Decision support systems for time-varying biomedical signals: EEG signals classification

Authors:
Elif Derya íbeyli
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji íniversitesi, 06530 Söğütözü, Ankara, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 11
Cited 10

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Feature extraction from Doppler ultrasound signals for automated diagnostic systems

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A modified mixture of experts network structure for ECG beats classification with diverse features

Engineering Applications of Artificial Intelligence
Recurrent neural networks employing Lyapunov exponents for EEG signals classification

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A novel large-memory neural network as an aid in medical diagnosis applications

IEEE Transactions on Information Technology in Biomedicine
Input feature selection for classification problems

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Training feedforward networks with the Marquardt algorithm

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Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals

Neural Networks
Advances in automated diagnostic systems

NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Statistics over features: EEG signals analysis

Computers in Biology and Medicine
Applications in Bio-informatics and Biomedical Engineering

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals

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Lyapunov exponents/probabilistic neural networks for analysis of EEG signals

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Analysis of sleep EEG activity during hypopnoea episodes by least squares support vector machine employing AR coefficients

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Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

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Short Communication: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model

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Time-frequency distributions in the classification of epilepsy from EEG signals

Expert Systems with Applications: An International Journal

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Abstract

An integrated view of the automated diagnostic systems combined with spectral analysis techniques in the classification of electroencephalogram (EEG) signals is presented. The paper includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from the EEG signals. The major objective of the paper is to be a guide for the readers, who want to develop an automated diagnostic system for classification of the EEG signals. Toward achieving this objective, this paper presents the techniques which should be considered in developing automated diagnostic systems. The author suggests that the content of the paper will assist to the people in gaining a better understanding of the techniques in the classification of the EEG signals.