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

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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 to...
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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. When...
Electrocardiogram01:29

Electrocardiogram

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 the T...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...

You might also read

Related Articles

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

Sort by
Same author

Rational Molecular Design of a Multi-Electron Organic Anode via Rapid Microwave Synthesis for Ultrastable NH<sub>4</sub> <sup>+</sup> Storage.

Angewandte Chemie (International ed. in English)·2026
Same author

Leaf Habit Drives Divergent Leaf Hydraulic Strategies in Gardening Trees From a Subtropical City in China.

Physiologia plantarum·2026
Same author

Tungsten-Doped RuO<sub>2</sub> Enables Direct Chloride Adsorption and Accelerated Krishtalik Kinetics for Industrial Chlor-Alkali Electrolysis.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Plasma metabolic signature of cardiovascular-kidney-metabolic syndrome and incident stroke: A nationwide prospective cohort study.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Survival benefit analysis of beta-blockers in patients with sepsis-induced TnT-positive myocardial injury across different clinical subtypes of sepsis: A retrospective study based on the MIMIC-IV database.

European journal of pharmacology·2026
Same author

Supracrestal Tissue Height Changes Around the Healing Abutment in the Posterior Region After Second-Stage Implant Surgery Measured by Intraoral Scanning: A Retrospective Study.

Clinical oral implants research·2026

Related Experiment Video

Updated: Jun 19, 2026

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

[Detection of QRS complexes using wavelet transformation and golden section search algorithm].

Wenli Chen1, Zhiwen Mo, Wen Guo

  • 1College of Mathematics and Softwvare Science, Sichuan Normal University, Chengdu 610068, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|October 10, 2009
PubMed
Summary

Accurate electrocardiogram (ECG) analysis relies on precise QRS complex detection. This study enhances detection rates using wavelet transform and Golden Section Search, achieving 99.6% accuracy on MIT-BIH data.

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Area of Science:

  • Biomedical Signal Processing
  • Cardiovascular Diagnostics
  • Computational Medicine

Context:

  • Electrocardiogram (ECG) signal characteristic parameter extraction is crucial for medical diagnosis.
  • Accurate QRS complex detection is a fundamental prerequisite for reliable ECG analysis and parameter calculation.
  • Existing methods may face challenges with mis-detections and missed detections in ECG signals.

Purpose:

  • To develop a robust and accurate method for QRS complex detection in ECG signals.
  • To improve the precision of QRS complex identification by combining wavelet transform with adaptive thresholding.
  • To minimize detection errors and enhance the reliability of ECG analysis.

Summary:

  • This research applies the modulus maximum of wavelet transform for initial QRS complex detection in ECG signals.
  • The Golden Section Search algorithm is employed to dynamically adjust detection thresholds, addressing mis-detections and missed detections.
  • The proposed method achieves a high correct detection rate of 99.6% for QRS complexes, validated using the MIT-BIH ECG database.

Impact:

  • Significantly improves the accuracy and reliability of automated ECG analysis.
  • Provides a foundation for more precise calculation of ECG-derived parameters.
  • Enhances the potential for earlier and more accurate diagnosis of cardiac conditions through improved ECG interpretation.