An approach to automated classification of epileptic seizures using Artificial Neural Network

Epileptic seizures are important public health issues as they affect 0.8% of humans. Electroencephalograph (EEG) records provide important understanding of epileptic disorders. The conventional method is to interpret by visual inspection of common patterns. This work deals with novel method of data generation using feature extraction and classification using Back Propagation Algorithm. Twelve patients' EEG is used for training and six patients' EEG for testing. Thus, the designed network classifies normal and types of abnormal EEG like focal, absence and tonic-clonic seizures. The network correctly classified normal and abnormal conditions. The performance of neural model has an accuracy of 96.3%.

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