General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

Author:

al-Qerem Ahmad12ORCID,Kharbat Faten3,Nashwan Shadi4ORCID,Ashraf Staish5,blaou khairi2

Affiliation:

1. Department of Computer Science, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

2. Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan

3. College of Engineering, Al Ain University, Abu Dhabi, UAE

4. Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia

5. Department of Computer Science, COMSATs University Islamabad, Islamabad, Pakistan

Abstract

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

Publisher

SAGE Publications

Subject

Computer Networks and Communications,General Engineering

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4. Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases;2024 2nd International Conference on Cyber Resilience (ICCR);2024-02-26

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