Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction.
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.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore. roshaniitsmst@gmail.com
Roshan Joy
RJ
Martis
U Rajendra
UR
Acharya
Jen Hong
JH
Tan
Andrea
A
Petznick
Louis
L
Tong
Chua Kuang
CK
Chua
Eddie Yin Kwee
EY
Ng
D003663:Decision Trees; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D017711:Nonlinear Dynamics; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted