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

Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

2.3K
To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
2.3K
Classification of Signals01:30

Classification of Signals

1.3K
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...
1.3K
Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

3.0K
Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
3.0K
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

704
Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
704

You might also read

Related Articles

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

Sort by
Same author

Comparison study of population-based methods for non-invasive fetal electrocardiography extraction.

Frontiers in medicine·2026
Same author

Machine learning based classification in obstetrics: evaluating models, partitioning strategies, and key predictors in cardiotocography.

BMC pregnancy and childbirth·2026
Same author

Immersive Technologies for Cognitive Rehabilitation in Dementia and Mild Cognitive Impairment: Systematic Review.

Journal of medical Internet research·2026
Same author

Performance comparison of conductive textile electrodes in ECG monitoring.

Frontiers in bioengineering and biotechnology·2026
Same author

Secure IoMT smartwatch-based blood glucose monitoring using multimodal activity and nutrition data with transfer learning.

Scientific reports·2026
Same author

A novel augmented reality and reinforcement learning empowered communication framework for underwater unmanned autonomous vehicle.

Scientific reports·2026

Related Experiment Video

Updated: Jan 9, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

885

Ballistocardiography Signal Quality Assessment Using Machine Learning.

Martina Ladrova, Dominik Vilimek, Katerina Barnova

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study developed a machine learning method to automatically assess Ballistocardiography (BCG) signal quality, improving heartbeat detection accuracy for cardiac MRI. The Extra Trees Classifier achieved 86.65% accuracy in identifying high-quality BCG signals.

    More Related Videos

    Ultrasonic Assessment of Myocardial Microstructure
    10:53

    Ultrasonic Assessment of Myocardial Microstructure

    Published on: January 14, 2014

    5.8K
    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
    06:16

    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

    Published on: August 9, 2024

    786

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    885
    Ultrasonic Assessment of Myocardial Microstructure
    10:53

    Ultrasonic Assessment of Myocardial Microstructure

    Published on: January 14, 2014

    5.8K
    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
    06:16

    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

    Published on: August 9, 2024

    786

    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Physiology
    • Machine Learning

    Background:

    • Ballistocardiography (BCG) is a non-invasive method for monitoring cardiac activity.
    • BCG shows potential for magnetic resonance imaging (MRI) gating applications.
    • Accurate heartbeat detection is vital for precise MR triggering.

    Purpose of the Study:

    • To develop an automated method for identifying Ballistocardiography signal quality.
    • To enhance heartbeat detection accuracy for improved MR triggering.
    • To classify BCG segments as high- or low-quality using machine learning.

    Main Methods:

    • Analyzed 21 statistical signal features of BCG data.
    • Evaluated 14 machine learning models for BCG signal classification.
    • Utilized ensemble classifiers, including the Extra Trees Classifier.

    Main Results:

    • The Extra Trees Classifier achieved 86.65% overall accuracy, 80.54% sensitivity, and 73.62% precision.
    • Time-domain features demonstrated superior performance over frequency-domain features.
    • Four ensemble classifiers exhibited the best performance in classifying BCG signal quality.

    Conclusions:

    • Automated BCG signal quality assessment improves heartbeat detection accuracy.
    • This method enhances the robustness of BCG applications, particularly in cardiac MRI.
    • The technique automates channel selection, reducing reliance on visual assessment and improving MRI data acquisition.