Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

Achmad Rizal,Wahmisari Priharti,Sugondo Hadiyoso

doi:10.3991/ijoe.v17i14.25889

Achmad Rizal, Wahmisari Priharti + Show 1 more

Open Access

https://doi.org/10.3991/ijoe.v17i14.25889

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Abstract

Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

Highlights

Electroencephalogram (EEG) signal recording is one of the main tools in diagnosing epilepsy [1]
At a resolution of 25-20-256, the signal is segmented on a window length of 25 samples and an overlap of 20, meaning that the window shift is only 5 data samples, so that the Short-Time Fourier Transform (STFT) plot looks very tight
The EEG signal is a non-stationary signal, so that the analysis on the time-frequency domain is the right one to use on the EEG signal

Summary

Introduction

Electroencephalogram (EEG) signal recording is one of the main tools in diagnosing epilepsy [1]. Observation of the EEG signal in cases of epilepsy is an essential part of detecting, analyzing, and determining treatment [2]. The localization of the epileptogenic zone can be analyzed using EEG [3]. The main task is to determine the seizure and non-seizure EEG signals, whereas the non-seizure signals include normal, pre-ictal, and inter-ictal. This type of EEG signal has a distinctive shape and rhythm that allows it to be visually observed. A system that is able to detect and evaluate the onset of seizures automatically is required [5]

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Results

Conclusion