Efficient communication and EEG signal classification in wavelet domain for epilepsy patients
Autor: | Adel S. El-Fishawy, Taha E. Taha, Turky N. Alotaiby, Saleh A. Alshebeili, Ashraf A. M. Khalaf, Saly Abd-Elateif El-Gindy, Sami M. El-Dolil, Walid El-Shafai, Asmaa Hamad, Fathi E. Abd El-Samie |
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Rok vydání: | 2021 |
Předmět: |
General Computer Science
medicine.diagnostic_test Computer science business.industry 0206 medical engineering Wavelet transform Pattern recognition 02 engineering and technology Electroencephalography medicine.disease 020601 biomedical engineering Daubechies wavelet 03 medical and health sciences Epilepsy 0302 clinical medicine Wavelet Coiflet medicine Artificial intelligence Sensitivity (control systems) Entropy (energy dispersal) business 030217 neurology & neurosurgery |
Zdroj: | Journal of Ambient Intelligence and Humanized Computing. 12:9193-9208 |
ISSN: | 1868-5145 1868-5137 |
DOI: | 10.1007/s12652-020-02624-5 |
Popis: | 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. |
Databáze: | OpenAIRE |
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