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An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis

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Abstract

Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells. Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy. In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method. The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using \(L^p\) norms computed from Fourier intrinsic band functions (FIBFs). The proposed scheme comprises three main sections. In the first section, the EEG signal is decomposed into a finite number of FIBFs. In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal–Wallis test. In the last stage, the significant features are passed on to the support vector machine (SVM) classifier. By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 99.96% and 99.94% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively. It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm.

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Authors and Affiliations

  1. Bennett University, Greater Noida, India

    Virender Kumar Mehla & Amit Singhal

  2. National Institute of Technology Hamirpur, Hamirpur, India

    Pushpendra Singh

  3. Indian Institute of Technology Indore, Indore, India

    Ram Bilas Pachori

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  1. Virender Kumar Mehla
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  2. Amit Singhal
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  3. Pushpendra Singh
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  4. Ram Bilas Pachori
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Correspondence to Amit Singhal.

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Mehla, V.K., Singhal, A., Singh, P. et al. An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis. Phys Eng Sci Med 44, 443–456 (2021). https://doi.org/10.1007/s13246-021-00995-3

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