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Heart Sounds01:15

Heart Sounds

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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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Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.

Sensors (Basel, Switzerland)ยท2017
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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

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Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy.

Xiefeng Cheng1,2, Pengfei Wang1, Chenjun She1

  • 1College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

A novel biometric method uses multimodal multiscale dispersion entropy to characterize heart sounds, achieving high recognition accuracy. This technique effectively identifies individuals based on unique heart sound patterns.

Keywords:
Fisher ratioICEEMDANRCMDEbiometric characterizationheart sound

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Heart sound analysis is crucial for diagnosing cardiac conditions.
  • Current biometric identification methods often lack robustness.
  • Developing accurate and non-invasive biometric techniques is essential.

Purpose of the Study:

  • To propose a novel biometric characterization of heart sounds using multimodal multiscale dispersion entropy.
  • To evaluate the effectiveness of the proposed method in heart sound recognition.
  • To optimize the method for practical application by analyzing the impact of different cycle starting positions.

Main Methods:

  • Heart sounds were periodically segmented and decomposed into intrinsic mode functions (IMFs) using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN).
  • Refined composite multiscale dispersion entropy (RCMDE) was calculated for feature representation.
  • Heart sound segmentation utilized logistic regression (LR) and hidden semi-Markov models (HSMM), with feature selection via Fisher ratio (FR).
  • Identification employed Euclidean distance (ED) and the close principle.

Main Results:

  • The proposed method achieved a recognition accuracy rate of 96.08% in simulation experiments on open databases.
  • Experiment II, using a custom database, demonstrated that starting the single-cycle heart sound analysis from the beginning of the first heart sound (S1) yielded the highest recognition rate of 97.5%.
  • The method proved effective for heart sound biometric recognition.

Conclusions:

  • The multimodal multiscale dispersion entropy approach provides an effective and accurate method for heart sound biometric recognition.
  • Optimizing the segmentation and starting point of single-cycle heart sounds enhances recognition performance.
  • This technique holds potential for practical applications in biometric identification systems.