Abstract
This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different groups of data according to similarities or dissimilarities among the patterns. In the proposed methodology, we use K-means clustering for partitioning each category EEG data set (e.g. healthy; epileptic seizure) into several clusters and then extract some representative characteristics from each cluster. Subsequently, we integrate all the features from all the clusters in one feature set and then evaluate that feature set by three well-known machine learning methods: Support Vector Machine (SVM), Naive bayes and Logistic regression. The proposed method is tested by a publicly available benchmark database: ‘Epileptic EEG database’. The experimental results show that the proposed scheme with SVM classifier yields overall accuracy of 100% for classifying healthy vs epileptic seizure signals and outperforms all the recent reported existing methods in the literature. The major finding of this research is that the proposed K-means clustering based approach has an ability to efficiently handle EEG data for the detection of epileptic seizure.
Highlights
Epilepsy, the most common and devastating neurological diseases worldwide, is characterised by recurrent seizures [1,36]
From the above discussion, it can be conclude that the Support Vector Machine (SVM) classifier with the proposed feature set is well suited for the classification of Set A and E while the Logistic Regression classifier is more appropriate for the other cases of binary EEG signals classification
Accurate and automatic classification of epileptic seizure through EEG signals is a complex problem as it requires the analysis of vast amount of EEG data
Summary
The most common and devastating neurological diseases worldwide, is characterised by recurrent seizures [1,36]. EEG recordings generally produce a huge amount of multichannel EEG signal data which are very complex in nature such as, non-stationarity, chaotic and aperiodic [34, 35]. Until now, these data are mainly visually analysed by experts or clinicians to identify and understand abnormalities within the brain and how they propagate. In order to find traces of epilepsy, visual marking of EEG recordings by human experts is not a satisfactory procedure for a reliable diagnosis and interpretation as such analysis is time-consuming, costly, onerous, subject to error and bias. One challenge in the current biomedical research is how to classify time-varying EEG signals automatically and as accurately as possible for assisting the diagnosis of epileptic seizure
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