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Related Concept Videos

Heart Sounds01:15

Heart Sounds

1.8K
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.
197
Classification of Signals01:30

Classification of Signals

381
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...
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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:
98
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
622
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.3K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Related Experiment Video

Updated: May 30, 2025

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

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A new HCM heart sound classification method based on weighted bispectrum features.

Fang Yu1, Huang Zhiyuan1, Leng Hongxia1

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China.

Physical and Engineering Sciences in Medicine
|January 30, 2025
PubMed
Summary

Early diagnosis of hypertrophic cardiomyopathy (HCM) is crucial for preventing sudden cardiac arrest. This study introduces a novel heart sound analysis method for accurate HCM classification in young patients.

Keywords:
Adaptive threshold weighting mutual informationBispectrum featuresCNN-RFHeart soundHypertrophic cardiomyopathy

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Ultrasonic Assessment of Myocardial Microstructure
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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Hypertrophic cardiomyopathy (HCM), encompassing obstructive and non-obstructive forms, poses a risk of sudden cardiac arrest in adolescents and athletes.
  • Early detection via heart sound auscultation is vital for preventing adverse events, yet differentiating HCM types based on pressure gradients is challenging.
  • Current diagnostic methods may lack the efficiency and cost-effectiveness required for widespread screening.

Purpose of the Study:

  • To develop an efficient and cost-effective classification method for hypertrophic cardiomyopathy (HCM) using heart sound (HS) analysis.
  • To accurately distinguish between normal, obstructive HCM, and non-obstructive HCM using advanced signal processing techniques.
  • To provide a reliable tool for the early diagnosis of HCM in young individuals.

Main Methods:

  • Heart sounds (HSs) were preprocessed to remove background noise.
  • Bispectrum contour maps were generated, and 56-dimensional features were extracted to capture pathological information.
  • An adaptive threshold weighting mutual information method was employed for feature selection and weighted fusion.
  • A Convolutional Neural Network-Random Forest (CNN-RF) classifier was developed for automated HCM type identification.

Main Results:

  • The proposed method achieved a high classification accuracy of 94.4% on a clinical dataset.
  • The technique successfully differentiated between normal heart sounds and the two types of HCM.
  • Feature extraction and selection methods effectively represented pathological information from heart sounds.

Conclusions:

  • The developed heart sound analysis method offers a reliable and accurate approach for diagnosing hypertrophic cardiomyopathy (HCM) in clinical settings.
  • This technique provides a cost-effective solution for early HCM detection, particularly in young patients.
  • The findings support the use of advanced signal processing and machine learning for non-invasive cardiac diagnostics.