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

858
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...
858
Electrocardiogram01:29

Electrocardiogram

3.2K
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...
3.2K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

3.4K
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....
3.4K

You might also read

Related Articles

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

Sort by
Same author

Risk Stratification of Patients With Type 2 Long-QT Syndrome Through Analysis of T-Wave Morphology.

Journal of the American Heart Association·2026
Same author

Spinal motor neuron pools may be partly driven by impulsive common inputs.

The Journal of physiology·2026
Same author

Effects of Macronutrient Deprivation on Spring Wheat Productivity.

Plants (Basel, Switzerland)·2026
Same author

Controlled External Thigh Compression: A Feasible Method to Simulate Venous Hemodynamic Alterations Resembling Deep Vein Thrombosis.

Annals of biomedical engineering·2026
Same author

Electrophysiological characterization of pre-adolescents born with intrauterine growth restriction: insights from clinical and computational data.

The Journal of physiology·2025
Same author

The 2023 wearable photoplethysmography roadmap.

Physiological measurement·2023

Related Experiment Video

Updated: Sep 3, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

891

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

Ana Santos Rodrigues1, Rytis Augustauskas2, Mantas Lukoševičius3

  • 1Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

Estimating the spatial QRS-T angle, a key indicator for sudden cardiac death risk, is now possible using fewer electrocardiogram (ECG) leads. This breakthrough enables comfortable, ambulatory monitoring with consumer devices.

Keywords:
cardiovascular heath assessmentcomposite loss functionconsumer healthcare devicesmachine learningregressionunobtrusive monitoringwearable devices

More Related Videos

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

3.9K
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

8.7K

Related Experiment Videos

Last Updated: Sep 3, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

891
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

3.9K
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

8.7K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • The spatial QRS-T angle is a valuable predictor for sudden cardiac death (SCD) risk stratification.
  • Current estimation methods rely on 12-lead electrocardiogram (ECG) systems, which are not suitable for continuous ambulatory monitoring.

Purpose of the Study:

  • To develop a novel method for estimating spatial QRS-T angles using a reduced number of ECG leads.
  • To facilitate ambulatory monitoring of cardiac health indicators through consumer-grade ECG devices.

Main Methods:

  • A deep learning model was designed to identify QRS and T wave vectors from ECG data.
  • An innovative loss function was implemented to guide the model in accurately determining vector coordinates in 3D space.
  • The model was trained and validated using the extensive PTB-XL dataset, progressively reducing the number of ECG leads.

Main Results:

  • Spatial QRS-T angles can be accurately estimated using a subset of four leads: {I, II, aVF, V2}.
  • The method achieved acceptable accuracy with absolute mean and median errors of 11.4° and 7.3°, respectively.
  • This accuracy is sufficient for identifying abnormal spatial QRS-T angles without compromising patient comfort.

Conclusions:

  • The developed deep learning model enables reliable spatial QRS-T angle estimation from reduced-lead ECGs.
  • This approach paves the way for ambulatory monitoring of SCD risk using wearable ECG devices.
  • Patients at high risk for SCD, including those with chronic cardiac and kidney conditions, stand to benefit significantly.