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

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

972
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
972
Pulse rhythm01:30

Pulse rhythm

807
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...
807
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

925
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
925
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

21
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...
21
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

222
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,...
222
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

4.7K
The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
4.7K

You might also read

Related Articles

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

Sort by
Same author

Molecular Weight Controls Interactions between Plastic Deformation and Fracture in Cold Spray of Glassy Polymers.

ACS omega·2023
Same author

Machine learning analysis of SERS fingerprinting for the rapid determination of <i>Mycobacterium tuberculosis</i> infection and drug resistance.

Computational and structural biotechnology journal·2022
Same author

Xanthine oxidase inhibitory study of eight structurally diverse phenolic compounds.

Frontiers in nutrition·2022
Same author

Integration of renewable energy and technological innovation in realizing environmental sustainability: the role of human capital in EKC framework.

Environmental science and pollution research international·2022
Same author

Nexus between environmental vulnerability and agricultural productivity in BRICS: what are the roles of renewable energy, environmental policy stringency, and technology?

Environmental science and pollution research international·2022
Same author

Effect of soil texture and zinc oxide nanoparticles on growth and accumulation of cadmium by wheat: a life cycle study.

Environmental research·2022

Related Experiment Video

Updated: Jul 9, 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

3.7K

Real-time arrhythmia detection using convolutional neural networks.

Thong Vu1, Tyler Petty1, Kemal Yakut2

  • 1School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States.

Frontiers in Big Data
|December 7, 2023
PubMed
Summary
This summary is machine-generated.

Real-time detection of abnormal heart rhythms (arrhythmia) is now feasible using convolutional neural networks on electrocardiogram (ECG) images. This breakthrough enables efficient, in-home heart monitoring, improving cardiovascular disease management.

Keywords:
anomaly detectionbig dataconvolutional neural networksmachine learningsmart health

More Related Videos

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

633
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: Jul 9, 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

3.7K
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

633
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:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Cardiovascular diseases are a leading global cause of death.
  • Current diagnostic methods are not ideal for continuous, out-of-hospital monitoring.
  • Real-time detection of arrhythmias is crucial for long-term cardiac health management.

Purpose of the Study:

  • To develop a real-time system for detecting arrhythmias using convolutional neural networks (CNNs).
  • To evaluate the runtime performance and computational cost of the arrhythmia detection workflow.
  • To demonstrate the feasibility and generalizability of CNN-based arrhythmia detection for in-home monitoring.

Main Methods:

  • Utilized convolutional neural networks (CNNs) to classify arrhythmia conditions from electrocardiogram (ECG) images.
  • Conducted extensive experiments to evaluate the computational cost of each workflow step for real-time processing.
  • Validated the trained model using data from a customized wearable sensor in a lab setting.

Main Results:

  • Achieved feasible real-time arrhythmic detection using CNNs.
  • Demonstrated high accuracy and efficiency of the approach.
  • Confirmed the generalizability of the model with wearable sensor data.

Conclusions:

  • CNNs can effectively support real-time arrhythmic detection from ECG images.
  • The developed approach is accurate, efficient, and suitable for in-home heart monitoring.
  • This research integrates machine learning with traditional diagnostics for improved cardiovascular care.