Multi-feature Characterization of Epileptic Activity for Construction of an Automated Internet-based Annotated Classification

Authors:
R. Arvind;B. Karthik;Natarajan Sriraam
Affiliations:
Department of Biomedical Engineering, SSN College of Engineering, Chennai, India 603110;Department of Biomedical Engineering, SSN College of Engineering, Chennai, India 603110;Department of Biomedical Engineering, Centre for Biomedical Informatics and Signal Processing, SSN College of Engineering, Chennai, India 603110
Venue:
Journal of Medical Systems
Year:
2012

Citing 11
Cited 0

Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Journal of Medical Systems
Wavelet/mixture of experts network structure for EEG signals classification

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Epileptic EEG detection using neural networks and post-classification

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A new approach for epileptic seizure detection using adaptive neural network

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Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

Expert Systems with Applications: An International Journal
Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks

Digital Signal Processing
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals

Expert Systems with Applications: An International Journal
Entropies for detection of epilepsy in EEG

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Entropies based detection of epileptic seizures with artificial neural network classifiers

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Epileptic seizure detection using dynamic wavelet network

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Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks

IEEE Transactions on Information Technology in Biomedicine

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Abstract

Continuous monitoring of EEG is essential for the neurologist to detect the epileptic seizures that occur at various intervals. Since large volume of data need to be analyzed, visual analysis has been proven to be time consuming and subsequently automated detection techniques have gained importance in the recent years. For the biomedical research community, the major challenge lies in providing a solution to neurologists in terms of diagnosis and EEG database management. This paper discusses the automated detection of epileptic seizure using frequency domain and entropy parameters which helps in the construction of epileptic database for handling EEG data. Experimental study indicates that the suggested mode of operation can be used for internet based framework which contains pure epileptic patterns in the server. This can be retrieved and analyzed for detection and annotation of epileptic spikes in extensive EEG recordings.