Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals
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Date
2022-05-11
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Focal and non-focal Electroencephalogram(EEG) signals have proved to be effective techniques
for identifying areas in the brain that are affected by epileptic seizures, known as the epileptogenic
zones. The detection of the location of focal EEG signals and the time of seizure occurrence are vital
information that help doctors treat focal epileptic seizures using a surgical method. This paper proposed
a computer-aided detection (CAD) system for detecting and classifying focal and non-focal EEG signals
as the manual process is time-consuming, prone to error, and tedious. The proposed technique employs
time-frequency features, statistical, and nonlinear approaches to form a robust features extraction
technique. Four detection and classification techniques for focal and non-focal EEG signals were
proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). Discrete
Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with
DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4). A newly proposed technique using
Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN). Prior to
feeding the features to classifiers, statistical analyses, including t-tests, were deployed to obtain relevant
and significant features at each approach. The proposed feature extraction technique and classification
proved effective and suitable for smart Internet of Medical Things (IoMT) devices as performance
parameters of accuracy, sensitivity, and specificity are higher than recently related works with a value of
99.7%, 99.5%, and 99.7% respectively.
Description
Keywords
EEG; DNN; deep CNN; WT-SST; DWT; SVM