EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE

Simple item page
dc.contributor.authorSaminu, Sani
dc.contributor.authorXu, Guizhi
dc.contributor.authorZhang, Shuai
dc.contributor.authorAbd El Kader, Isselmou
dc.contributor.authorJabire, Adamu Halilu
dc.contributor.authorAhmed, Yusuf Kola
dc.contributor.authorKaraye, Ibrahim Abdullahi
dc.contributor.authorAhmad, Isah Salim
dc.date.accessioned2023-08-28T09:48:16Z
dc.date.available2023-08-28T09:48:16Z
dc.date.issued2023-06
dc.description.abstractObjective: 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.issn0219-5194
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11688
dc.language.isoenen_US
dc.publisherWorld Scientificen_US
dc.subjectEEG; time–frequency; SVM; SAE; DBN; MEWT; FBSEen_US
dc.titleEPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTUREen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
39J_SS_JMMB2023_Epileptic EEG signal Rhythm Analysis.pdf
Size:
2.97 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections