Publication Type : Conference Paper
Publisher : 2012 International Conference on Advances in Computing and Communications
Source : 2012 International Conference on Advances in Computing and Communications (2012)
Url : https://ieeexplore.ieee.org/document/6305563
Keywords : ANN, Artificial Neural Network, Artificial neural networks, brain disorders, Electroencephalogram (EEG), Electroencephalography, epilepsy, epilepsy centre database, epilepsy localization, epileptic EEG signal, epileptic spikes, Feature extraction, feed forward artificial neural network, Feedforward network, feedforward neural nets, Feedforward neural networks, Frequency domain analysis, frequency domain features, frequent electrochemical impulses, medical signal processing, mental function, neuronal activity, Neurons, Pattern classification, Pattern recognition, recurrent disturbances, seizure detection, Time domain features, transient disturbances
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2012
Abstract : Epilepsy is one of the important brain disorders, characterized by sudden recurrent and transient disturbances of mental function and movements of body, which is caused from excessive neuronal activity due to highly frequent electrochemical impulses from the neurons. This excessive discharge is shown in EEG as epileptic spikes which are complementary source of information in diagnosis and localization of epilepsy. Currently there are many techniques for the diagnosis and monitoring of epilepsy. Artificial Neural Networks (ANN) have proved to be an effective approach for a broad spectrum of applications for EEG signals because of its self-adaptation and natural way to organize and implement the redundancy. This paper proposes a neural-network-based automated epileptic EEG detection system that uses Feed forward Artificial Neural Network incorporating sliding window technique for pattern recognition. This work utilizes 100 single channel EEG signals obtained from the database of Epilepsy Centre in Bonn, Germany. The algorithm was trained with 50 datasets and tested for 25 normal data and 25 epileptic data sets. The performance of classification using Feed forward Artificial Neural Network gave a high success rate of 93.37% for distinguishing normal signals and 95.5% for epileptic signals.
Cite this Research Publication : Anusha K. S., Mathews, M. T., and Puthankattil, S. D., “Classification of Normal and Epileptic EEG Signal Using Time & Frequency Domain Features through Artificial Neural Network”, in 2012 International Conference on Advances in Computing and Communications, 2012.