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

3.7K
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)...
3.7K
Chromatographic Methods: Classification01:12

Chromatographic Methods: Classification

4.0K
Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...
4.0K
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

398
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
398
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.2K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
1.2K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.3K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.3K
Korotkoff Sounds01:12

Korotkoff Sounds

8.4K
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...
8.4K

You might also read

Related Articles

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

Sort by
Same author

High-speed quantitative X-ray multi-contrast imaging with deep learning based modulated pattern analysis.

Journal of synchrotron radiation·2026
Same author

Generative Models for Medical Image Creation and Translation: A Scoping Review.

Sensors (Basel, Switzerland)·2026
Same author

Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.

Annals of biomedical engineering·2025
Same author

Under-Urine-Adhered Supramolecular Hydrogel with Linearly Sustained Quercetin Release Facilitates Hemorrhagic Cystitis Healing via Inflammation Regulation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

M-AECA Net: A Mamba-Based Auxiliary Encoder With Cross-Attention Fusion Network for PET/CT Tumor Segmentation.

IEEE journal of biomedical and health informatics·2025
Same author

Predicting depression by using a novel deep learning model and video-audio-text multimodal data.

Frontiers in psychiatry·2025
Same journal

Compressed multi-scale entropy and its application in mechanical fault diagnosis.

The Review of scientific instruments·2026
Same journal

Bidirectional drive and multi-resolution adjustment across frequency bands in inertial impact piezoelectric motors via multimodal resonant vibration.

The Review of scientific instruments·2026
Same journal

A magnetic field sensor based on flaky Terfenol-D material and dual fiber grating.

The Review of scientific instruments·2026
Same journal

A novel E-field eight-way cavity combiner for high-power S-band applications.

The Review of scientific instruments·2026
Same journal

Constant radius blade spring suspended bench for vibration isolation.

The Review of scientific instruments·2026
Same journal

Qualification of infrared optical fibers and emitters for a spectrometer for in situ planetary exploration: Results from the TRIS (TRansmission and Illumination System) project.

The Review of scientific instruments·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

A Novel Ex vivo Culture Method for the Embryonic Mouse Heart
07:47

A Novel Ex vivo Culture Method for the Embryonic Mouse Heart

Published on: May 24, 2013

14.0K

A method for heart sound classification using sample augmentation and INDANet.

Jinpo Wang1, Zijian Qiao1,2, Yudong Yao3

  • 1Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, Zhejiang, China.

The Review of Scientific Instruments
|February 5, 2026
PubMed
Summary
This summary is machine-generated.

Cardiovascular disease diagnosis is improved with artificial intelligence. A novel method, INDANet, enhances heart sound classification accuracy, especially in regions with limited clinical experience.

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.7K
High-Throughput Measurement and Classification of Organic P in Environmental Samples
08:58

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

13.4K

Related Experiment Videos

Last Updated: Feb 7, 2026

A Novel Ex vivo Culture Method for the Embryonic Mouse Heart
07:47

A Novel Ex vivo Culture Method for the Embryonic Mouse Heart

Published on: May 24, 2013

14.0K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.7K
High-Throughput Measurement and Classification of Organic P in Environmental Samples
08:58

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

13.4K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Cardiovascular diseases (CVDs) are a leading cause of death globally, disproportionately affecting underdeveloped regions.
  • Limited clinical expertise in diagnosing heart conditions via auscultation in resource-limited settings necessitates advanced diagnostic tools.
  • Existing artificial intelligence (AI) methods for heart sound classification face challenges due to small datasets and high noise levels, impacting accuracy.

Purpose of the Study:

  • To develop and evaluate a novel AI-based method for accurate heart sound classification to aid in cardiovascular disease diagnosis.
  • To address the limitations of small sample sizes and significant noise in heart sound data.
  • To improve the robustness and generalization capabilities of AI models for auxiliary cardiac diagnoses.

Main Methods:

  • Preprocessing of heart sounds using a Butterworth filter to remove extraneous noise.
  • Implementation of sample augmentation techniques to enlarge the training dataset.
  • Development of the Injected Noise Dual Attention Network (INDANet), incorporating channel and spatial attention mechanisms with injected Gaussian noise for enhanced robustness.

Main Results:

  • The proposed INDANet method demonstrated superior performance compared to six other advanced models in heart sound classification tasks.
  • Achieved high accuracy rates of 99.85% on one dataset and 98.07% on another.
  • The integration of sample augmentation and injected noise significantly improved model robustness and generalization.

Conclusions:

  • The INDANet method offers a promising AI-driven solution for accurate heart sound classification, particularly beneficial for clinicians in resource-limited areas.
  • The dual attention mechanism combined with data augmentation and noise injection effectively enhances diagnostic accuracy for cardiovascular diseases.
  • This approach has the potential to significantly improve early detection and management of cardiovascular conditions globally.