EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE
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Date
2023-06
Authors
Saminu, Sani
Xu, Guizhi
Zhang, Shuai
Abd El Kader, Isselmou
Jabire, Adamu Halilu
Ahmed, Yusuf Kola
Karaye, Ibrahim Abdullahi
Ahmad, Isah Salim
Journal Title
Journal ISSN
Volume Title
Publisher
World Scientific
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.
Description
Keywords
EEG; time–frequency; SVM; SAE; DBN; MEWT; FBSE