Identification of Epilepsy utilizing Hilbert Transform and SVM based Classifier

Tara Mashayekh Bakhsh, Saeed Meshgini, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Epilepsy is a persistent neurological condition of the brain in which the activity of the brain goes out of normal state. Classification and Analysis of EEG signal is the early approach for epilepsy diagnosis. During this paper, we have a tendency to propose an EEG signal classification approach based on Support Vector Machine (SVM) classifier. In extracting features from the raw EEG data we applied the Hilbert Transform method and used its coefficients. Then, after the PCA dimension reduction a two-class SVM classifier is used for EEG signals automatic classification, one class for healthy subjects and another for subjects with epilepsy. In SVM classifier we need to divide the EEG signals into a training dataset and testing dataset for classification. We have used five sets of EEG signals which are publicly accessible on EEG time series database. The evaluation and comparison of SVM based classifier with two other classification methods such as KNN and LVQ based classifier was done. The average classification accuracy of our study is 95.33%.

Original languageEnglish
Title of host publication2020 28th Iranian Conference on Electrical Engineering, ICEE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728172965
ISBN (Print)9781728172972
Publication statusPublished - 26 Nov 2020
Externally publishedYes
Event28th Iranian Conference on Electrical Engineering - Tabriz, Iran, Islamic Republic of
Duration: 4 Aug 20206 Aug 2020
Conference number: 28

Publication series

NameIranian Conference on Electrical Engineering,
ISSN (Print)2164-7054
ISSN (Electronic)2642-9527


Conference28th Iranian Conference on Electrical Engineering
Abbreviated titleICEE 2020
Country/TerritoryIran, Islamic Republic of

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