Neural network design
Computers in Biology and Medicine
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
Artificial Intelligence in Medicine
Nearest neighbor estimate of conditional mutual information in feature selection
Expert Systems with Applications: An International Journal
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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 classification 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) different patients and (ii) multiple patients, using two datasets. The classification accuracies of 6 types of neural networks architectures are compared. We concluded that with the 14 features and using the data of a single patient results in a classification accuracy of 99%, while using a network trained for multiple patients an accuracy of 98% is achieved.