Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

TL;DR

This paper introduces a novel parallel genetic Naive Bayes classifier for epileptic seizure classification, achieving high accuracy by combining unique features and optimization techniques on EEG data.

Contribution

It proposes a new parallel classifier using genetic algorithms and feature combination for improved seizure type classification.

Findings

Third model achieves 85% accuracy.

Genetic algorithm improves feature selection.

Combining seizure types enhances classification performance.

Abstract

Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonic- clonic models. This study proposes a method that utilises unique features with a novel parallel classifier - Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).