Automated Epileptic Seizures Detection and Classification

Authors

  • Harshavarthini S  Department of Computer science and Engineering, Sri Krishna college of Technology, Coimbatore, Tamil Nadu, India
  • Aswathy M. P.  Department of Computer science and Engineering, Sri Krishna college of Technology, Coimbatore, Tamil Nadu, India
  • Harshini P  Department of Computer science and Engineering, Sri Krishna college of Technology, Coimbatore, Tamil Nadu, India
  • Priyanka G  Department of Computer science and Engineering, Sri Krishna college of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT1951136

Keywords:

Electroencephalogram Signals, Probabilistic Neural Network, Discrete Wavelet Transformation, Gray-Level Co-Occurrence Matrix

Abstract

Detection of epileptic seizure activities from multi-channel electroencephalogram (EEG) signals plays a giant position inside the timely treatment of the sufferers with epilepsy. Visual identification of epileptic seizure in long-time period EEG is bulky and tedious for neurologists, which may additionally cause human errors. An automated device for accurate detection of seizures in a protracted-time period multi-channel EEG is crucial for the scientific prognosis. The features selection is based on discrete wavelet transformation (DWT).and feature extraction based GLCM. In the last stage, Probabilistic Neural Network is employed to classify the Normal and epileptic EEG signals.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-02-28

Issue

Volume 5, Issue 1, January-February-2019

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Harshavarthini S, Aswathy M. P., Harshini P, Priyanka G, " Automated Epileptic Seizures Detection and Classification, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.555-560, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT1951136