Zobrazeno 1 - 10 of 22 pro vyhledávání: '"Zhengling He"'
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Akademický článek
Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning
Autor: Zhengling He, Huajun Zhang, Xianxiang Chen, Junshan Shi, Lu Bai, Zhen Fang, Rong Wang
Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques
Externí odkaz: https://doaj.org/article/632ed4b56a4f459f9eb717144394682f
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2
Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records
Autor: Pengfei Zhang, Rongjian Zhao, Xianxiang Chen, Lidong Du, Zhengling He, Zhen Fang
Publikováno v: Critical Care Medicine. 48:e1337-e1342
OBJECTIVES Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sep
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd45ea395f64b08aa344f86ccb6486e3
https://doi.org/10.1097/ccm.0000000000004644
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3
Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation
Autor: Zhengling He, Lidong Du, Peng Wang, Pan Xia, Zhe Liu, Yuanlin Song, Xianxiang Chen, Zhen Fang
Publikováno v: Computers in biology and medicine. 149
Automatic sleep stage classification is an effective technology compared to conventional artificial visual inspection in the field of sleep staging. Numerous algorithms based on machine learning and deep learning on single-channel electroencephalogra
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::631de09cf6b00d547460874da051f63d
https://pubmed.ncbi.nlm.nih.gov/36084381
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4
A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs
Autor: Pan Xia, Zhengling He, Zhongrui Bai, Yuqi Wang, Xianya Yu, Fanglin Geng, Lidong Du, Xianxiang Chen, Peng Wang, Yusi Zhu, Mingyan Du, Zhen Fang
Publikováno v: Physiological measurement. 43(10)
Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to deve
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a25deb3eb2ba773ca9f5cab4f51caa8
https://pubmed.ncbi.nlm.nih.gov/36336789
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8
A Novel Convolutional Neural Network for Arrhythmia Detection From 12-lead Electrocardiograms
Autor: Lirui Xu, Hao Zhang, Xianxiang Chen, Pengfei Zhang, Zhengling He, Weisong Li, Zhongrui Bai, Pan Xia
Publikováno v: CinC
Electrocardiogram (ECG) is a widely medical tool used in the clinical diagnosis of arrhythmia, numerous algorithms based on deep learning have been proposed to achieve automatic arrhythmia detection. In PhysioNetlComputing in Cardiology Challenge 202
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::48e583f542a070a5795cb7a5501d26e5
https://doi.org/10.22489/cinc.2020.196
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9
Machine learning for the early prediction of head-up tilt testing outcome
Autor: Xianxiang Chen, Jiexin Liu, Song Du, Bin Wu, Zhen Fang, Zhengling He, Zhiqi Fan, Binmu Xin, Lidong Du
Publikováno v: Biomedical Signal Processing and Control. 69:102904
Objective Head-up Tilt Testing (HUTT) is a widely used medical tool for the diagnosis of unexplained syncope. Current HUTT protocols, however, are time-consuming. This study aims to investigate the feasibility of using hemodynamic monitoring and mach
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ca2c3b08c626b0d385adeab5f865c3eb
https://doi.org/10.1016/j.bspc.2021.102904
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