVMs are one the powerful technics of machine learning, and they are widely applicable in many fields. The training and testing data were obtained from investigating EEG signals of 367 healthy and ill individuals. The data used in this paper have been derived from Barekat Imam Khomeini (RAH) Hospital in Miyaneh city. In this study the noise removal was done over the data by FIR Filter and genetic algorithm was used for the calculation of filter coefficients and optimal sample number. This method classifies the signals of both healthy individuals and the ones with Epilepsy with an accuracy of 100%.
| Published in | Machine Learning Research (Volume 5, Issue 2) |
| DOI | 10.11648/j.mlr.20200502.12 |
| Page(s) | 28-38 |
| 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 |
Epilepsy, SVM, Genetic Algorithm, EEG Signals
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APA Style
Simin Mirzayi, Saman Rajebi. (2020). Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network. Machine Learning Research, 5(2), 28-38. https://doi.org/10.11648/j.mlr.20200502.12
ACS Style
Simin Mirzayi; Saman Rajebi. Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network. Mach. Learn. Res. 2020, 5(2), 28-38. doi: 10.11648/j.mlr.20200502.12
AMA Style
Simin Mirzayi, Saman Rajebi. Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network. Mach Learn Res. 2020;5(2):28-38. doi: 10.11648/j.mlr.20200502.12
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Download@article{10.11648/j.mlr.20200502.12,
author = {Simin Mirzayi and Saman Rajebi},
title = {Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network},
journal = {Machine Learning Research},
volume = {5},
number = {2},
pages = {28-38},
doi = {10.11648/j.mlr.20200502.12},
url = {https://doi.org/10.11648/j.mlr.20200502.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20200502.12},
abstract = {Epilepsy is a central nervous system (neurological) disorder that is caused by abnormal pathologic oscillating activity of a group of nerve cells in the brain. The electroencephalographic signals gained from brain electrical activities are mostly used for the diagnosis of neurological diseases. These signals indicate electrical activities in the brain and they contain some data about the brain; however, gaining long-term EEG data with seizure activities specifically in regions lacking medical centers and educated neurologists would be very costly and unpleasant. In this article based on electroencephalogram (EEG) signals, a new method is proposed for the automatic detection of Epilepsy. The aim of this article is to provide a model for the detection of Epilepsy by SVM optimization using genetic algorithm for the classification of EEG data. SVMs are one the powerful technics of machine learning, and they are widely applicable in many fields. The training and testing data were obtained from investigating EEG signals of 367 healthy and ill individuals. The data used in this paper have been derived from Barekat Imam Khomeini (RAH) Hospital in Miyaneh city. In this study the noise removal was done over the data by FIR Filter and genetic algorithm was used for the calculation of filter coefficients and optimal sample number. This method classifies the signals of both healthy individuals and the ones with Epilepsy with an accuracy of 100%.},
year = {2020}
}
TY - JOUR T1 - Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network AU - Simin Mirzayi AU - Saman Rajebi Y1 - 2020/10/07 PY - 2020 N1 - https://doi.org/10.11648/j.mlr.20200502.12 DO - 10.11648/j.mlr.20200502.12 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 28 EP - 38 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20200502.12 AB - Epilepsy is a central nervous system (neurological) disorder that is caused by abnormal pathologic oscillating activity of a group of nerve cells in the brain. The electroencephalographic signals gained from brain electrical activities are mostly used for the diagnosis of neurological diseases. These signals indicate electrical activities in the brain and they contain some data about the brain; however, gaining long-term EEG data with seizure activities specifically in regions lacking medical centers and educated neurologists would be very costly and unpleasant. In this article based on electroencephalogram (EEG) signals, a new method is proposed for the automatic detection of Epilepsy. The aim of this article is to provide a model for the detection of Epilepsy by SVM optimization using genetic algorithm for the classification of EEG data. SVMs are one the powerful technics of machine learning, and they are widely applicable in many fields. The training and testing data were obtained from investigating EEG signals of 367 healthy and ill individuals. The data used in this paper have been derived from Barekat Imam Khomeini (RAH) Hospital in Miyaneh city. In this study the noise removal was done over the data by FIR Filter and genetic algorithm was used for the calculation of filter coefficients and optimal sample number. This method classifies the signals of both healthy individuals and the ones with Epilepsy with an accuracy of 100%. VL - 5 IS - 2 ER -
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