EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE
dc.contributor.author | Saminu, Sani | |
dc.contributor.author | Xu, Guizhi | |
dc.contributor.author | Zhang, Shuai | |
dc.contributor.author | Abd El Kader, Isselmou | |
dc.contributor.author | Jabire, Adamu Halilu | |
dc.contributor.author | Ahmed, Yusuf Kola | |
dc.contributor.author | Karaye, Ibrahim Abdullahi | |
dc.contributor.author | Ahmad, Isah Salim | |
dc.date.accessioned | 2023-08-28T09:48:16Z | |
dc.date.available | 2023-08-28T09:48:16Z | |
dc.date.issued | 2023-06 | |
dc.description.abstract | Objective: Most studies in epileptic seizure detection and classi¯cation focused on classifying di®erent types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain's is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as e®ective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of ; ; ; and features were used to obtain a joint instantaneous frequency and amplitude components using a subband alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a signi¯cant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using -rhythms in channels 1 and 2. Signi¯cance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient's brain to increase the success of the surgery and reduce computational cost. | en_US |
dc.identifier.issn | 0219-5194 | |
dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/11688 | |
dc.language.iso | en | en_US |
dc.publisher | World Scientific | en_US |
dc.subject | EEG; time–frequency; SVM; SAE; DBN; MEWT; FBSE | en_US |
dc.title | EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE | en_US |
dc.type | Article | en_US |
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