Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Action-Aware Multimodal Wavelet Fusion Network for Quantitative Elbow Motor Function Assessment Using sEMG and Robotic Kinematics.

Sensors (Basel, Switzerland)·2026
Same author

ECG-AuxNet: A Dual-Branch Spatial-Temporal Feature Fusion Framework with Auxiliary Learning for Enhanced Cardiac Disease Diagnosis.

IEEE journal of biomedical and health informatics·2026
Same author

The role of executive function for differentiating symptoms of ADHD in preschoolers.

BMC pediatrics·2026
Same author

VAM: A Parallel Cross-Modal Hybrid Network for Accurate and Interpretable Vascular Age Estimation from PPG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Unleashing the Power of Pretrained Transformer for Dense Prediction in Physiological Signals.

IEEE journal of biomedical and health informatics·2025
Same author

Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios.

Nature communications·2025
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 4, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K

一个基于无监督学习和自适应特征转移的多模块心跳分类算法.

Yanan Wang1, Shuaicong Hu1, Jian Liu1

  • 1Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.

Computers in biology and medicine
|February 1, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于心跳分类的新深度学习方法,通过使用无监督学习和自适应转移来克服有限的数据,以弥合领域差异. 该算法在分类心跳时实现了96.7%的准确性.

关键词:
适应性特征转移适应性特征的转移有注释的数据稀缺性.域名不一致性 域名不一致性心跳的分类心跳的分类没有监督的学习学习.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

727
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

相关实验视频

Last Updated: Jul 4, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

727
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

科学领域:

  • 人工智能的人工智能
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 对于心跳分类的深度学习面临挑战,因为注释数据稀缺.
  • 现有的转移学习 (TL) 方法经常忽略源 (SD) 和目标 (TD) 数据库之间的域分布差异.
  • 在SD和TD数据库之间不一致的任务进一步复杂化了有效的转移学习.

研究的目的:

  • 为了应对心跳分类模型中有限的标记数据的挑战.
  • 开发一种有效的方法来消除SD和TD数据库之间的域差异.
  • 通过调整来自不同任务领域的特征来提高心跳分类性能.

主要方法:

  • 提出了一个多模块的心跳分类算法,利用无监督的特征提取器.
  • 引入了一种新的自适应转移方法,以消除预培训 (PTF-SD) 和微调 (FTF-TD) 功能之间的域差异.
  • 雇员无监督学习和适应性特征转移用于模型预培训和微调.

主要成果:

  • 在将心跳分为正常心跳,心室外心跳 (SVEB) 和心室外心跳 (VEB) 的整体准确率达到了96.7%.
  • 对于SVEBs的表现很高,灵敏度 (Sen) 为0.802,正预测值 (PPV) 为0.701,F1得分为0.748.
  • 展示了VEB的优秀结果,其中Sen为0.976,PPV为0.840,F1得分为0.903.

结论:

  • 拟议的多模块算法有效地减轻了心跳分类中的标记数据稀缺性.
  • 无监督学习和适应性特征转移是成功跨领域心跳分类的关键组成部分.
  • 该方法表现出强大的性能,即使源域和目标域来自不一致的任务.