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

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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 to...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Electrocardiogram01:29

Electrocardiogram

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 the T...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Classification of Signals01:30

Classification of Signals

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...

You might also read

Related Articles

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

Sort by
Same author

Adopting common data elements for the National Trauma Research Repository through a consensus meeting: the trauma core.

Trauma surgery & acute care open·2026
Same author

Ethical Implications of the Slow Code: A Systematic Review of Ethics of Slow Codes in U.S. Hospitals.

Critical care medicine·2026
Same author

PCT-Anchored Machine Learning for Pre-Culture Identification of Gram-Negative Sepsis in Children: A Four-Site Study.

Shock (Augusta, Ga.)·2026
Same author

Mortality risk prediction from dense PICU data from patients with suspected infection: data-derived physiological trajectories outperform expert assessments when temporal resolution is high.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Assessing the capability of large language models in answering pediatric critical care board-style questions.

Scientific reports·2026
Same author

Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.

JMIR formative research·2026

Related Experiment Videos

CardioFM: A Multimodal Foundation Model for Joint ECG and PPG Representation Learning.

Md Hassanuzzaman1,2,3, Tilendra Choudhary2,3, Alasdair Gent2,3

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.

Research Square
|May 25, 2026
PubMed
Summary

CardioFM, a new foundation model, jointly analyzes electrocardiography (ECG) and photoplethysmography (PPG) signals. This multimodal approach improves cardiovascular disease classification and hemodynamic monitoring without extra hardware.

Related Experiment Videos

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiovascular Signal Processing

Background:

  • Electrocardiography (ECG) and photoplethysmography (PPG) are co-acquired but lack unified foundation models.
  • Existing methods are modality-specific or domain-agnostic, failing to capture cross-modal physiological coupling.
  • There is a need for a model that integrates ECG and PPG for comprehensive cardiac monitoring.

Purpose of the Study:

  • To develop CardioFM, a self-supervised multimodal foundation model for joint ECG and PPG analysis.
  • To learn unified representations from diverse patient data for cross-context transfer.
  • To demonstrate the model's utility in cardiovascular disease classification, hemodynamic trending, and intensive care alarm reduction.

Main Methods:

  • Developed CardioFM using bidirectional cross-modal attention and adaptive residual vector quantization.
  • Pretrained the model on over 500,000 hours of ECG and PPG data from diverse settings.
  • Evaluated performance on cardiovascular disease classification, QT interval estimation, pulse arrival time measurement, and false alarm reduction.

Main Results:

  • Achieved an F1-score of 0.86 for cardiovascular disease classification.
  • Estimated QT interval with a mean error of 20.2 ms and pulse arrival time with a mean error of 22.7 ms.
  • Demonstrated superior performance in false alarm reduction and demographic inference, encoding biological characteristics without diagnostic labels.

Conclusions:

  • CardioFM consolidates cardiac biosignal analysis, offering a unified framework for clinical monitoring and wearables.
  • The model's cross-modal attention enhances robustness to signal degradation.
  • CardioFM is compatible with edge deployment and requires no additional sensing hardware, paving the way for broader critical illness surveillance.