Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection

By Samanwoy Ghosh-Dastidar, Hojjat Adeli, Nahid Dadmehr. IEEE Transactions on Biomedical Engineering, 2007

473 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b98b6ee27334013b7a3a0aa259c9b999aba4558e

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In Cognitive Neuroscience

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Citations in the corpus

Automated EEG analysis of epilepsy: A review (2013, background): “In another study by the same group [34], a good classification accuracy of 96.”
Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection (2008, methodology): “gration of the mixed-band wavelet-chaos methodology [10] and the new PCA-enhanced cosine RBFNN yields high EEG classification accuracy and is quite robust with respect to changes”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, unknown): “In the three class classification of epilepsy the following features are used: nonlinear features extracted from DWT coefficients [23,24], HOS cumulants extracted from Wavelet Packet Decomposition (WPD) coefficients [27], wavelet coefficients using WPT, and extracted eigenvalues from the resultant w”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “[37] introduce a mixed-band wavelet-chaos-neural network (NN) methodology for epilepsy diagnosis as well as epileptic seizure detection.”
A comparative study of wavelet families for EEG signal classification (2011, background): “The sole purpose of feature extraction, is to extract salient characteristics from digitized data collected from the data acquisition phase [3] followed by classification based on the extracted features [4] [5] [6] [7] [8].”
Improved spiking neural networks for EEG classification and epilepsy and seizure detection (2007, unknown): “Subsequently, this research was extended by the authors to the three-class EEG classiﬁcation problem [48].”