Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers

Wail Mardini,Shadi Aljawarneh,Yaser Khamayseh,Muneer Masadeh Bani Yassein,Rana Al-Rawashdeh,Omar Meqdadi

doi:10.1109/access.2020.2970012

Wail Mardini, Shadi Aljawarneh + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.2970012

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 75	License type: CC BY 4.0

Affiliation: Jordan University of Science and Technology

Abstract

Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54-DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Naive Bayes (NB). The performance of 14 different combinations of two-class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54-DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers.

Highlights

Epilepsy is considered as one of the most severe neurological disorders that affect humans’ life
The results presented here represent the accuracy, specificity, and sensitivity of 14 dataset ’combinations, these results represent the evaluation metrics, that are generated after applying the genetic algorithm
We propose a novel approach to diagnosis the EEG signals using Multi-discrete wavelet transform (DWT), and Genetic algorithm coupled with four classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Naive Bayes

Summary

Introduction

Epilepsy is considered as one of the most severe neurological disorders that affect humans’ life. Epilepsy can be identified by analyzing the patterns of Electroencephalogram (EEG) signals, which is a popular technique that is used to determine the abnormality of the brain. EEG signals are widely used by medical doctors and researchers to study epilepsy [1]. Epileptic seizures cause abnormal changes in the brain; the detection of unpredictable epileptic seizures is implemented traditionally by expert clinicians. The experts usually rely on visual observation of the EEG signals for detecting abnormalities. This process is typically timeconsuming and prone to human errors. Automatic diagnosis of epileptic seizures is essential in the clinical environment, and there is a need for improving the automated

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