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 detection of schizophrenia using nonlinear signal processing methods.
V. Jahmunah,Shu Lih Oh,Venkatesan Rajinikanth,Edward J. Ciaccio,Kang Hao Cheong,N. Arunkumar,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +8 more
TL;DR: An Automated Diagnostic Tool to investigate and classify the EEG signal patterns into normal and schizophrenia classes is developed and the experimental outcome showed that the SVM with Radial-Basis-Function (SVM-RBF) offered a superior average performance value on the considered EEG dataset, as compared to other classifiers implemented in this work.
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Cardiac decision making using higher order spectra
Roshan Joy Martis,U. Rajendra Acharya,U. Rajendra Acharya,K. M. Mandana,Ajoy Kumar Ray,Chandan Chakraborty +5 more
TL;DR: This work has analyzed five types of beats and obtained highest average accuracy, average sensitivity and specificity of 99.27% and 98.31% respectively using LS-SVM with Radial Basis Function (RBF) kernel and is clinically ready to run on large amount of data sets.
211
Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals
Turker Tuncer,Sengul Dogan,Paweł Pławiak,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: DWT coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection and the results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmmia detection using ECG signals.
209
An efficient compression of ECG signals using deep convolutional autoencoders
TL;DR: A new deep convolutional autoencoder (CAE) model for compressing ECG signals that can learn to use different ECG records automatically and allow secure data transfer in a low-dimensional form to remote medical centers is proposed.
206
Comprehensive electrocardiographic diagnosis based on deep learning.
Oh Shu Lih,V. Jahmunah,Tan Ru San,Edward J. Ciaccio,Toshitaka Yamakawa,Masayuki Tanabe,Makiko Kobayashi,Oliver Faust,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +10 more
TL;DR: The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification.
200