R
Ru San Tan
Researcher at National University of Singapore
Publications - 363
Citations - 13577
Ru San Tan is an academic researcher from National University of Singapore. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 42, co-authored 301 publications receiving 9470 citations. Previous affiliations of Ru San Tan include Canberra Hospital & Singapore General Hospital.
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Journal ArticleDOI
Apixaban in Patients with Atrial Fibrillation
Stuart J. Connolly,John W. Eikelboom,Campbell D. Joyner,Hans-Christoph Diener,Robert G. Hart,Sergey P. Golitsyn,Greg C. Flaker,Alvaro Avezum,Stefan H. Hohnloser,Rafael Diaz,Mario Talajic,Jun Zhu,Prem Pais,Andrzej Budaj,Alexander Parkhomenko,Petr Jansky,Patrick J. Commerford,Ru San Tan,Kui-Hian Sim,Basil S. Lewis,Walter Van Mieghem,Jae Hyung Kim,Fernando Lanas-Zanetti,Antonio Gonzalez-Hermosillo,Antonio L. Dans,Muhammad Munawar,John H. Lawrence,Gayle Lewis,Rizwan Afzal,Salim Yusuf +29 more
TL;DR: In patients with atrial fibrillation for whom vitamin K antagonist therapy was unsuitable, apixaban reduced the risk of stroke or systemic embolism without significantly increasing therisk of major bleeding or intracranial hemorrhage.
Journal ArticleDOI
A deep convolutional neural network model to classify heartbeats
U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam,Arkadiusz Gertych,Ru San Tan +6 more
TL;DR: A 9-layer deep convolutional neural network (CNN) is developed to automatically identify 5 different categories of heartbeats in ECG signals to serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmicheartbeats.
Journal ArticleDOI
Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).
Journal ArticleDOI
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
TL;DR: An automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branches block (RBBB) and atrial premature beats (APB), and premature ventricular contraction (PVC) on ECG signals.
Journal ArticleDOI
Prevalence and correlates of coronary microvascular dysfunction in heart failure with preserved ejection fraction: PROMIS-HFpEF.
Sanjiv J. Shah,Carolyn S.P. Lam,Sara Svedlund,Antti Saraste,Camilla Hage,Ru San Tan,Lauren Beussink-Nelson,Ulrika Ljung Faxén,Maria Lagerstrom Fermer,Malin A. Broberg,Li-Ming Gan,Li-Ming Gan,Li-Ming Gan,Lars H. Lund +13 more
TL;DR: PROMIS-HFpEF is the first prospective multi-centre, multinational study to demonstrate a high prevalence of CMD in HFpEF in the absence of unrevascularized macrovascular CAD, and to show its association with systemic endothelial dysfunction (RHI, UACR as well as markers of HF severity (NTproBNP and RV dysfunction).