Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network
Author:
Affiliation:
1. University of Virginia,Department of Pediatrics
2. University of Virginia,Department of Engineering Systems and Environment
Funder
National Institutes of Health
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871878.pdf?arnumber=9871878
Reference27 articles.
1. Predicting Cardiopulmonary Response to Incremental Exercise Test
2. Wearable Patch-Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure
3. Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs
4. Development of novel maximal oxygen uptake prediction models for Turkish college students using machine learning and exercise data
Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A review of evaluation approaches for explainable AI with applications in cardiology;Artificial Intelligence Review;2024-08-09
2. Detection of cystic and alveolar echinococcosis based on tissue surface‐enhanced Raman spectroscopy combined with deep learning;Journal of Raman Spectroscopy;2024-05-28
3. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review;Cureus;2024-05-05
4. State of charge estimation for lithium-ion batteries based on data augmentation with generative adversarial network;Journal of Energy Storage;2024-03
5. Transforming Multidimensional Data into Images to Overcome the Curse of Dimensionality;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3