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Epilepsy Seizure Detection Using Autoregressive Modelling and Multiple Layer Perceptron Neural Network
Current Issue
Volume 2, 2015
Issue 4 (July)
Pages: 26-31   |   Vol. 2, No. 4, July 2015   |   Follow on         
Paper in PDF Downloads: 38   Since Aug. 28, 2015 Views: 2277   Since Aug. 28, 2015
Authors
[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.
Abstract
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.
Keywords
Electroencephalogram (EEG) Signal, Linear Prediction Coding (LPC), Multiple Layer Perceptron (MLP), Epileptic Seizure Classification
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