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Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection

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Abstract

In this paper, a comprehensive method using symmetric normal inverse Gaussian (NIG) parameters of the sub-bands of EEG signals calculated in the dual-tree complex wavelet transformation domain is proposed for classifying EEG data. The suitability of the NIG probability distribution function is illustrated using statistical measures. A support vector machine is employed as the classifier of the EEG signals, wherein the NIG parameters are used as features. The performance of the proposed method is studied using a publicly available benchmark EEG database for various classification cases that include healthy, inter-ictal (seizure-free interval) and ictal (seizure), non-seizure and seizure, healthy and seizure, and inter-ictal and ictal, and compared with that of several recent methods. It is shown that in almost all the cases, the proposed method can provide 100 % accuracy with 100 % sensitivity and 100 % specificity while being faster as compared to the time–frequency analysis-based and EMD techniques.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Anindya Bijoy Das & Mohammed Imamul Hassan Bhuiyan

  2. Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA

    S. M. Shafiul Alam

Authors
  1. Anindya Bijoy Das
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  2. Mohammed Imamul Hassan Bhuiyan
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  3. S. M. Shafiul Alam
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Correspondence to Anindya Bijoy Das.

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Das, A.B., Bhuiyan, M.I.H. & Alam, S.M.S. Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. SIViP 10, 259–266 (2016). https://doi.org/10.1007/s11760-014-0736-2

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