Abstract
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 language | English |
---|---|
Title of host publication | 2020 28th Iranian Conference on Electrical Engineering, ICEE 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 5 |
ISBN (Electronic) | 9781728172965 |
ISBN (Print) | 9781728172972 |
DOIs | |
Publication status | Published - 26 Nov 2020 |
Externally published | Yes |
Event | 28th Iranian Conference on Electrical Engineering - Tabriz, Iran, Islamic Republic of Duration: 4 Aug 2020 → 6 Aug 2020 Conference number: 28 |
Publication series
Name | Iranian Conference on Electrical Engineering, |
---|---|
Publisher | IEEE |
Volume | 2020 |
ISSN (Print) | 2164-7054 |
ISSN (Electronic) | 2642-9527 |
Conference
Conference | 28th Iranian Conference on Electrical Engineering |
---|---|
Abbreviated title | ICEE 2020 |
Country/Territory | Iran, Islamic Republic of |
City | Tabriz |
Period | 4/08/20 → 6/08/20 |