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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

10.0K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
10.0K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.7K
3.7K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

13.8K
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
13.8K
Nursing Code of Ethics01:29

Nursing Code of Ethics

4.5K
The Nursing Code of Ethics sets the ethical benchmark for the profession, and guides nurses in ethical analysis and decision making at the societal, organizational, and clinical levels. The code encompasses showing compassion and respect for the patient, their families, and communities in all circumstances while committing to providing patient-centered care. In addition, the code states that nurses must advocate for the patient by defending a cause or recommendation to protect their rights,...
4.5K
Antibiotic Selection00:57

Antibiotic Selection

59.9K
Overview
59.9K
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.0K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.0K

You might also read

Related Articles

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

Sort by
Same author

Evaluating canonical babbling ratios extracted from day-long audio recordings in infants later diagnosed with autism spectrum disorder.

Infant behavior & development·2025
Same author

Predicting quantum emitter fluctuations with time-series forecasting models.

Scientific reports·2024
Same author

Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision.

Scientific reports·2023
Same author

Family still matters: Human social motivation across 42 countries during a global pandemic.

Evolution and human behavior : official journal of the Human Behavior and Evolution Society·2022
Same author

Publisher Correction: Fundamental social motives measured across forty-two cultures in two waves.

Scientific data·2022
Same author

Fundamental social motives measured across forty-two cultures in two waves.

Scientific data·2022
Same journal

Continuous tracking of aortic aneurysm diameter with peripheral pulse waves: a computational framework combining sequential Markov chain Monte Carlo with Kalman filtering.

Physiological measurement·2026
Same journal

The 2026 global roadmap for textile-integrated wearable technologies in health.

Physiological measurement·2026
Same journal

Augmenting single-lead ECG interpretation through QRS waveform decomposition and rotation.

Physiological measurement·2026
Same journal

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Physiological measurement·2026
Same journal

Dual warm-start fusion versus attention-based fusion in low-label ECG-PCG classification: a controlled ablation study.

Physiological measurement·2026
Same journal

Inter-patient multi-label ECG classification via low-rank adaptation fine-tuned large language models with dynamic graph convolutional network.

Physiological measurement·2026
See all related articles

Related Experiment Video

Updated: Feb 1, 2026

Ambulatory ECG Recording in Mice
08:00

Ambulatory ECG Recording in Mice

Published on: May 27, 2010

24.8K

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.

Muhammed Rizwan1, Bradley M Whitaker2, David V Anderson3

  • 1Department of Electrical Engineering, University of Management and Technology, Lahore, Pakistan.

Physiological Measurement
|December 8, 2018
PubMed
Summary
This summary is machine-generated.

This study presents an automated algorithm for detecting atrial fibrillation (AF) from ECG signals using machine learning. The developed classifier achieved an overall test score of 0.80, demonstrating effective AF detection.

More Related Videos

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.1K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.8K

Related Experiment Videos

Last Updated: Feb 1, 2026

Ambulatory ECG Recording in Mice
08:00

Ambulatory ECG Recording in Mice

Published on: May 27, 2010

24.8K
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.1K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.8K

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Cardiology

Background:

  • Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for cardiovascular health.
  • Accurate and automated AF detection remains a challenge in clinical practice.

Purpose of the Study:

  • To develop and validate an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals.
  • To classify four types of ECG signals: normal, AF, other, and noisy.

Main Methods:

  • Feature extraction from ECG waveforms, including statistical features and fiduciary points.
  • Application of sparse coding for unsupervised feature extraction.
  • Utilizing a decision tree-based ensemble learning classifier with disciplined feature selection.

Main Results:

  • The classifier achieved F1 scores of 0.91 (Normal), 0.78 (AF), and 0.71 (Other) on hidden test data.
  • An overall test score of 0.80 was obtained by averaging the F1 scores across classes.
  • Demonstrated the effectiveness of feature selection and ensemble learning for AF classification.

Conclusions:

  • Feature selection and ensemble learning significantly improve the performance of ECG-based atrial fibrillation classification.
  • The developed algorithm provides an accurate and automated approach for AF detection.