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

Instrumentation Amplifier01:25

Instrumentation Amplifier

507
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...
507
Electrocardiogram01:29

Electrocardiogram

2.3K
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...
2.3K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

594
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...
594

You might also read

Related Articles

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

Sort by
Same author

Technical Tips: Preventing Electrode-Induced Skin Injuries During Prolonged Ambulatory Electroencephalography Monitoring.

The Neurodiagnostic journal·2026
Same author

OpenStride: an inexpensive, open-source force plate actometry system for quantification of rodent motor activity and behaviour.

Scientific reports·2026
Same author

A wireless power transfer system for leadless endovascular electrocorticography.

Communications engineering·2026
Same author

Individualized brain-computer interface for people with disabilities: a review.

Frontiers in human neuroscience·2026
Same author

Memory T Cell Donor Lymphocyte Infusion as a Treatment for Viral Infection After Pediatric Haploidentical Hematopoietic Stem Cell Transplant.

Transplantation and cellular therapy·2025
Same author

Scoping review: Sexual dysfunction in people with epilepsy.

Seizure·2025

Related Experiment Video

Updated: Jul 1, 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

8.6K

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware.

Zhaojing Huang1, Luis Fernando Herbozo Contreras2, Wing Hang Leung2

  • 1School of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia. zhaojing.huang@sydney.edu.au.

Journal of Cardiovascular Translational Research
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

Two novel AI models, CLTC and CCfC, effectively identify abnormalities in electrocardiogram (ECG) data. Deployed on microcontrollers, these models offer efficient, generalizable solutions for edge healthcare applications.

Keywords:
Abnormality identificationECG dataEdge devicesGeneralizationPerformance evaluationRobustnessSimple network

More Related Videos

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
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

Related Experiment Videos

Last Updated: Jul 1, 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

8.6K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
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

Area of Science:

  • Artificial Intelligence in Healthcare
  • Biomedical Signal Processing
  • Machine Learning for Medical Devices

Background:

  • Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
  • Developing efficient AI models for real-time ECG abnormality detection is essential for remote patient monitoring and edge computing.
  • Existing models often face challenges with resource constraints and generalizability across diverse datasets.

Purpose of the Study:

  • To introduce and evaluate two novel deep learning models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), for ECG abnormality identification.
  • To assess the performance, generalizability, and resilience of these models.
  • To demonstrate the feasibility of deploying these models on resource-constrained microcontrollers for edge applications.

Main Methods:

  • Development of two distinct deep learning architectures: CLTC and CCfC, both based on ConvLSTM2D.
  • Training and evaluation of the models using the Telehealth Network of Minas Gerais (TNMG) subset dataset.
  • Validation of generalizability using the China Physiological Signal Challenge 2018 (CPSC) dataset.
  • Assessment of model performance using F1 scores, AUROC values, and accuracy.
  • Evaluation of resource utilization (memory and flash) for microcontroller deployment.

Main Results:

  • Both CLTC and CCfC models demonstrated comparable performance in identifying ECG abnormalities, achieving similar F1 scores and AUROC values.
  • The CCfC model exhibited slightly higher overall accuracy.
  • The CLTC model showed superior performance in handling datasets with empty ECG channels.
  • Successful deployment on a resource-constrained microcontroller was achieved, confirming edge computing viability.
  • Models demonstrated strong generalization capabilities when tested on an independent dataset (CPSC).
  • Efficient resource utilization was confirmed, with models occupying 70.6% of memory and 9.4% of flash memory.

Conclusions:

  • The developed CLTC and CCfC models represent significant advancements in AI-driven ECG abnormality identification.
  • These models are suitable for deployment on edge devices due to their efficient resource utilization and proven generalizability.
  • The research supports the integration of advanced AI into real-world healthcare applications, particularly for remote monitoring and diagnostics.