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Efficient communication and EEG signal classification in wavelet domain for epilepsy patients

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Abstract

In this paper, we present an approach for the anticipation of electroencephalography (EEG) seizures using different families of wavelet transform. Different signal attributes are investigated to anticipate the seizure onset based on the wavelet transform. These attributes comprise amplitude, local mean, local median, local variance, derivative, and entropy of the wavelet-transformed signals. Different wavelet families are considered including Haar, Daubechies (db4, and db8), Symlets (Sym4), and Coiflets (Coif4) wavelets. The seizure prediction process is intended to be simple to be applied on a mobile application accompanying the patient to give him alerts of possible incoming seizures. The proposed approach is performed on long-term EEG recordings from the available CHB-MIT scalp dataset. It gives the best results in comparison with the other previous algorithms. It achieves a high sensitivity of 100% with Daubechies wavelet transform (db4) in addition to a low average False Prediction Rate (FPR) of 0.0818 h−1 and a high average Prediction Time (PT) of 38.1676 min. Therefore, it can help specialists for the prediction of epileptic seizures as early as possible.

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Acknowledgement

Dr Alshebeili wishes to acknowledge the support of King Saud University through the Researchers Supporting Project number (RSP-2020/46).

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Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Saly Abd-Elateif El-Gindy, Asmaa Hamad & Ashraf A. M. Khalaf

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Walid El-Shafai, Sami M. El-Dolil, Taha E. Taha, Adel S. El-Fishawy & Fathi E. Abd El-Samie

  3. KACST, Riyadh, 11442, Saudi Arabia

    Turky N. Alotaiby

  4. KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Riyadh, 11421, Saudi Arabia

    Saleh A. Alshebeili

  5. Department of Electrical Engineering, King Saud University, Riyadh, 11421, Saudi Arabia

    Saleh A. Alshebeili

  6. Department of Information Technology, College of Computer and Information sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Saly Abd-Elateif El-Gindy
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Correspondence to Saly Abd-Elateif El-Gindy.

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El-Gindy, S.AE., Hamad, A., El-Shafai, W. et al. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. J Ambient Intell Human Comput 12, 9193–9208 (2021). https://doi.org/10.1007/s12652-020-02624-5

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