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

3.4K
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...
3.4K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

909
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...
909
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.8K
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...
8.8K
Pulse rhythm01:30

Pulse rhythm

952
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
952
Classification of Signals01:30

Classification of Signals

975
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
975
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

4.8K
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....
4.8K

You might also read

Related Articles

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

Sort by
Same author

The Alberta Quality Assessment Tool: Risk of Bias (AQAT:RoB) for the Evaluation of Medical Large Language Model Question-Answer Studies: Development and Pilot Validation.

Journal of medical Internet research·2026
Same author

From Motion Artifacts to Clinical Insight: Multi-Modal Deep Learning for Robust Arrhythmia Screening in Ambulatory ECG Monitoring.

Sensors (Basel, Switzerland)·2026
Same author

Automatic Bone Segmentation from MRI for Real-Time Knee Tracking in Fluoroscopic Imaging.

Diagnostics (Basel, Switzerland)·2022
Same author

An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.

Sensors (Basel, Switzerland)·2021
Same author

Usability and Acceptability of a Home Blood Pressure Telemonitoring Device Among Community-Dwelling Senior Citizens With Hypertension: Qualitative Study.

JMIR aging·2019
Same author

Telemonitoring and Protocolized Case Management for Hypertensive Community-Dwelling Seniors With Diabetes: Protocol of the TECHNOMED Randomized Controlled Trial.

JMIR research protocols·2016
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 27, 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

3.9K

Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.

Hongzu Li1, Pierre Boulanger1

  • 1Department of Computer Science, Faculty of Science, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3, Canada.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using electrocardiogram (ECG) spectrograms and a Convolutional Neural Network to detect complex heart anomalies, improving early cardiovascular disease diagnosis beyond current capabilities.

Keywords:
anomaly detectiondeep learningelectrocardiogrammachine learningsignal processing

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

2.2K
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

1.0K

Related Experiment Videos

Last Updated: Sep 27, 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

3.9K
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

2.2K
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

1.0K

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of mortality, necessitating advanced diagnostic tools.
  • Existing commercial devices primarily detect basic rhythm fluctuations, limiting the diagnosis of complex cardiac anomalies.
  • Early and accurate detection of diverse heart conditions is crucial for effective patient management and improved health outcomes.

Purpose of the Study:

  • To develop an advanced algorithm for detecting a wider range of heart anomalies than currently available commercial products.
  • To improve the accuracy and sensitivity of diagnosing both cardiac rhythm and heartbeat irregularities.
  • To leverage Short-Time Fourier Transform (STFT) spectrograms and handcrafted features for enhanced ECG analysis.

Main Methods:

  • A novel method combining Short-Time Fourier Transform (STFT) spectrograms of ECG signals with handcrafted features was developed.
  • A Convolutional Neural Network (CNN) model was proposed and trained to analyze the combined ECG data.
  • The algorithm was evaluated on its ability to detect multiple types of cardiac rhythm and heartbeat anomalies.

Main Results:

  • The algorithm achieved 99.79% accuracy in detecting 16 different rhythm anomalies, with a low 0.15% false-alarm rate and 99.74% sensitivity.
  • It also demonstrated high performance in detecting 13 heartbeat anomalies, achieving 99.18% accuracy, a 0.45% false-alarm rate, and 98.80% sensitivity.
  • These results surpass the diagnostic capabilities of many current commercial heart monitoring products.

Conclusions:

  • The proposed STFT spectrogram and CNN-based method offers a significant advancement in diagnosing complex cardiovascular conditions.
  • This approach enhances the early detection of both rhythm and heartbeat anomalies, potentially improving patient outcomes.
  • The high accuracy and sensitivity suggest clinical utility for this advanced ECG analysis technique.