Why the study?
Epileptic seizures provoked by sudden electrical discharges of brain cells could not be predicted, making automatic seizure detection and classification based on EEG signals essential.
An automated EEG classification system using empirical mode decomposition and autoregressive modeling with a neural network classifier accurately detects epileptic seizures.
May aid automated EEG seizure detection; leaves open prospective clinical validation before practice change.
Epilepsy is a neurological disorder affecting several millions of humans on earth. Epileptic seizures provoked in major cases by sudden electrical discharges of tremendous brain cells could not be predicted. Hence, automatic seizures detection and classification based on the analysis of electroencephalographic (EEG) signals becomes essential. The purpose of this paper is to propose a new feature extraction method using empirical mode decomposition (EMD) and a multilayer perceptron neural network (MLPNN). The EMD algorithm decomposes a time segment EEG into intrinsic mode functions (IMFs) on which autoregressive (AR) parameters are extracted, combined and fed to the MLPNN classifier. Experimental results carried out on a publicly available dataset, comprising normal, interictal and ictal EEG signals achieved classification accuracy up to 98 %. The outcome of this research is mainly intended to aid practioners in the diagnosis of epileptic portions in the EEG recordings.
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Djemili et al. (2019) studied this question.
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