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

3.2K
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)...
3.2K
Pulse rhythm01:30

Pulse rhythm

1.3K
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
1.3K
Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

3.1K
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...
3.1K
Cardiovascular System Abnormal Findings II: Auscultation01:25

Cardiovascular System Abnormal Findings II: Auscultation

562
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:
562
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

2.4K
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:
2.4K
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

725
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,...
725

You might also read

Related Articles

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

Sort by
Same author

An Explainable Plane-Wise ConvNet Approach for Detecting Femoral Head Osteonecrosis from Magnetic Resonance Images.

Bioengineering (Basel, Switzerland)·2026
Same author

Hydroxytyrosol suppressed the nephrotoxicity of cisplatin while supporting its therapeutic potential in a rat model of hepatocellular carcinoma.

Drug and chemical toxicology·2026
Same author

DTF-STCANet: A Dual Time-Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification.

Diagnostics (Basel, Switzerland)·2026
Same author

Time-Varying Prognostic Impact of the Age×BUN/LVEF Index on Long-Term MACCE After ST-Elevation Myocardial Infarction.

Journal of cardiovascular development and disease·2026
Same author

Acute Coronary Syndrome in Intensive Care Unit Patients: Troponin or Triglyceride Glucose Index Levels?

Journal of clinical medicine·2025
Same author

Automatic Detection of Occluded Main Coronary Arteries of NSTEMI Patients with MI-MS ConvMixer + WSSE Without CAG.

Diagnostics (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jan 16, 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

930

Improving the Detection Performance of Cardiovascular Diseases from Heart Sound Signals with a New Deep

Ozgen Safak1, Mehmet Tolga Hekim1, Tolga Cakmak2

  • 1Clinics of Cardiology, Balıkesir University, Balıkesir 10185, Turkey.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study presents an AI-powered system for diagnosing cardiovascular diseases using heart sound analysis. The novel approach achieves over 98% accuracy, enabling early detection and reducing mortality risk.

Keywords:
NRBMI algorithmPCG signalsRAMM modelcardiovascular diseases

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

3.0K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.8K

Related Experiment Videos

Last Updated: Jan 16, 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

930
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

3.0K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.8K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of mortality.
  • Early diagnosis of CVDs is crucial for mitigating adverse outcomes.
  • Traditional heart sound auscultation requires expert interpretation, limiting accessibility.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based decision support system for diagnosing cardiovascular diseases.
  • To leverage phonocardiogram (PCG) signals for automated CVD detection.
  • To enhance the accuracy and reliability of heart sound analysis.

Main Methods:

  • Utilized the 2016 PhysioNet/CinC Challenge dataset of PCG signals.
  • Applied spectrogram image transformation for enhanced signal representation.
  • Employed a deep learning model (residual and attention blocks, MLP-mixer) for feature extraction.
  • Developed a hybrid feature selection algorithm (NCA and ReliefF).
  • Classified features using a Support Vector Machine (SVM) algorithm.

Main Results:

  • The proposed AI system achieved over 98% accuracy in diagnosing cardiovascular diseases.
  • High performance was consistently observed across all evaluated metrics: accuracy, sensitivity, specificity, precision, and F1-score.
  • The combined feature selection and deep learning approach proved effective.

Conclusions:

  • An accurate AI-based decision support system for cardiovascular disease detection has been successfully developed.
  • The system demonstrates high potential for improving early diagnosis and patient outcomes.
  • This AI approach offers a scalable and reliable method for analyzing heart sounds.