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Heart Sounds

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)...

Classification of Signals

Classification of Signals

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...

Assessment of the Cardiovascular System IV: Auscultation

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.

Hearing

Hearing

When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.

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Related Experiment Video

Updated: Jul 7, 2025

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Published on: June 6, 2012

[Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network].

Qin Wang¹, Hongbo Yang^2,3, Jiahua Pan³

¹School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|December 28, 2023

Summary

This study introduces a novel feature extraction method combining mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD) for improved heart sound classification. The developed algorithm shows high accuracy in diagnosing congenital heart disease.

Keywords:

Classification of heart sounds Congenital heart disease Fuzzy neural network Mel-frequency cepstral coefficients Power spectral density

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Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

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Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Context:

Heart sound classification is crucial for diagnosing cardiac conditions.
Accurate feature extraction and classifier selection are key challenges.
Existing methods may not fully capture pathological heart sound features.

Purpose:

To develop an advanced feature extraction method for heart sound signals.
To evaluate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) as a classifier.
To identify optimal parameters for feature extraction, specifically combining MFCC and PSD.

Summary:

A novel feature extraction technique combining mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD) was developed.
The adaptive neuro-fuzzy inference system (ANFIS) was employed as the classifier.
Experimental analysis identified median PSD and MFCC within the 100-300 Hz systolic period as optimal features, achieving 96.50% accuracy.

Impact:

The proposed method demonstrates significant potential for aiding in the diagnosis of congenital heart disease.
Achieved high performance metrics including accuracy (96.50%), precision (99.27%), sensitivity (93.35%), specificity (99.60%), and F1 score (96.35%).
Offers a promising tool for non-invasive cardiac diagnostics and clinical decision support.