Epilepsy Seizure Detection Using Autoregressive Modelling and Multiple Layer Perceptron Neural Network
[1]
Abdelhaq Ouelli, Sustainable Development Laboratory, University Sultan Moulay Slimane, Beni Mellal, Morocco.
[2]
Benachir Elhadadi, Sustainable Development Laboratory, University Sultan Moulay Slimane, Beni Mellal, Morocco.
[3]
Hicham Aissaoui, Sustainable Development Laboratory, University Sultan Moulay Slimane, Beni Mellal, Morocco.
[4]
Belaid Bouikhalene, Sustainable Development Laboratory, University Sultan Moulay Slimane, Beni Mellal, Morocco.
In this paper, we present a new method for epilepsy seizure detection based on autoregressive modelling. The method, termed linear prediction coding (LPC), is used to model ictal and seizure-free EEG signals. It is found that the modeling error energy is substantially higher for ictal EEG signals compared to seizure-free EEG signals. Moreover, it is known that ictal EEG signals have higher energy than seizure-free EEG signals. These two parameters are then given as inputs to train a Multiple Layer Perceptron (MLP). The trained MLP is then used to classify a set of EEG signals into ictal and seizure-free categories. It is found that the proposed method gives a classification accuracy of 94.67% when the MLP is trained with the Levenberg–Marquardt (LM) algorithm.
Electroencephalogram (EEG) Signal, Linear Prediction Coding (LPC), Multiple Layer Perceptron (MLP), Epileptic Seizure Classification
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