Epileptic Seizure Classification Using Neural Networks With 14 Features



Epilepsy is one of the most frequent neurological disorders.
The main method used in epilepsy diagnosis is electroencephalogram
(EEG) signal analysis. However this method requires a time-consuming
analysis when made manually by an expert due to the length of EEG
recordings. This paper proposes an automatic classi cation system for
epilepsy based on neural networks and EEG signals. The neural networks
use 14 features (extracted from EEG) in order to classify the brain state
into one of four possible epileptic behaviors: inter-ictal, pre-ictal, ictal
and pos-ictal. Experiments were made in a (i) single patient (ii) di erent
patients and (ii) multiple patients, using two datasets. The classi cation
accuracies of 6 types of neuronal networks architectures are compared.
We concluded that with the 14 features and using the data of a single
patient results in a classi cation accuracy of 99% for the data patient,
while using a network trained for multiple patients an accuracy of 98%
is achieved.


Neural Networks, Epilepsy, Seizure Prediction, Data Mining,

Cited by

Year 2015 : 2 citations

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Abstract Research in seizure prediction has come a long way since its debut almost 4
decades ago. Early studies suffered methodological caveats leading to overoptimistic results
and lack of statistical significance. The publication of guidelines addressing mainly the ...

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Considerable efforts have been made in order to predict seizures. Among these methods,
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Year 2014 : 4 citations

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Abstract Epilepsy patients experience challenges in daily life due to precautions they have
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endanger the life of the patients or others, especially when they are using heavy ...

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Year 2012 : 3 citations

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Year 2011 : 2 citations

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N Moghim, D Corne - … (NaBIC), 2011 Third World Congress on, 2011 -