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

Heart Sounds01:15

Heart Sounds

3.6K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
3.6K
Convolution Properties II01:17

Convolution Properties II

584
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
584
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Convolution Properties I01:20

Convolution Properties I

588
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
588
Korotkoff Sounds01:12

Korotkoff Sounds

8.1K
Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...
8.1K
Soundness of Cement01:17

Soundness of Cement

564
The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
564

You might also read

Related Articles

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

Sort by
Same author

Sporadic Progressive Ataxia and Palatal Tremor: An Autopsy Case without Tau Pathology.

Movement disorders clinical practice·2026
Same author

Editorial: Innovations in cognitive and psychological assessment: integrating immersive VR technologies for enhanced ecological validity.

Frontiers in psychology·2026
Same author

Structural and functional characterization of DNAH5 variants in a Portuguese family with primary ciliary dyskinesia.

Journal of assisted reproduction and genetics·2026
Same author

Functional Characterization of a Novel Homozygous <i>DNAH5</i> Single-Nucleotide Intronic Deletion in a Consanguineous Portuguese Family with Primary Ciliary Dyskinesia.

Cells·2026
Same author

FAIR Omics Data Management: Overview, Challenges, and Best Practices.

Advances in experimental medicine and biology·2026
Same author

Impact of sit-stand desks on subjective sitting time and psychological outcomes in office workers: findings from the SUFHA randomized controlled trial.

Journal of occupational and environmental medicine·2026

Related Experiment Video

Updated: Jan 30, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Deep Convolutional Neural Networks for Heart Sound Segmentation.

Francesco Renna, Jorge Oliveira, Miguel T Coimbra

    IEEE Journal of Biomedical and Health Informatics
    |January 23, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Deep convolutional neural networks accurately segment heart sounds into S1, systole, S2, and diastole. This novel approach improves heart sound analysis by outperforming existing methods in detecting key sound components.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.0K
    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.8K

    Related Experiment Videos

    Last Updated: Jan 30, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.1K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.0K
    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.8K

    Area of Science:

    • Biomedical Signal Processing
    • Artificial Intelligence in Medicine
    • Cardiology

    Background:

    • Accurate segmentation of heart sounds is crucial for diagnosing cardiac conditions.
    • Current methods for heart sound segmentation face limitations in precision and efficiency.

    Purpose of the Study:

    • To develop and evaluate a deep convolutional neural network (CNN) based method for segmenting heart sounds.
    • To improve the accuracy and reliability of identifying heart sound components (S1, systole, S2, diastole).

    Main Methods:

    • Utilized a deep convolutional neural network architecture inspired by image segmentation techniques.
    • Integrated temporal modeling schemes with hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs).
    • Applied the methods to heart sound signals from the PhysioNet dataset.

    Main Results:

    • Achieved an average sensitivity of 93.9% in detecting S1 and S2 sounds.
    • Attained an average positive predictive value of 94% for S1 and S2 sound detection.
    • Demonstrated superior performance compared to current state-of-the-art heart sound segmentation methods.

    Conclusions:

    • Deep convolutional neural networks, combined with temporal modeling, offer a powerful tool for heart sound segmentation.
    • The proposed method enhances the accuracy of identifying fundamental heart sound components.
    • This advancement holds potential for improved non-invasive cardiac diagnostics.