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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.

Cardiovascular System Abnormal Findings II: Auscultation

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:

Imaging Studies for Cardiovascular System I:Echocardiography

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

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Related Experiment Video

Updated: Dec 21, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

A Review of Computer-Aided Heart Sound Detection Techniques.

Suyi Li¹, Feng Li¹, Shijie Tang¹

¹College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China.

Biomed Research International

|May 19, 2020

Summary

Computer-aided heart sound detection offers a noninvasive approach to predicting cardiovascular diseases. This review highlights recent advancements in signal processing and deep learning for heart sound analysis.

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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
Artificial Intelligence in Medicine

Background:

Cardiovascular diseases (CVDs) are a leading global health concern.
Heart sound analysis is a crucial noninvasive diagnostic tool for CVD prediction.
Advancements in computational methods are enhancing heart sound detection capabilities.

Purpose of the Study:

To review the latest developments in computer-aided heart sound detection techniques over the past five years.
To provide insights into the application of deep learning in heart sound analysis.
To identify future research directions for improved CVD prediction.

Main Methods:

Review of theories on heart sounds and their relation to cardiovascular diseases.
Analysis of key signal processing technologies: denoising, segmentation, feature extraction, and classification.
Emphasis on the application of deep learning algorithms in heart sound processing.

Main Results:

Recent progress in computer-aided heart sound detection has been systematically reviewed.
Key signal processing techniques and their role in analyzing heart sounds are detailed.
The significant impact and applications of deep learning in this field are highlighted.

Conclusions:

Computer-aided heart sound detection is vital for noninvasive cardiovascular disease prediction.
Deep learning shows great promise for advancing heart sound analysis.
Further research is needed to refine these techniques for clinical application.