An automated system using intrinsic time-scale decomposition (ITD) features and a decision tree classifier achieved an average classification accuracy of 95.67%, sensitivity of 99%, and specificity of 99.5% for seizure prediction.
Does intrinsic time-scale decomposition (ITD) coupled with a decision tree classifier accurately classify normal, interictal, and ictal EEG signals?
The ITD-based automated seizure prediction system demonstrates high accuracy, sensitivity, and specificity for classifying EEG signals, suggesting potential for mass screening.
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.
Martis et al. (Tue,) conducted a other in Seizure. Intrinsic time-scale decomposition (ITD) based automated classification was evaluated on Classification accuracy. An automated system using intrinsic time-scale decomposition (ITD) features and a decision tree classifier achieved an average classification accuracy of 95.67%, sensitivity of 99%, and specificity of 99.5% for seizure prediction.
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