Automated Epileptic Seizures Detection and Classification

Authors(4) :-Harshavarthini S, Aswathy M. P., Harshini P, Priyanka G

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 555-560
Manuscript Number : CSEIT1951136
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Harshavarthini S, Aswathy M. P., Harshini P, Priyanka G, "Automated Epileptic Seizures Detection and Classification", International 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
Journal URL : http://ijsrcseit.com/CSEIT1951136

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