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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...
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.

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Related Experiment Video

Updated: Jun 4, 2026

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

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

A framework for automatic heart sound analysis without segmentation.

Sumeth Yuenyong1, Akinori Nishihara, Waree Kongprawechnon

  • 1Department of Communication and Integrated Systems, Tokyo Institute of Technology, Japan 2-12-1-W9-108 Ookayama, Meguro-ku, Tokyo, Japan. toey123@gmail.com

Biomedical Engineering Online
|February 10, 2011
PubMed
Summary

A novel heart sound analysis framework effectively segments cardiac cycles despite murmur interference. This robust method achieves high accuracy in noisy conditions, offering a promising advancement in cardiovascular diagnostics.

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Area of Science:

  • Cardiovascular diagnostics
  • Biomedical signal processing

Background:

  • Heart sound analysis is challenging due to murmur interference, complicating segmentation.
  • Accurate segmentation is crucial for reliable heart sound interpretation.

Purpose of the Study:

  • To propose a new framework for robust heart sound analysis and segmentation.
  • To overcome segmentation difficulties caused by murmurs and noise.

Main Methods:

  • Cardiac cycle extraction using autocorrelation function envelopes, avoiding manual labeling of fundamental heart sounds (FHS).
  • Feature extraction via discrete wavelet transform and principal component analysis.
  • Classification using neural network bagging predictors.

Main Results:

  • The method achieved an average classification performance of 0.92 in noise-free conditions.
  • Performance remained high (0.90) under white noise (10 dB SNR) and impulse noise.
  • Demonstrated high noise robustness across various heart sound recordings.

Conclusions:

  • The proposed framework shows promising results and significant noise robustness for heart sound analysis.
  • Further validation is required with larger, diverse patient datasets to address potential biases.
  • Future work includes creating a new training set from actual patient recordings for enhanced evaluation.