Nonlinear time series analysis
Nonlinear time series analysis
Discrimination of locally stationary time series using wavelets
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition
Research Letters in Signal Processing
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Expert Systems with Applications: An International Journal
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
Expert model for detection of epileptic activity in EEG signature
Expert Systems with Applications: An International Journal
Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Journal of Medical Systems
Auto mutual information analysis with order patterns for epileptic EEG
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Computers in Biology and Medicine
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Methodology for epileptic episode detection using complexity-based features
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Expert Systems with Applications: An International Journal
Possibilistic entropy: a new method for nonlinear dynamical analysis of biosignals
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition
Computer Methods and Programs in Biomedicine
A tunable support vector machine assembly classifier for epileptic seizure detection
Expert Systems with Applications: An International Journal
Discrete harmony search based expert model for epileptic seizure detection in electroencephalography
Expert Systems with Applications: An International Journal
Application of Higher Order Spectra to Identify Epileptic EEG
Journal of Medical Systems
Test-retest reliability and feature selection in physiological time series classification
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Time-frequency distributions in the classification of epilepsy from EEG signals
Expert Systems with Applications: An International Journal
Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis
Journal of Medical Systems
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Neural Network Approaches to Grade Adult Depression
Journal of Medical Systems
Possibilistic nonlinear dynamical analysis for pattern recognition
Pattern Recognition
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Seizure detection in clinical EEG based on entropies and EMD
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The aim of this work is to compare the different entropy estimators when applied to EEG data from normal and epileptic subjects. The results obtained indicate that entropy estimators can distinguish normal and epileptic EEG data with more than 95% confidence (using t-test). The classification ability of the entropy measures is tested using ANFIS classifier. The results are promising and a classification accuracy of about 90% is achieved.