Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Epileptic seizure detection: A nonlinear viewpoint
Computer Methods and Programs in Biomedicine
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
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
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Seizure detection in clinical EEG based on multi-feature integration and SVM
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Hi-index | 12.06 |
The electroencephalogram (EEG) has proven a valuable tool in the study and detection of epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a feature for automated epileptic seizure detection. A Support Vector Machine (SVM) is used to classify segments of normal and epileptic EEG based on PE values. The proposed system utilizes the fact that the EEG during epileptic seizures is characterized by lower PE than normal EEG. It is shown that average sensitivity of 94.38% and average specificity of 93.23% is obtained by using PE as a feature to characterize epileptic and seizure-free EEG, while 100% sensitivity and specificity were also obtained in single-trial classifications.