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

Electrocardiogram01:29

Electrocardiogram

2.3K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
2.3K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

635
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....
635
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

552
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
552
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.9K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
3.9K

You might also read

Related Articles

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

Sort by
Same author

Muriicola marianensis sp. nov., isolated from seawater.

International journal of systematic and evolutionary microbiology·2014
Same author

Protective effect of apigenin on ischemia/reperfusion injury of the isolated rat heart.

Cardiovascular toxicology·2014
Same author

Inhibition of monocyte adhesion to brain-derived endothelial cells by dual functional RNA chimeras.

Molecular therapy. Nucleic acids·2014
Same author

Presynaptic GABAergic inhibition regulated by BDNF contributes to neuropathic pain induction.

Nature communications·2014
Same author

Advances in malignant peritoneal mesothelioma.

International journal of colorectal disease·2014
Same author

Regulation of BGC-823 cell sensitivity to adriamycin via miRNA-135a-5p.

Oncology reports·2014

Related Experiment Video

Updated: Jun 18, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.6K

A simple and effective deep neural network based QRS complex detection method on ECG signal.

Wei Zhao1, Zhenqi Li1, Jing Hu1

  • 1Central Research Institute, Guangzhou Shiyuan Electronics Co., Ltd., Guangzhou, China.

Frontiers in Physiology
|July 30, 2024
PubMed
Summary

A novel deep neural network (DNN) algorithm accurately detects QRS complexes in electrocardiograph (ECG) signals. This efficient method shows promise for cardiovascular disease monitoring on wearable devices.

Keywords:
QRS complexQRS complex boundaryQrs complex detectiondeep learningelectrocardiogram (ECG)

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.7K
Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.4K

Related Experiment Videos

Last Updated: Jun 18, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.6K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.7K
Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.4K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Accurate QRS complex detection is crucial for electrocardiograph (ECG) signal analysis.
  • ECG analysis aids in monitoring and diagnosing cardiovascular diseases.

Purpose of the Study:

  • To develop a simple, effective, and computationally efficient deep neural network (DNN) algorithm for QRS complex detection.
  • To evaluate the algorithm's performance across multiple public ECG datasets and compare it with existing state-of-the-art methods.

Main Methods:

  • A DNN model featuring a Feature Pyramid Network (FPN) backbone with dual input channels and a location head was designed.
  • Depthwise convolution was employed to minimize model parameters.
  • A novel training strategy included specific QRS complex proximity targets and data augmentation with simulated high heart rate ECG segments.

Main Results:

  • The proposed algorithm achieved comparable performance to state-of-the-art methods with significantly fewer parameters (26,976) and FLOPs (9.90M).
  • On the MITBIH NST dataset, it showed sensitivity of 95.59% and precision of 91.03%.
  • On the CPSC 2019 dataset, it achieved similar sensitivity (95.15%) and improved precision (91.75%).

Conclusions:

  • The developed DNN algorithm offers a computationally efficient solution for QRS complex detection.
  • Its low parameter count and FLOPs make it suitable for real-time ECG analysis on resource-constrained wearable devices.
  • The algorithm provides a valuable tool for cardiovascular health monitoring.