J
Jen Hong Tan
Researcher at Ngee Ann Polytechnic
Publications - 73
Citations - 7857
Jen Hong Tan is an academic researcher from Ngee Ann Polytechnic. The author has contributed to research in topics: Support vector machine & Mass screening. The author has an hindex of 36, co-authored 73 publications receiving 5434 citations. Previous affiliations of Jen Hong Tan include Nanyang Technological University & National Yang-Ming University.
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Journal ArticleDOI
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli +6 more
TL;DR: In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
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.
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Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Hamido Fujita,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam +7 more
TL;DR: A convolutional neural network algorithm is implemented for the automated detection of a normal and MI ECG beats (with noise and without noise) and can accurately detect the unknown ECG signals even with noise.
Journal ArticleDOI
Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
TL;DR: A convolutional neural network (CNN) technique to automatically detect the different ECG segments and can serve as an adjunct tool to assist clinicians in confirming their diagnosis is presented.
Journal ArticleDOI
Automated EEG-based screening of depression using deep convolutional neural network.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli,D. P. Subha +7 more
TL;DR: It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.