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

Amyloid Fibrils03:03

Amyloid Fibrils

11.9K
Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining,...
11.9K
Amyloid Fibrils03:03

Amyloid Fibrils

6.4K
6.4K
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

551
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
551
Statistical Significance01:50

Statistical Significance

21.3K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
21.3K
The Tree of Life - Bacteria, Archaea, Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, Eukaryotes

38.4K
The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both...
38.4K
Survival Tree01:19

Survival Tree

423
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
423

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

Investigating Structurally and Pigmentary Colored Featherworks via Noninvasive Methodologies.

ACS omega·2026
Same author

Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.

Npj mental health research·2026
Same author

Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.

Journal of imaging informatics in medicine·2025
Same author

Comprehensive Optoelectronic Study of Copper Nitride: Dielectric Function and Bandgap Energies.

Nanomaterials (Basel, Switzerland)·2025
Same author

Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning.

IEEE access : practical innovations, open solutions·2025
Same author

ScarNet: a novel foundation model for automated myocardial scar quantification from late gadolinium-enhancement images.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2025

相关实验视频

Updated: Jan 31, 2026

Optimization of Transesophageal Atrial Pacing to Assess Atrial Fibrillation Susceptibility in Mice
08:05

Optimization of Transesophageal Atrial Pacing to Assess Atrial Fibrillation Susceptibility in Mice

Published on: June 29, 2022

3.5K

优化通过使用额外树和统计关联措施选择心电图特征来检测心房动.

Georgios Petmezas1, Vasileios E Papageorgiou2, Rod S Passman3

  • 1School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Journal of electrocardiology
|January 29, 2026
PubMed
概括

这项研究引入了一种混合方法,用于选择关键心电图 (ECG) 功能,以检测心房动 (AFib). 该方法成功识别了重要的心电图标记,提高了AFib诊断的准确性.

关键词:
耳前动 (AFib) 检测检测电心电图 (ECG) 功能选择选项可解释的机器学习 (ML)非常随机的树木 (额外的树木)统计措施 统计措施

更多相关视频

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

2.2K
Transesophageal Atrial Burst Pacing for Atrial Fibrillation Induction in Rats
05:12

Transesophageal Atrial Burst Pacing for Atrial Fibrillation Induction in Rats

Published on: February 14, 2022

3.8K

相关实验视频

Last Updated: Jan 31, 2026

Optimization of Transesophageal Atrial Pacing to Assess Atrial Fibrillation Susceptibility in Mice
08:05

Optimization of Transesophageal Atrial Pacing to Assess Atrial Fibrillation Susceptibility in Mice

Published on: June 29, 2022

3.5K
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

2.2K
Transesophageal Atrial Burst Pacing for Atrial Fibrillation Induction in Rats
05:12

Transesophageal Atrial Burst Pacing for Atrial Fibrillation Induction in Rats

Published on: February 14, 2022

3.8K

科学领域:

  • 心脏病学和医疗信息学
  • 信号处理和机器学习

背景情况:

  • 心房动 (AFib) 是一种常见的心律失常,增加了中风和心力衰竭的风险.
  • 由于复杂的解释,从12导电心电图中准确检测AFib具有挑战性.
  • 机器学习 (ML) 和深度学习 (DL) 是有前途的,但需要最佳的特征选择.

研究的目的:

  • 开发一种混合特征选择方法来识别有歧视性的心电图特征.
  • 使用心电图数据客观区分AFib与正常鼻节律 (NSR).
  • 为了提高ML/DL模型的可解释性和效率,用于AFib检测.

主要方法:

  • 一个混合框架,将极端随机树 (额外树) 与统计关联措施相结合.
  • 从12个ECG中对形态,基于和光谱手工制作的特征的评估.
  • 引入新型指标:特征重要性评分 (FIS) 和整体特征重要性评分 (OFIS).

主要成果:

  • 该方法对97个特征进行了排名,确定了每个领先的前10名和整体的20名,具有很高的一致性.
  • 在RR区间的四分位数间的区间中,OFIS的正常化值是最高的,这表明它具有强大的歧视力.
  • 功能空间维度减少了近80%,保留了可解释性和生理意义.

结论:

  • 拟议的方法提供了一个可重现,可解释和统计基础的框架,用于ECG特征发现.
  • 这种方法在AFib检测中作为ML/DL模型的有价值的预处理步骤.
  • 这些发现有助于临床医生实现更准确的实时AFib检测.