APPLICATION OF INTRINSIC TIME-SCALE DECOMPOSITION (ITD) TO EEG SIGNALS FOR AUTOMATED SEIZURE PREDICTION
- 7 August 2013
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Neural Systems
- Vol. 23 (5), 1350023
- https://doi.org/10.1142/s0129065713500238
Abstract
Intrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.Keywords
This publication has 64 references indexed in Scilit:
- Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector RegressionComputer-Aided Civil and Infrastructure Engineering, 2012
- Wavelet Coherence Model for Diagnosis of Alzheimer DiseaseClinical Eeg and Neuroscience, 2012
- Incidence of epilepsyNeurology, 2011
- Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s diseaseClinical Neurophysiology, 2011
- Fractality and a Wavelet-Chaos-Neural Network Methodology for EEG-Based Diagnosis of Autistic Spectrum DisorderJournal Of Clinical Neurophysiology, 2010
- New diagnostic EEG markers of the Alzheimer’s disease using visibility graphJournal of Neural Transmission, 2010
- Advances in Quantitative Electroencephalogram Analysis MethodsAnnual Review of Biomedical Engineering, 2004
- Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain statePhysical Review E, 2001
- Data compression by linear prediction for storage and transmission of EEG signalsInternational Journal of Bio-Medical Computing, 1994
- High-Frequency EEG Activity at the Start of SeizuresJournal Of Clinical Neurophysiology, 1992