Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electrocardiogram Fundamentals

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...

Electrocardiogram

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...

ECG Interpretation of Rhythms

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...

Instrumentation Amplifier

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...

Correlation between ECG and Cardiac Cycle

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...

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...

You might also read

Related Articles

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

Sort by

Same author

Using SEEK for multichannel pattern recognition.

Computers and biomedical research, an international journal·1983

Same author

A system for clinical data-base applications.

Computers and biomedical research, an international journal·1982

Same journal

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Rule-Based Learning for More Accurate ECG Analysis.

¹Computer Science Division, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720; 100 Wellington Avenue, New Rochelle, NY 10804.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

A new learning algorithm accurately identifies cardiac abnormalities by analyzing QRS morphologies in 24-hour electrocardiograms (ECGs). This approach represents waveform shapes as rule activation trees, improving diagnostic accuracy for heart conditions.

More Related Videos

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

Related Experiment Videos

Last Updated: May 29, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

Area of Science:

Cardiology
Biomedical Engineering
Machine Learning

Background:

Long-term electrocardiograms (ECGs) contain a limited set of QRS morphologies (waveform shapes).
Analysis of these QRS morphologies is crucial for detecting cardiac abnormalities.
Accurate identification of QRS morphology instances in 24-hour ECG recordings remains a challenge.

Purpose of the Study:

To develop and evaluate a novel learning algorithm for the accurate identification of QRS morphologies in long-term ECG recordings.
To improve the detection of cardiac abnormalities through precise waveform analysis.

Main Methods:

A new learning algorithm was developed, representing each QRS morphology as a tree of rule activations.
Attribute measurements are associated with rules, each possessing a syntactic pattern and a semantic procedure for knowledge management.
Delineation refinement and a conflict resolution mechanism were employed to enhance signal interpretation accuracy.

Main Results:

The developed system demonstrated the ability to accurately identify instances of various QRS morphologies within 24-hour ECG data.
The algorithm effectively utilizes rule activations and refinement processes for detailed waveform analysis.
Comparison with an existing program indicated the promising performance of the new approach.

Conclusions:

The novel learning algorithm shows significant promise for accurate QRS morphology identification in long-term ECG analysis.
This method offers an improved approach to detecting cardiac abnormalities by enhancing ECG interpretation.
The system's ability to manage complex waveform data suggests potential for broader clinical application.