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Related Concept Videos

Heart Sounds01:15

Heart Sounds

2.5K
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)...
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Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

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Cardiac auscultation is a clinical skill used to assess heart function and detect abnormalities. It involves listening to heart sounds at specific anatomical locations through a stethoscope.
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.
815
Cardiovascular System Abnormal Findings II: Auscultation01:25

Cardiovascular System Abnormal Findings II: Auscultation

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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:
276
Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies

109
The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
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Mitral Stenosis II: Clinical features and Diagnostic Tests01:23

Mitral Stenosis II: Clinical features and Diagnostic Tests

43
Mitral stenosis is a heart condition in which the mitral valve, which allows blood to flow from the left atrium to the left ventricle, becomes narrowed or stenotic. This narrowing hinders blood flow and leads to clinical symptoms requiring specific medical evaluations and management strategies. The following overview outlines the clinical symptoms, assessments, diagnostic findings, prevention methods, and treatments for mitral stenosis.Clinical ManifestationsDyspnea (shortness of breath): This...
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Related Experiment Video

Updated: Oct 9, 2025

Echocardiographic Assessment of Cardiac Anatomy and Function in Adult Rats
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Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis.

Yang Yang1, Xing-Ming Guo1, Hui Wang1

  • 1Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, China.

Diagnostics (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel non-invasive method using heart sounds and deep learning for early diagnosis of left ventricular diastolic dysfunction (LVDD). The approach utilizes data augmentation to improve diagnostic accuracy, offering a promising tool for heart failure prediction.

Keywords:
convolutional neural networkdeep convolutional generative adversarial networksdiagnosisheart soundsleft ventricular diastolic dysfunction

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Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
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Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Left ventricular diastolic dysfunction (LVDD) can lead to heart failure with preserved ejection fraction.
  • Early diagnosis of LVDD is crucial for effective management and treatment.
  • Non-invasive diagnostic methods are highly desirable for patient convenience and widespread screening.

Purpose of the Study:

  • To develop and validate a non-invasive method for the early diagnosis of LVDD.
  • To leverage deep learning, specifically convolutional neural networks (CNNs), and heart sound (HS) analysis.
  • To enhance the diagnostic model's performance using data augmentation techniques.

Main Methods:

  • Heart sound (HS) signals were preprocessed using an improved wavelet denoising method.
  • HS signals were segmented using a logistic regression-based hidden semi-Markov model.
  • Spectrograms were generated for data augmentation (DA) via short-time Fourier transform (STFT), employing a deep convolutional generative adversarial network (DCGAN) model.
  • The proposed CNN model was trained and evaluated against established deep learning architectures.

Main Results:

  • The proposed method achieved high diagnostic performance for LVDD.
  • Achieved accuracy of 0.987, sensitivity of 0.986, and specificity of 0.988.
  • Demonstrated the effectiveness of the DCGAN-based DA method in augmenting HS data for improved model training.

Conclusions:

  • Heart sound analysis combined with CNNs provides an effective non-invasive approach for early LVDD diagnosis.
  • The DCGAN-based data augmentation significantly improves the performance of LVDD detection models.
  • This methodology shows promise for early detection and management of heart failure with preserved ejection fraction.