APPLICATION OF INTRINSIC TIME-SCALE DECOMPOSITION (ITD) TO EEG SIGNALS FOR AUTOMATED SEIZURE PREDICTION

作者：Martis Roshan Joy^*; Acharya U Rajendra; Tan Jen Hong; Petznick Andrea; Tong Louis; Chua Chua Kuang; Ng Eddie Yin Kwee

来源：International Journal of Neural Systems, 2013, 23(5): 1350023.

DOI：10.1142/S0129065713500238

• Electroencephalogram (EEG)
• ictal
• interictal
• energy
• fractal dimension
• Hurst exponent
• sample entropy
• classifier

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.

• 出版日期2013-10