U. Rajendra Acharya
Ngee Ann Polytechnic
730 Papers
1.7K Citations
U. Rajendra Acharya is an academic researcher from Ngee Ann Polytechnic. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 90, co-authored 570 publications. Previous affiliations of U. Rajendra Acharya include Kumamoto University & University of Southern Queensland.
Chat about Author
Papers
Automated identification of normal and diabetes heart rate signals using nonlinear measures
TL;DR: A non-invasive diagnosis support system for diabetes mellitus is proposed, which determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis.
151
Age-related Macular Degeneration detection using deep convolutional neural network
Jen Hong Tan,Sulatha V. Bhandary,Sobha Sivaprasad,Yuki Hagiwara,Akanksha Bagchi,U. Raghavendra,A. Krishna Rao,Biju Raju,Nitin S Shetty,Arkadiusz Gertych,Kuang Chua Chua,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +13 more
TL;DR: A fourteen-layer deep Convolutional Neural Network model is developed to automatically and accurately diagnose age-related Macular Degeneration at an early stage and is cost-effective and highly portable, hence, it can be utilized anywhere.
151
Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia
Rajamanickam Yuvaraj,Murugappan Murugappan,U. Rajendra Acharya,Hojjat Adeli,Norlinah Mohamed Ibrahim,Edgar Mesquita +5 more
TL;DR: Findings suggest that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas.
149
Iterative variational mode decomposition based automated detection of glaucoma using fundus images
Shishir Maheshwari,Ram Bilas Pachori,Vivek Kanhangad,Sulatha V. Bhandary,U. Rajendra Acharya +4 more
TL;DR: A novel method for an automated diagnosis of glaucoma using digital fundus images using Variational mode decomposition (VMD) method, which achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively is presented.
141
Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.
TL;DR: A deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data, using well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet.
140