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

Classification of Signals01:30

Classification of Signals

575
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...
575
Heart Sounds01:15

Heart Sounds

2.1K
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)...
2.1K
Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
Classification of Systems-I01:26

Classification of Systems-I

236
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
236
Classification of Systems-II01:31

Classification of Systems-II

192
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
192
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

662
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...
662

You might also read

Related Articles

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

Sort by
Same author

The Impacts of Diabetes Mellitus on Clinical Outcomes of Hospitalization Following Craniotomy for Brain Tumor.

Clinical Medicine Insights. Oncology·2026
Same author

[Targeted inhibition of miR-503 upregulates Apelin expression to alleviate myocardial infarction in mice].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026
Same author

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026
Same author

Effective Gaussian Management for High-fidelity Scene Reconstruction.

IEEE transactions on visualization and computer graphics·2026
Same author

A Dual-branch Network with Cross-scale Feature Interaction and Alignment for Weakly Supervised Whole Slide Image Analysis.

IEEE journal of biomedical and health informatics·2026
Same author

Artificial intelligence empowered coronary artery imaging: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Aug 3, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

Junxin Chen, Zhihuan Guo, Xu Xu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 7, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust neural network for automatic heart sound classification, improving early heart disease detection. The method uses an enhanced attention module and STFT spectrum analysis for accurate, accessible diagnosis, especially in remote areas.

    More Related Videos

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.0K
    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

    152

    Related Experiment Videos

    Last Updated: Aug 3, 2025

    Semi-automated Optical Heartbeat Analysis of Small Hearts
    12:10

    Semi-automated Optical Heartbeat Analysis of Small Hearts

    Published on: September 16, 2009

    12.3K
    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.0K
    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

    152

    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Manual heart sound analysis for disease detection requires expert clinicians, limiting accessibility and increasing diagnostic uncertainty, particularly in underserved regions.
    • Automated methods are needed to improve the accuracy, consistency, and accessibility of early heart disease detection through heart sound analysis.

    Purpose of the Study:

    • To develop and validate a robust neural network with an improved attention module for the automatic classification of heart sound waves.
    • To enhance the accuracy and reliability of heart sound analysis for early heart disease detection.

    Main Methods:

    • Heart sound recordings were preprocessed using a Butterworth bandpass filter for noise removal.
    • Time-frequency spectrums were generated using Short-Time Fourier Transform (STFT) to drive the neural network model.
    • An improved attention module, combining Squeeze-and-Excitation and coordinate attention mechanisms, was developed for feature fusion.

    Main Results:

    • The proposed neural network effectively classified heart sound waves using STFT spectrums and extracted features.
    • The improved attention module enhanced feature fusion, contributing to the model's classification performance.
    • Validation on public datasets demonstrated the method's effectiveness and advantages over existing approaches.

    Conclusions:

    • The developed neural network offers a robust and effective solution for automatic heart sound classification.
    • This approach has the potential to improve early heart disease detection, increasing accessibility in medically underserved areas.
    • The integration of advanced attention mechanisms and data balancing techniques addresses key challenges in automated cardiac auscultation.