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.
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Papers
Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference
Myagmarbayar Nergui,Jun Inoue,Murai Chieko,Wenwei Yu,U. Rajendra Acharya,U. Rajendra Acharya +5 more
- 01 Jan 2014
TL;DR: A Bayesian Network model was applied to data collected in the stance phase of walking and showed that BNs extracted could express the underlying mechanism of foot function.
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Recurrence quantification analysis of body response to functional electrical stimulation on hemiplegic subjects
TL;DR: Lower values were observed for most of the RQA parameters with FES than obtained without FES, which confirmed the fact that FES is very useful in bringing more order, rhythm and better control in the physical activities of hemiplegic people.
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Carotid IMT variability (IMTV): Its design and validation in symptomatic vs. asymptomatic 142 Italian population
Filippo Molinari,Kristen M. Meiburger,Luca Saba,Giuseppe Ledda,Michele Anzidei,U. Rajendra Acharya,Guang Zeng,Shoaib Shafique,Andrew N. Nicolaides,Jasjit S. Suri +9 more
- 01 Jan 2012
TL;DR: Results showed that the symptomatic subjects had comparable IMT with respect to asymptomatic subjects, but a higher IMTV value.
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Development of an automated system for the detection of genotype in polypoidal choroidal vasculopathy using retinal image phenotype.
Lakshmi Priyankka Alagappan,Lakshmi Priyankka Alagappan,Joel En Wei Koh,Jahmunah,Adhithi Ramesh,Muna Bhende,Rajiv Raman,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Sinnakaruppan Mathavan +10 more
TL;DR: Based on the analysis, it may be possible to predict the genotype and disease status of PCV patients using fundus images in assistance with a machine learning algorithm.
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