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Computer-Aided Diagnosis of Epilepsy Using Bispectrum of EEG Signals

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Application of Biomedical Engineering in Neuroscience

Abstract

This chapter aims to analyze the dynamics of brain activity from the electroencephalogram (EEG) signals and classify the seizures that are responsible for epilepsy. Hence, seizure classification is the primary task of this article. In this study, a nonlinear higher-order spectral method is proposed that significantly explore the underlying dynamics of nonstationary EEG signals. Various statistical parameters are measured from the principal region of the higher-order spectra that are subjected to the data reduction technique of the locality sensitive discriminant analysis (LSDA). The LSDA maps the measured features at higher dimensional space and ranks them according to the probability of discrimination. The ranked features are then used as inputs to the support vector machine (SVM) classifier with radial basis function kernel. The proposed algorithm is simulated on the web-available Bonn university database that achieved excellent seizures classification accuracy.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, UP, India

    Rahul Sharma & Pradip Sircar

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, MP, India

    Ram Bilas Pachori

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  1. Rahul Sharma
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  2. Pradip Sircar
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  3. Ram Bilas Pachori
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Correspondence to Rahul Sharma .

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  1. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India

    Sudip Paul

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Sharma, R., Sircar, P., Pachori, R.B. (2019). Computer-Aided Diagnosis of Epilepsy Using Bispectrum of EEG Signals. In: Paul, S. (eds) Application of Biomedical Engineering in Neuroscience. Springer, Singapore. https://doi.org/10.1007/978-981-13-7142-4_10

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