A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy

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

664 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/05f730c2986051ade88e39228cfda1fcbc4ac900

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, background): “In the literature, many studies are presented to analyze Alzheimer’s disease [1], attention-deficit/hyperactivity disorder (ADHD) [2], autism [3], autistic spectrum disorder [4], alcoholism [5], epilepsy [6,7], depth of anesthesia [8,9], etc.”
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection (2009, methodology): “An important discovery of that research is that a mixed-band feature space comprising the following nine parameters is effective for characterizing the underlying dynamics of the EEG accurately: the standard deviation (STD) computed from the band-limited EEG and alpha, beta, and gamma sub-bands; the”
Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection (2008, methodology): “[9], statistically significant differences were observed in the three groups based on the values of CD and”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “[34] presented a wavelet-chaos approach to analyze 6-seconds EEGs and the different subbands (delta, theta, alpha, beta, and gamma) of EEGs for the identification of seizure and epilepsy through integration of wavelets [35], a signal processing technique, and chaos theory from nonlinear science [36]”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, neutral): “[99] 2007 CAD of epilepsy Ghosh-Dastidar et al.”
New diagnostic EEG markers of the Alzheimer’s disease using visibility graph (2010, methodology): “Following Adeli et al. (2007), using a three-level wavelet decomposition, the band-limited EEG (0–30 Hz) is decomposed into four sub-bands: beta (15–30 Hz) ( b ), alpha (8–15 Hz) ( a ), theta (4–8 Hz) ( h ), and delta (0–4 Hz) ( d ).”