EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

• Published in: IEEE access Vol. 8; pp. 10584 - 10605
• Main Authors: Alyasseri, Zaid Abdi Alkareem, Khader, Ahamad Tajudin, Al-Betar, Mohammed Azmi, Abasi, Ammar Kamal, Makhadmeh, Sharif Naser
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Comparative studies; Configuration management; Datasets; EEG; Electrocardiography; Electroencephalography; flower pollination algorithm; Genetic algorithms; Heuristic methods; Imagery; Mean square errors; Mean square values; metaheuristic algorithms; Noise reduction; optimization; Parameters; Particle swarm optimization; Root-mean-square errors; signal denoising; Signal processing; Signal to noise ratio; wavelet transform; Wavelet transforms

Background. The most common and successful technique for signal denoising with nonstationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of its control parameters which are often experim...