S. Vinitha Sree

TL;DR: This review discusses various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail, and briefly presents the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting.

TL;DR: This work proposes a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals and shows that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%.

TL;DR: The technique can be easily written as a software application and used by medical professionals without any extensive training and cost and can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

TL;DR: A novel method for glaucoma detection using a combination of texture and higher order spectra (HOS) features from digital fundus images is presented and it is demonstrated that the texture and HOS features after z-score normalization and feature selection, and when combined with a random-forest classifier, performs better than the other classifiers.

TL;DR: This work uses the recorded EEG signals in Recurrence Plots (RP), and extracts Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes.