Y
Yuki Hagiwara
Researcher at Ngee Ann Polytechnic
Publications - 57
Citations - 7515
Yuki Hagiwara is an academic researcher from Ngee Ann Polytechnic. The author has contributed to research in topics: Electroencephalography & Deep learning. The author has an hindex of 29, co-authored 56 publications receiving 4896 citations. Previous affiliations of Yuki Hagiwara include Bosch.
Papers
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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.
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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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Deep learning for healthcare applications based on physiological signals: A review.
TL;DR: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.
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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.
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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.