Abstract
This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.
Highlights
Epilepsy is a neurological disorder where abnormal or asynchronous neuronal activity of the brain, known as the symptoms of seizures, are present [1,2]
EEG signals are recorded from patients with epilepsy in order to determine the focal area of the epileptic seizures
Focus region localization has been investigated at the epileptic seizure by the application of machine learning methods to identify the seizure activity onset
Summary
Epilepsy is a neurological disorder where abnormal or asynchronous neuronal activity of the brain, known as the symptoms of seizures, are present [1,2]. Electroencephalogram (EEG) signals represent a crucial approach for evaluation of epileptic behavior [3]. One of the key issues for neurosurgery is the detection of early seizure discharge which helps in defining the brain area as the source of abnormal activity [3]. One of the aims of EEG recordings is to detect the brain areas where seizures begin and to estimate if the subject’s condition could be improved by neurosurgical removal of problematic brain areas. The localization of epileptic foci represents a very important stage for surgical treatment planning by detecting the earliest time of seizure onset in electroencephalographic (EEG) recordings
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