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Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms

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Abstract

Surgery is recommended for epilepsy diagnosis in cases where patients do not respond well to anti-epilepsy medications. Successful surgery is essentially dependent on the area suffered from epilepsy, i.e., focal area. Electroencephalogram (EEG) signals are considered a powerful tool to identify focal or non-focal (normal) areas. In this work, we propose an automated method for focal and non-focal EEG signal identification, taking into account non-linear features derived from rhythms in the empirical wavelet transform (EWT) domain. The research paradigm is related to the decomposition of EEG signals into the delta, theta, alpha, beta, and gamma rhythms through the development of the EWT. Specifically, various non-linear features are extracted from rhythms composed of Stein’s unbiased risk estimation entropy, threshold entropy, centered correntropy, and information potential. From a statistical point of view, Kruskal–Wallis (KW) statistical test is then used to identify the significant features. The significant features obtained from the KW test are fed to support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. The SURE entropy provides an average classification accuracy of 93% and 82.6% for small and entire datasets by utilizing SVM and KNN classifiers with a tenfold cross-validation method, respectively. It is observed that the proposed method is better and competitive in comparison with other studies for small and large data, respectively. The obtained outcome concludes that the proposed framework could be used for people with epilepsy and can help the physicians to validate the assessment.

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Acknowledgements

Hesam Akbari and Muhammad Tariq Sadiq are co-first authors. This work is a part of the Final year Master’s thesis of Hesam Akbari. The authors are thankful to “Iranian Army Ground Forces” for providing access to research labs for calculating the features of the entire dataset.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Hesam Akbari

  2. School of Automation, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Muhammad Tariq Sadiq

  3. Department of Electrical Engineering, The University of Lahore, Lahore, 54000, Pakistan

    Muhammad Tariq Sadiq

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  1. Hesam Akbari
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  2. Muhammad Tariq Sadiq
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Correspondence to Muhammad Tariq Sadiq.

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Akbari, H., Sadiq, M.T. Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms. Phys Eng Sci Med 44, 157–171 (2021). https://doi.org/10.1007/s13246-020-00963-3

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  • DOI: https://doi.org/10.1007/s13246-020-00963-3

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