"A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands"
written by Hamed Vavadi, Ahmad Ayatollahi, Ahmad Mirzaei,
published by Journal of Biomedical Science and Engineering, Vol.3 No.12, 2010
has been cited by the following article(s):
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[15] Dalgacık Dönüşümü ve Ampirik Mod Ayrışımı Tabanlı Özelliklerin Epileptik Nöbet Algılama Performanslarının Karşılaştırılması
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[17] Comparison of Seizure Detection Performances of Features Based on Wavelet Transform and Empirical Mode Decomposition
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[18] A novel method of EEG data acquisition, feature extraction and feature space creation for early detection of epileptic seizures
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[19] Influences of the signal border extension in the discrete wavelet transform in EEG spike detection
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[20] 基于脑电信号的麻醉特征参数分析
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[21] A hybrid automated detection of epileptic seizures in EEG records
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[23] Characterization of EEG signals for identification of alcoholics using ANOVA ranked approximate entropy and classifiers
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[24] A high accuracy continuous wavelet function approximation for implantable device applications
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[25] Analysis of EEG Signals Related to Artists and Nonartists during Visual Perception, Mental Imagery, and Rest Using Approximate Entropy
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[26] Chaotic analysis of the human brain cortical model and robust control of epileptic seizures using sliding mode control
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[27] Anticipation des crises d'épilepsie temporale combinant des méthodes statistiques et non-linéaires d'analyse d'électroencéphalographie
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[28] EEG Subband Analysis using Approximate Entropy for the Detection of Epilepsy
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[30] Effects of subject's wakefulness state and health status on approximated entropy during eye opening and closure test of routine EEG examination
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[31] Automatic Seizure Detection Based on Wavelet-Chaos Methodology from EEG and its Sub-bands
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[32] An Efficient Classification of EEG Signals for Epilepsy based on Discrete Wavelet Transform and Approximate Entropy using Constrained Neyman-Pearson Criteria
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