Detection of Epilepsy in Electroencephalographic (EEG) Signals Based on Global Wavelet Spectrum (GWS) Using Support Vector Machine (SVM)

Document Type : Original Article

Authors

Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

Approximately one percent of the world's population suffers from epilepsy. The first stage of epilepsy treatment is timely and correct diagnosis. One of the ways to diagnose epilepsy is to accurately analyze EEG signals. There are various features to diagnose the disease from a signal such as the signal amplitude. In this paper, a new method for the diagnosis of epilepsy is presented by examining the time-frequency information of the EEG signal in people with seizure-free seizure syndrome and healthy people. Initially, the Global Wavelet Spectrum (GWS) feature of the EEG signal was extracted. To interpret this Spectrum in frequency bands, EEG signals decompose to five levels by continuous wavelet transform. Then, by applying this feature, a Support vector machine-based classifier was used to diagnose epilepsy. The results of the analysis provided a significant difference in the separation of the individual based on the brain signal. The proposed method compared to the previous methods, can classify epilepsy and intact signals with 100% accuracy. It was also observed that the dominant (GWS) values for the signals selected from patients with epilepsy in the delta and theta frequency band are discussed.

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References

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