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

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...
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...
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...
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism, and...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

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...

You might also read

Related Articles

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

Sort by
Same author

Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.

Applied and environmental microbiology·2026
Same author

Conserved 3' stem-loop structures enable comprehensive analysis of bacterial transcription termination in metagenomes.

Microbiome·2026
Same author

Molecular Mechanism of Baicalin in Ameliorating Chronic Pancreatitis: Insights From Network Pharmacology and Metabolomics.

Biomedical chromatography : BMC·2026
Same author

Dominant peak frequency bias modeling and boundary compensation in STFT-BOTDR with small BFS differences.

Optics express·2026
Same author

Nutritional Supply vs. Flavor Quality: Characterizing the Physicochemical Properties and Amino Acid Profiles of Tomatoes from Beijing and Shandong.

Foods (Basel, Switzerland)·2026
Same author

Bowel wall thickness measured by intestinal ultrasound as a marker of endoscopic disease activity in patients with Crohn's disease.

Gastroenterologia y hepatologia·2026

Related Experiment Video

Updated: Jul 17, 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

ECG QRS complex detection using slope vector waveform (SVW) algorithm.

Xiaomin Xu1, Ying Liu

  • 1Department of Electronic and Computer Engineering, University of Portsmouth, UK.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
Summary

A novel Slope Vector Waveform (SVW) algorithm enhances electrocardiogram (ECG) QRS complex detection and RR interval analysis. This efficient method excels in noisy conditions, making it suitable for real-time monitoring.

More Related Videos

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

Related Experiment Videos

Last Updated: Jul 17, 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

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

Area of Science:

  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
  • Detecting the QRS complex and evaluating RR intervals are key parameters in ECG interpretation.
  • Existing algorithms may struggle with signal noise, limiting their real-world application.

Purpose of the Study:

  • To introduce and evaluate a new algorithm, Slope Vector Waveform (SVW), for ECG QRS complex detection and RR interval evaluation.
  • To demonstrate the algorithm's robustness in the presence of noise.
  • To highlight its suitability for embedded real-time ECG monitoring applications.

Main Methods:

  • The proposed algorithm utilizes variable stage differentiation to extract slope vectors for feature extraction.
  • Non-linear amplification is employed to enhance the signal-to-noise ratio.
  • The algorithm's performance is assessed based on its QRS detection accuracy and computational efficiency.

Main Results:

  • The SVW algorithm demonstrates excellent performance in QRS complex detection, even with noisy ECG signals.
  • The method achieves high signal-to-noise ratio improvement through non-linear amplification.
  • The algorithm is computationally less intensive compared to existing methods.

Conclusions:

  • The Slope Vector Waveform (SVW) algorithm offers a robust and efficient solution for ECG QRS detection and RR interval analysis.
  • Its effectiveness in noisy environments and low computational demands make it ideal for embedded real-time ECG monitoring systems.
  • SVW presents a promising tool for advancing automated cardiac diagnostics.