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

3.9K
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...
3.9K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

975
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...
975
Instrumentation Amplifier01:25

Instrumentation Amplifier

775
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...
775
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

495
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
495
Cardiopulmonary Resuscitation III: AED Use01:23

Cardiopulmonary Resuscitation III: AED Use

139
Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
139
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

9.4K
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...
9.4K

You might also read

Related Articles

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

Sort by
Same author

Polymer-Based Multiparameter Sensing Integrated Photonic Chip for Health Monitoring.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

LoRa Power Model for Energy Optimization in IoT Applications.

Sensors (Basel, Switzerland)·2026
Same author

Color QR Codes for Smartphone-Based Analysis of Free Chlorine in Drinking Water.

Sensors (Basel, Switzerland)·2025
Same author

Optical Biosensors Utilizing Polymer-Based Athermal Integrated Photonic Devices.

ACS sensors·2025
Same author

Design, Characterization, and Performance of Woven Fabric Electrodes for Electrocardiogram Signal Monitoring.

Sensors (Basel, Switzerland)·2022
Same author

SOI Waveguide Bragg Grating Photonic Sensor for Human Body Temperature Measurement Based on Photonic Integrated Interrogator.

Nanomaterials (Basel, Switzerland)·2022

Related Experiment Video

Updated: Oct 18, 2025

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

8.8K

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG.

Hongqiang Li1, Zhixuan An1, Shasha Zuo2

  • 1Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China.

Sensors (Basel, Switzerland)
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) algorithm using an improved ResNet model enhances wearable electrocardiogram (ECG) devices for rapid arrhythmia detection. This AI-powered ECG system achieves a 98.3% average recognition rate for classifying seven common arrhythmia types.

Keywords:
ECG science popularizationbiomedical monitoringcloud computingfabric electrodesresidual network

More Related Videos

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

12.0K
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

2.6K

Related Experiment Videos

Last Updated: Oct 18, 2025

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

8.8K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

12.0K
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

2.6K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Heart disease remains a leading global cause of mortality.
  • Advancements in electrocardiogram (ECG) technology, particularly residual networks (ResNet), have improved cardiac physiology understanding.
  • Wearable ECG devices offer continuous monitoring but require sophisticated analysis for accurate diagnosis.

Purpose of the Study:

  • To develop an artificial intelligence-enabled ECG algorithm for a wearable device.
  • To improve the accuracy and speed of arrhythmia classification using an enhanced ResNet model.
  • To integrate a wearable ECG system with a cloud platform for diagnostics.

Main Methods:

  • A wearable ECG system comprising conductive fabric electrodes, a wireless acquisition module, a mobile app, and a cloud platform was utilized.
  • A novel algorithm based on an improved ResNet-50 architecture was developed for arrhythmia classification.
  • ECG signals were converted into two-dimensional images using Gramian angular fields, and the ResNet model was optimized with multistage shortcut branches and SELU activation functions.

Main Results:

  • The improved ResNet algorithm achieved an average recognition rate of 98.3% for classifying seven types of arrhythmia.
  • The system demonstrated effective rapid classification of various cardiac rhythm abnormalities.
  • The integration of the algorithm with the wearable hardware and cloud platform facilitated a comprehensive diagnostic solution.

Conclusions:

  • The proposed AI-enabled ECG algorithm based on an improved ResNet significantly enhances the diagnostic capabilities of wearable ECG devices.
  • This technology offers a promising approach for early and accurate detection of heart arrhythmias, potentially reducing mortality rates.
  • The system provides a scalable solution for remote cardiac monitoring and diagnostics.