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
Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank
TL;DR: The performance of the model indicates that the proposed technique is able to identify the abnormal knee-Joint reliably and thus this technique can aid the orthopedic surgeons in the early diagnosis of knee-joint abnormalities.
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Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters
TL;DR: An efficient, effective and robust automated system to detect shockable and non-shockable arrhythmia using an optimal wavelet-based features extracted from ECG epochs of 2 s durations is devised and can be integrated in automated external defibrillators that can be deployed for hospitals as well as out-of-hospital emergency resuscitation of SCD.
Model uncertainty quantification for diagnosis of each main coronary artery stenosis
Roohallah Alizadehsani,Mohamad Roshanzamir,Moloud Abdar,Adham Beykikhoshk,Mohammad Hossein Zangooei,Abbas Khosravi,Saeid Nahavandi,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +10 more
- 29 May 2020
TL;DR: High diagnostic performance of the proposed method for diagnosis of stenosis in individual RCA, LCX, and LAD is demonstrated, achieving accuracy rates of 82.67%, 83.67% and 86.43%, respectively, which is the best performance of ML techniques applied to the Z-Alizadeh Sani dataset.
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Automatic detection of ischemic stroke using higher order spectra features in brain MRI images
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Kristen M. Meiburger,Oliver Faust,Joel En Wei Koh,Shu Lih Oh,Edward J. Ciaccio,Asit Subudhi,V. Jahmunah,Sukanta Sabut +10 more
TL;DR: The proposed system is based upon the extraction of higher order bispectrum entropy and its phase features from brain MRI (Magnetic Resonance Imaging) images and is efficacious for delivering decision support in the diagnosis of ischemic stroke severity, thereby aiding the neuroradiologist in routine screening procedures.
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Atheromatic™: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture
U. Rajendra Acharya,Oliver Faust,S. Vinitha Sree,Ang Peng Chuan Alvin,Ganapathy Krishnamurthi,Josae C. R. Seabra,Joao Sanches,Jasjit S. Suri +7 more
- 01 Dec 2011
TL;DR: A computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic carotid ultrasound images is presented, and an integrated index, a unique number called symptomatic asymptonomaticCarotid index (SACI) is proposed.
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