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
Automated Carotid IMT Measurement and Its Validation in Low Contrast Ultrasound Database of 885 Patient Indian Population Epidemiological Study: Results of AtheroEdge® Software
Filippo Molinari,Kristen M. Meiburger,Luca Saba,U. Rajendra Acharya,U. Rajendra Acharya,Luca Famiglietti,Niki Georgiou,Andrew N. Nicolaides,Raja Sriswan Mamidi,Hannah Kuper,Jasjit S. Suri +10 more
TL;DR: CALEX 3.0 is the first technique, which has led to high accuracy and reproducibility on low-resolution images acquired during an epidemiological study from India and is proposed as a generalized framework for IMT measurement on large datasets.
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Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network
TL;DR: A deep learning model based on 1-dimensional convolutional neural network (1D-CNN) is proposed for CAP detection and homogenous 3-class sleep stages classification, namely wakefulness (W), rapid eye movement (REM) and NREM sleep.
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Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals
İrem Taşcı,Burak Taşçi,Prabal Datta Barua,Sengul Dogan,Turker Tuncer,Elizabeth Palmer,Hamido Fujita,U. Rajendra Acharya +7 more
TL;DR: In this paper , a new hypercube-based feature extractor has been proposed to generate two feature vectors in the feature extraction phase, and these selected features were fed to the k nearest neighbors (kNN) classifier with the leave one subject out (LOSO) cross-validation (CV) strategy.
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A novel machine learning framework for automated detection of arrhythmias in ECG segments
The-Hanh Pham,Vinitha Sree,John Mapes,Sumeet Dua,Oh Shu Lih,Joel E.W. Koh,Edward J. Ciaccio,U. Rajendra Acharya +7 more
TL;DR: Since the proposed predictive models work effectively in detecting arrhythmia in two or five second ECG segments rather than single ECG beats, they have better clinical adaptability and can be incorporated into clinical monitoring systems.
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Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS.
Krzysztof Rzecki,Tomasz Sośnicki,Mateusz Baran,Michał Niedźwiecki,Małgorzata Król,Tomasz Łojewski,U. Rajendra Acharya,Ozal Yildirim,Paweł Pławiak +8 more
TL;DR: This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes and demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.
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