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Related Concept Videos

Heart Sounds01:15

Heart Sounds

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) valves at the...
Heart Valves01:16

Heart Valves

The human heart is a complex organ with an intricate system of valves that regulate blood flow. There are two main types of valves: atrioventricular (AV) valves and semilunar valves.
The AV valves prevent the backflow of blood from the ventricles to the atria during ventricular contraction. These valves function with the assistance of the chordae tendineae and papillary muscles. When the ventricles are relaxed, the chordae tendineae are slack, allowing blood to flow from the atria into the...
Cardiovascular System Abnormal Findings II: Auscultation01:25

Cardiovascular System Abnormal Findings II: Auscultation

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:
Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

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.
Mitral Regurgitation I: Introduction01:20

Mitral Regurgitation I: Introduction

Mitral regurgitation is characterized by the backward circulation of blood from the left ventricle to the left atrium during systole, a phase of the cardiac cycle when the heart contracts and pumps blood out of the chambers. This abnormal flow occurs primarily due to the dysfunction of the mitral valve or its supporting structures, which include the mitral leaflets, chordae tendineae, annulus, and papillary muscles.Etiology and Mechanisms:Primary Mitral Regurgitation: This type arises from...
Anatomy of the Heart01:27

Anatomy of the Heart

The human heart is made up of three layers of tissue that are surrounded by the pericardium, a membrane that protects and confines the heart. The outermost layer, closest to the pericardium, is the epicardium. The pericardial cavity separates the pericardium from the epicardium. Beneath the epicardium is the myocardium, the middle layer, and the endocardium, the innermost layer. There are four chambers of the heart: the right atrium, the right ventricle, the left atrium, and the left ventricle.

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Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice
12:12

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice

Published on: February 14, 2017

A robust heart sound segmentation algorithm for commonly occurring heart valve diseases.

S Ari1, P Kumar, G Saha

  • 1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India. samit.ari@gmail.com

Journal of Medical Engineering & Technology
|July 30, 2008
PubMed
Summary

This study introduces an automatic heart sound segmentation method for detecting valvular heart disease. The novel approach accurately identifies heart sounds without requiring an electrocardiographic (ECG) signal, improving efficiency and accuracy.

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Semi-automated Optical Heartbeat Analysis of Small Hearts
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Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

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Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice
12:12

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice

Published on: February 14, 2017

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

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Accurate segmentation of heart sounds (phonocardiogram) is crucial for diagnosing valvular heart diseases.
  • Existing methods often rely on electrocardiographic (ECG) signals, necessitating complex instrumentation.
  • There is a need for simpler, more efficient segmentation techniques.

Purpose of the Study:

  • To develop an automatic heart sound segmentation algorithm that does not require an auxiliary ECG signal.
  • To improve the accuracy and reduce the computational complexity of heart sound segmentation.
  • To evaluate the algorithm's performance across various conditions and pathological cases.

Main Methods:

  • Developed an automatic segmentation algorithm utilizing biomedical domain features.
  • The algorithm processes phonocardiogram signals without requiring simultaneous ECG recordings.
  • Performance was evaluated using nine pathological heart sound cases and normal heart sounds.

Main Results:

  • Achieved an overall accuracy of 97.47% in heart sound segmentation.
  • Demonstrated superior performance compared to two competing techniques.
  • Showcased robustness against additive white Gaussian noise at various signal-to-noise ratio (SNR) levels.

Conclusions:

  • The proposed automatic segmentation method offers a more accurate and computationally efficient alternative for heart sound analysis.
  • Eliminating the need for ECG signals simplifies instrumentation and broadens applicability.
  • The algorithm shows promise for reliable detection of valvular heart diseases.