Choo Min Lim

TL;DR: Five types of beats are automatically classified using HOS features (higher order cumulants) using two different approaches to detect cardiac abnormalities in ECG recordings and the developed system is ready clinically to run on large datasets.

TL;DR: An automatic screening system for the detection of normal and DR stages (NPDR and PDR) and can aid clinicians to make a faster DR diagnosis during the mass screening of normal/DR images is presented.

TL;DR: A comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum, showing that the selected HOS based features achieve 93.11% classification accuracy.

TL;DR: In this article, the authors studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia and performed an analysis of variance (ANOVA) test.

TL;DR: This work proposes a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform, Higher Order Spectra and textures, and observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results.