Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function

10.1088/1361-6579/ac08e6

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Bibliographic Details
Main Authors:	Zhu, Zhaowei, Lan, Xiang, Zhao, Tingting, Guo, Yangming, Kojodjojo, Pipin, Xu, Zhuoyang, Liu, Zhuo, Liu, Siqi, Wang, Han, Sun, Xingzhi, Feng, Mengling
Other Authors:	SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
Format:	Article
Published:	IOP Publishing Ltd 2022
Subjects:	Deep neural network Ecg abnormalities Ecg signal Ensemble modeling Multi-label classification
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/232924
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-232924
record_format	dspace
spelling	sg-nus-scholar.10635-2329242024-04-17T09:53:06Z Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function Zhu, Zhaowei Lan, Xiang Zhao, Tingting Guo, Yangming Kojodjojo, Pipin Xu, Zhuoyang Liu, Zhuo Liu, Siqi Wang, Han Sun, Xingzhi Feng, Mengling SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH MEDICINE Deep neural network Ecg abnormalities Ecg signal Ensemble modeling Multi-label classification 10.1088/1361-6579/ac08e6 Physiological Measurement 42 6 065008 2022-10-13T01:19:13Z 2022-10-13T01:19:13Z 2021-06-01 Article Zhu, Zhaowei, Lan, Xiang, Zhao, Tingting, Guo, Yangming, Kojodjojo, Pipin, Xu, Zhuoyang, Liu, Zhuo, Liu, Siqi, Wang, Han, Sun, Xingzhi, Feng, Mengling (2021-06-01). Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function. Physiological Measurement 42 (6) : 065008. ScholarBank@NUS Repository. https://doi.org/10.1088/1361-6579/ac08e6 0967-3334 https://scholarbank.nus.edu.sg/handle/10635/232924 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ IOP Publishing Ltd Scopus OA2021
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Deep neural network Ecg abnormalities Ecg signal Ensemble modeling Multi-label classification
spellingShingle	Deep neural network Ecg abnormalities Ecg signal Ensemble modeling Multi-label classification Zhu, Zhaowei Lan, Xiang Zhao, Tingting Guo, Yangming Kojodjojo, Pipin Xu, Zhuoyang Liu, Zhuo Liu, Siqi Wang, Han Sun, Xingzhi Feng, Mengling Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function
description	10.1088/1361-6579/ac08e6
author2	SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
author_facet	SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH Zhu, Zhaowei Lan, Xiang Zhao, Tingting Guo, Yangming Kojodjojo, Pipin Xu, Zhuoyang Liu, Zhuo Liu, Siqi Wang, Han Sun, Xingzhi Feng, Mengling
format	Article
author	Zhu, Zhaowei Lan, Xiang Zhao, Tingting Guo, Yangming Kojodjojo, Pipin Xu, Zhuoyang Liu, Zhuo Liu, Siqi Wang, Han Sun, Xingzhi Feng, Mengling
author_sort	Zhu, Zhaowei
title	Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function
title_short	Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function
title_full	Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function
title_fullStr	Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function
title_full_unstemmed	Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function
title_sort	identification of 27 abnormalities from multi-lead ecg signals: an ensembled se_resnet framework with sign loss function
publisher	IOP Publishing Ltd
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/232924
_version_	1800915643597324288

Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function

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