Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically ex...

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Vydané v:	Frontiers in neuroscience Ročník 14; s. 618481
Hlavní autori:	Zhu, Xi, Xia, Wei, Bao, Zhuqing, Zhong, Yaohui, Fang, Yu, Yang, Fei, Gu, Xiaohua, Ye, Jing, Huang, Wennuo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 10.02.2021 Frontiers Media S.A
Predmet:	Accuracy Algorithms Artificial intelligence Behavior Cardiac arrhythmia Cardiovascular disease Cardiovascular diseases Cerebrovascular diseases continuity analysis Death detection of severe cardiovascular disease Echolocation Electrocardiography Fibrillation Heart attacks Medical imaging Morphology Mortality Neural coding Neural networks Neuroscience Plaques Predictions segmented dynamic Sparsity Ventricle video imaging Wavelet transforms segmented dynamic detection of severe cardiovascular disease video imaging continuity analysis artificial intelligence
ISSN:	1662-453X, 1662-4548, 1662-453X
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Abstract	In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.
AbstractList	In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four-layer stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats is with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 minutes before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data is then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 seconds. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases. In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases. In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.
Author	Xia, Wei Gu, Xiaohua Ye, Jing Zhu, Xi Zhong, Yaohui Huang, Wennuo Bao, Zhuqing Fang, Yu Yang, Fei
AuthorAffiliation	2 Department of Computer Science and Technology, Nanjing University , Nanjing , China 1 Clinical Medical College, Yangzhou University , Yangzhou , China
AuthorAffiliation_xml	– name: 2 Department of Computer Science and Technology, Nanjing University , Nanjing , China – name: 1 Clinical Medical College, Yangzhou University , Yangzhou , China
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Cites_doi	10.1007/s00138-018-00998-3 10.1073/pnas.1813476116 10.1080/10572252.2018.1528388 10.1504/ijbra.2020.10030363 10.2214/ajr.19.21258 10.1109/tip.2017.2725582 10.1002/ppul.23930 10.12785/ijcds/070401 10.1007/s13042-017-0678-4 10.1109/jstsp.2019.2955022 10.1007/s11045-017-0483-y 10.1089/tmj.2020.0006 10.1016/j.jcct.2017.11.004 10.1007/s13177-015-0112-9 10.1007/s10115-019-01337-2 10.1016/j.ipm.2018.01.010 10.1016/j.tics.2019.05.004 10.1148/rg.2016150223 10.1109/tmi.2019.2900031 10.2478/dim-2018-0014 10.1145/3131896 10.1145/3329119 10.1167/tvst.9.2.43 10.3233/ip-190128 10.1007/s10462-019-09719-2 10.1007/s12652-017-0511-7 10.1007/s10115-016-0987-z 10.1145/3214284 10.1145/3411832 10.1093/jcr/ucx104 10.1177/0954411919900720
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Copyright	Copyright © 2021 Zhu, Xia, Bao, Zhong, Fang, Yang, Gu, Ye and Huang. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2021 Zhu, Xia, Bao, Zhong, Fang, Yang, Gu, Ye and Huang. 2021 Zhu, Xia, Bao, Zhong, Fang, Yang, Gu, Ye and Huang
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Keywords	segmented dynamic detection of severe cardiovascular disease video imaging continuity analysis artificial intelligence
Language	English
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Snippet	In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring...
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StartPage	618481
SubjectTerms	Accuracy Algorithms Artificial intelligence Behavior Cardiac arrhythmia Cardiovascular disease Cardiovascular diseases Cerebrovascular diseases continuity analysis Death detection of severe cardiovascular disease Echolocation Electrocardiography Fibrillation Heart attacks Medical imaging Morphology Mortality Neural coding Neural networks Neuroscience Plaques Predictions segmented dynamic Sparsity Ventricle video imaging Wavelet transforms
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Title	Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease
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