A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
Physica D
Computers in Biology and Medicine
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Epileptic Spike Recognition in Electroencephalogram Using Deterministic Finite Automata
Journal of Medical Systems
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Meta-rules and uncertain reasoning for diagnosis of epilepsy in childhood
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Parallel Algorithm to Analyze the Brain Signals: Application on Epileptic Spikes
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
DFAspike: A new computational proposition for efficient recognition of epileptic spike in EEG
Computers in Biology and Medicine
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
Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition
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
Computer Methods and Programs in Biomedicine
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Feature extraction and recognition of ictal EEG using EMD and SVM
Computers in Biology and Medicine
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
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
Computer Methods and Programs in Biomedicine
International Journal of Mobile Learning and Organisation
Hi-index | 12.06 |
In this study, a new scheme was presented for detecting epileptic seizures from electro-encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new scheme was based on approximate entropy (ApEn) and discrete wavelet transform (DWT) analysis of EEG signals. Seizure detection was accomplished in two stages. In the first stage, EEG signals were decomposed into approximation and detail coefficients using DWT. In the second stage, ApEn values of the approximation and detail coefficients were computed. Significant differences were found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with over 96% accuracy. Without DWT as preprocessing step, it was shown that the detection rate was reduced to 73%. The analysis results depicted that during seizure activity EEG had lower ApEn values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG. The data was further analyzed with surrogate data analysis methods to test for evidence of nonlinearities. It was shown that epileptic EEG had significant nonlinearity whereas normal EEG behaved similar to Gaussian linear stochastic process.