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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)...

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.

Conduction System of the Heart

Conduction System of the Heart

Autorhythmicity is a term that refers to the heart's inherent ability to generate electrical signals and instigate muscle contractions. This self-regulating conduction system within the heart consists of two key components: the pacemaker cells and specialized conducting cells.
The pacemaker cells are located in two primary nodes: the sinoatrial (SA) node and the atrioventricular (AV) node. The SA node pacemaker cells can autonomously depolarize, triggering an action potential that leads to the...

Conduction System of the Heart

Conduction System of the Heart

The cardiac conduction system produces and transmits electrical impulses that prompt myocardial contraction, ensuring efficient heart function. This intricate system ensures that the heart beats in a coordinated and efficient manner, beginning with the atria and then the ventricles. The conduction system optimizes cardiac output by maintaining this precise sequence, which is crucial for adequate blood circulation.
This system relies on the unique properties of nodal and Purkinje cells:...

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Related Experiment Video

Updated: Nov 10, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Heart sound recognition technology based on convolutional neural network.

Ximing Huai¹, Satoshi Kitada², Dongeun Choi³

¹Graduate School of Science and Technology, Kyoto Institute of Technology, Kyoto, Japan.

Informatics for Health & Social Care

|April 5, 2021

Summary

This study introduces a novel convolutional neural network algorithm for classifying heart sound signals, achieving high accuracy. This AI-driven approach aids in early heart disease detection and diagnosis, potentially reducing mortality rates.

Keywords:

Heart disease convolutional neural network heart sound spectrogram

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Area of Science:

Cardiology
Artificial Intelligence
Biomedical Signal Processing

Background:

Rising heart disease mortality rates necessitate improved diagnostic tools.
Heart sound auscultation is a fundamental clinical skill for diagnosing cardiac conditions.
Current diagnostic methods require enhancement for greater accuracy and accessibility.

Purpose of the Study:

To develop and evaluate a convolutional neural network (CNN) algorithm for accurate heart sound signal classification.
To improve the efficiency and effectiveness of diagnosing heart disease through automated auscultation analysis.
To provide a supplementary tool for clinicians in identifying cardiac abnormalities.

Main Methods:

Collected clinical and literature-based heart sound data.
Preprocessed heart sound signals into 5-second grayscale images.
Trained and optimized a CNN model using the prepared datasets.
Evaluated the CNN model's performance on a separate test set.

Main Results:

The CNN achieved a training accuracy of 95.17% and a loss of 0.23.
Testing yielded an accuracy of 94.80%, sensitivity of 94.29%, and specificity of 95.54%.
The algorithm demonstrated superior accuracy and sensitivity compared to existing methods, with an AUC of 0.943.

Conclusions:

The proposed CNN algorithm effectively classifies heart sound signals.
This method shows significant potential for assisting in heart sound auscultation and early heart disease diagnosis.
The findings suggest a valuable application of AI in improving cardiovascular diagnostics.