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

1.7K
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)...
1.7K
Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

215
Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...
215
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

280
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,...
280
Location and Orientation of the Heart01:13

Location and Orientation of the Heart

2.2K
The human heart, despite its modest size and weight, is an organ of remarkable strength and endurance. Roughly the size of a fist, the heart weighs between 250 and 350 grams and is nestled within the mediastinum, the medial cavity of the thorax. It extends obliquely for about 12 to 14 cm, resting on the superior surface of the diaphragm. The heart is positioned anterior to the vertebral column and posterior to the sternum, with two-thirds of its mass lying to the left of the midsternal line.
2.2K
Cardiovascular System Abnormal Findings II: Auscultation01:25

Cardiovascular System Abnormal Findings II: Auscultation

86
Auscultation, an essential part of a heart examination, is done using a stethoscope. It provides crucial information about heart function and possible heart problems. Due to heart problems, abnormal sounds can be heard during systole or diastole. These sounds include S3 and S4 gallops, opening snaps, systolic clicks, and murmurs.
Abnormal Heart Sounds
Gallops:
86
Cardiac Output and Stroke Volume01:11

Cardiac Output and Stroke Volume

2.7K
Cardiac output (CO) is an integral aspect of human physiology, reflecting the heart's efficiency and responsiveness to the body's needs. It represents the volume of blood that the left or right ventricle ejects into the aorta or pulmonary trunk each minute. The CO is calculated by multiplying the heart rate (HR)—the number of heartbeats per minute—by the stroke volume (SV)—the amount of blood pumped out with each heartbeat.
In an average resting adult male, the typical cardiac...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Inter-speaker acoustic differences of sustained vowels at varied dysarthria severities for amyotrophic lateral sclerosis.

JASA express letters·2024
Same author

A machine-learning tool to identify bistable states from calcium imaging data.

The Journal of physiology·2024
Same author

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.

IEEE journal of translational engineering in health and medicine·2023
Same author

Noise Robust Detection of Fundamental Heart Sound using Parametric Mixture Gaussian and Dynamic Programming.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021
Same author

Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021
Same author

Role of breath phase and breath boundaries for the classification between asthmatic and healthy subjects.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Related Experiment Video

Updated: May 24, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.6K

Exploring Wav2vec 2.0 Model for Heart Sound Analysis.

Alex Paul Kamson, Akshay V Sawant, Prasanta Kumar Ghosh

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study uses self-supervised learning (SSL) with wav2vec 2.0 for heart sound analysis, reducing the need for expert labels. The SSL model achieved high accuracy in detecting heart murmurs and segmenting heart sounds.

    More Related Videos

    Ultrasound-based Pulse Wave Velocity Evaluation in Mice
    08:07

    Ultrasound-based Pulse Wave Velocity Evaluation in Mice

    Published on: February 14, 2017

    13.1K
    Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
    11:13

    Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

    Published on: May 24, 2021

    6.2K

    Related Experiment Videos

    Last Updated: May 24, 2025

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
    12:09

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

    Published on: January 8, 2013

    13.6K
    Ultrasound-based Pulse Wave Velocity Evaluation in Mice
    08:07

    Ultrasound-based Pulse Wave Velocity Evaluation in Mice

    Published on: February 14, 2017

    13.1K
    Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
    11:13

    Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

    Published on: May 24, 2021

    6.2K

    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Medicine
    • Computational Acoustics

    Background:

    • Supervised learning for cardiovascular diagnostics requires extensive expert-annotated data, posing a significant bottleneck.
    • Deep learning models, particularly self-supervised learning (SSL) frameworks like wav2vec 2.0, offer a promising avenue to reduce reliance on labeled datasets.
    • Analyzing complex biological sounds like heart and lung sounds necessitates models capable of learning robust acoustic representations.

    Purpose of the Study:

    • To investigate the efficacy of the wav2vec 2.0 framework for automated heart sound analysis.
    • To reduce the dependency on cardiologists for data labeling in cardiovascular diagnostics.
    • To develop a pre-trained model for biological sound analysis adaptable to specific downstream tasks like heart murmur detection and phonocardiogram segmentation.

    Main Methods:

    • Pre-training the wav2vec 2.0 model on a diverse dataset including heart sounds, lung sounds, and ambient hospital noise.
    • Utilizing the pre-trained model as a feature extractor by freezing either the CNN encoder or the entire model.
    • Training classifier heads using a Long Short-Term Memory (LSTM) with self-attention framework for downstream tasks.
    • Evaluating the framework on heart murmur detection and segmentation of S1 and S2 heart sounds.

    Main Results:

    • The wav2vec 2.0 model pre-trained on 52 hours of biological sounds demonstrated superior performance compared to a model pre-trained on 960 hours of speech data.
    • Achieved a weighted accuracy of 81% for heart murmur detection.
    • Achieved a weighted accuracy of 97.56% for S1 and S2 sound segmentation.

    Conclusions:

    • Self-supervised learning with wav2vec 2.0 is a viable and effective approach for heart sound analysis, significantly reducing the need for labeled data.
    • The proposed framework shows strong potential for clinical application in non-invasive cardiovascular diagnostics.
    • The model's ability to generalize across different biological sounds enhances its utility for broader acoustic analysis tasks.