Machine Learning
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
Journal of Medical Systems
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Estimating driving performance based on EEG spectrum analysis
EURASIP Journal on Applied Signal Processing
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Computer Methods and Programs in Biomedicine
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Approximately 1% of the world's population has epilepsy, and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. If an automatic seizure-detection system was available, it could reduce the time required by a neurologist to perform an off-line diagnosis by reviewing electroencephalogram (EEG) data. It could produce an on-line warning signal to alert healthcare professionals or to drive a treatment device such as an electrical stimulator to enhance the patient's safety and quality of life. This paper describes a systematic evaluation of current approaches to seizure detection in the literature. This evaluation was then used to suggest a reliable, practical epilepsy detection method. The combination of complexity analysis and spectrum analysis on an EEG can perform robust evaluations on the collected data. Principle component analysis (PCA) and genetic algorithms (GAs) were applied to various linear and nonlinear methods. The best linear models resulted from using all of the features without other processing. For the nonlinear models, applying PCA for feature reduction provided better results than applying GAs. The feasibility of executing the proposed methods on a personal computer for on-line processing was also demonstrated.