Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Amyloid Fibrils03:03

Amyloid Fibrils

12.0K
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,...
12.0K
Amyloid Fibrils03:03

Amyloid Fibrils

6.4K
6.4K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

259
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
259
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Fibril-associated Collagen01:11

Fibril-associated Collagen

3.4K
Fibril-associated collagens are a type of collagens present in the extracellular matrix with interrupted triple helices or FACIT (Fibril-associated collagens interrupted triple-helices). FACIT help connect and attach the collagen fibrils with each other as well as with other proteins of the extracellular matrix.
For example, the type II collagen fibrils in cartilage have covalently bound type IX fibril-associated collagens at regular intervals. Other types of fibril-associated collagens are...
3.4K
Confidence Intervals01:21

Confidence Intervals

10.8K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[A seroepidemiologic analysis of hepatitis B in Sichuan province].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2009
Same author

[Efficacy and safety of drospirenone-ethinylestradiol on contraception in healthy Chinese women: a multicenter randomized controlled trial].

Zhonghua fu chan ke za zhi·2009
Same author

RGS5, a hypoxia-inducible apoptotic stimulator in endothelial cells.

The Journal of biological chemistry·2009
Same author

Theory and experiment of a fiber loop mirror filter of two-stage polarization-maintaining fibers and polarization controllers for multiwavelength fiber ring laser.

Optics express·2009
Same author

Selective binding and highly sensitive fluorescent sensor of palmatine and dehydrocorydaline alkaloids by cucurbit[7]uril.

Organic & biomolecular chemistry·2009
Same author

Abatement of toluene from gas streams via ferro-electric packed bed dielectric barrier discharge plasma.

Journal of hazardous materials·2009

Related Experiment Video

Updated: Feb 6, 2026

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

[Automatic detection and classification of atrial fibrillation using RR intervals and multi-eigenvalue].

Zhibo Chen1, Jian Li1, Zhi Li2

  • 1School of Electronic Information, Sichuan University, Chengdu 610041, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|August 21, 2018
PubMed
Summary

This study introduces an automated method for detecting atrial fibrillation (AF) using electrocardiogram (ECG) RR intervals. The approach utilizes robust coefficient of variation, skewness, and Lempel-Ziv complexity for accurate AF classification.

Keywords:
Lempel-Ziv complexityatrial fibrillationrobust coefficient of variationskewnesssupport vector machine

More Related Videos

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
Robotic Ablation of Atrial Fibrillation
11:21

Robotic Ablation of Atrial Fibrillation

Published on: May 29, 2015

20.2K

Related Experiment Videos

Last Updated: Feb 6, 2026

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
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
Robotic Ablation of Atrial Fibrillation
11:21

Robotic Ablation of Atrial Fibrillation

Published on: May 29, 2015

20.2K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Context:

  • Atrial fibrillation (AF) is a prevalent cardiac arrhythmia requiring accurate clinical diagnosis.
  • Manual electrocardiogram (ECG) interpretation is time-consuming and susceptible to errors due to signal complexity.
  • Automated detection methods are crucial for efficient and reliable AF diagnosis.

Purpose:

  • To develop an automated feature extraction and classification method for atrial fibrillation detection using ECG RR intervals.
  • To address the limitations of manual ECG analysis, including time consumption and potential for misdiagnosis.
  • To improve the accuracy and efficiency of atrial fibrillation diagnosis.

Summary:

  • A novel feature extraction method using robust coefficient of variation (RCV), skewness parameter (SKP), and Lempel-Ziv complexity (LZC) of RR intervals is proposed.
  • These features are used to train a support vector machine (SVM) classifier for automated AF detection.
  • Validation on the MIT-BIH atrial fibrillation database demonstrated high sensitivity (95.81%) and specificity (96.48%).

Impact:

  • The proposed method offers an effective and accurate approach for automatic atrial fibrillation detection.
  • It has the potential to significantly aid clinicians in diagnosing AF, improving patient outcomes.
  • The method's high performance suggests its clinical applicability in real-world diagnostic settings.