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Automated Identification System for Focal EEG Signals Using Fractal Dimension of FAWT-Based Sub-bands Signals

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Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

Abstract

The classification of focal and non-focal electroencephalogram (EEG) signals for diagnosis of epilepsy at an early stage is one of the most difficult problems. There have been many attempts to develop automated detection algorithms to assist clinical research for presurgical analysis of epilepsy. In this paper, a novel approach for studying EEG signals has been proposed using flexible analytic wavelet transform (FAWT) which is a nonstationary signal processing technique. In this study, EEG signals are decomposed into the desired number of sub-bands (SBs). Fractal dimension (FD) is used as a feature and then computed it for all SB signals which are obtained from FAWT. The significant features obtained from the Kruskal–Wallis statistical test and are classified using robust energy-based least square twin support vector machine (RELS-TSVM). In order to show the effectiveness of the proposed method for classification of focal (F) and non-focal (NF) EEG signals, publicly available database termed as Bern-Barcelona EEG dataset is used for the study.

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Acknowledgements

This work was supported by Science and Engineering Research Board (SERB) as Early Career Research Award grant no. ECR/2017/000053. We are thankful to National Board for Higher Mathematics (NBHM) for fellowship to Mamta Dalal. We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support.

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Authors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India

    M. Dalal & M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, India

    Ram Bilas Pachori

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  1. M. Dalal
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  2. M. Tanveer
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  3. Ram Bilas Pachori
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Correspondence to M. Dalal .

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Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

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Dalal, M., Tanveer, M., Pachori, R.B. (2019). Automated Identification System for Focal EEG Signals Using Fractal Dimension of FAWT-Based Sub-bands Signals. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_50

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