Narrow Results

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.

As the world’s population continues to expand, scientists are working to address the energy needs and challenges that accompany growth with environmentally responsible approaches. Nanoscience is helping to provide solutions to energy and environmental concerns in a number of ways.

The current topic of clean air and water is often left incomplete. When we discuss cleaner cars or more regulations, we forget that none of this can genuinely be sustainable without improving our infrastructure, an objective we are mostly avoidant. With this sprouting age of technology, we have companies interject that their model is “the newest in sustainability.” Still, the systems that support production have fallen rapidly behind due to a lack of funding and push from the public. In this paper, I hope to begin a discussion on the importance of improving our energy infrastructure over the course of my lifetime. Eventually, a sprout of real change might happen to improve the lives of those around me.