Novel Epileptic Seizure Detection Techniques and their Empirical Analysis

TL;DR

This paper proposes a novel framework for epileptic seizure detection using EEG signals, combining wavelet transforms, dimensionality reduction, feature fusion, and machine learning classifiers, achieving near-perfect accuracy on the Bonn dataset.

Contribution

It introduces a new combination of feature extraction, reduction, and classification techniques that significantly improve seizure detection accuracy.

Findings

Achieved 100% accuracy with LDA and Naive Bayes classifiers.

Demonstrated the effectiveness of PCA, ICA, and LDA in feature reduction.

Outperformed other classifier combinations in seizure detection accuracy.

Abstract

An Electroencephalogram (EEG) is a non-invasive exam that records the brain's electrical activity. This is used to help diagnose conditions such as different brain problems. EEG signals are taken for epilepsy detection, and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, machine learning classifiers and statistical features are mainly used. The hidden information in the EEG signal helps detect diseases affecting the brain. Sometimes it is complicated to identify the minimum changes in the EEG in the time and frequency domain's purpose. The DWT can give a suitable decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step, three different classifiers, Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest-Neighbor (KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset. The simulation results provide 100% accuracy for the LDA and NB combination outperforming accuracy with other classifiers combinations, including 89.17% for LDA and SVM, 80.42% for LDA and KNN, 89.92% for PCA and NB, 85.58% PCA and SVM, 80.42% PCA and KNN, 82.33% for ICA and NB, 90.42% for ICA and SVM, 90% for ICA and KNN. Also, the LDA and NB combination shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. The results prove the effectiveness of this model.