Epileptic Seizure Prediction and the Dimensionality Reduction Problem
- Cite this paper as:
- Ventura A., Franco J.M., Ramos J.P., Direito B., Dourado A. (2009) Epileptic Seizure Prediction and the Dimensionality Reduction Problem. In: Alippi C., Polycarpou M., Panayiotou C., Ellinas G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg
Seizures prediction may substantially improve the quality of life of epileptic patients. Processing EEG signals, by extracting a convenient set of features, is the most promising way to classify the brain state and to predict with some antecedence its evolution to a seizure condition. In this work neural networks are proposed as effective classifiers of brain state among 4 classes: interictal, preictal, ictal and postictal. A two channels set of 26 features is extracted. By correlation analysis and by extracting the principal components, a reduced features space is obtained where, by an appropriate neural network, over 90% successful classifications are achieved, for dataset with several patients from the Freiburg database.
KeywordsClassification Neural Networks Feature Selection PCA Correlation Epilepsy Seizure Prediction EEG Processing
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