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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.
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin to...
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...

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Related Experiment Video

Updated: Jun 19, 2026

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

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

The moment segmentation analysis of heart sound pattern.

Zhonghong Yan1, Zhongwei Jiang, Ayaho Miyamoto

  • 1Biomedical Department, ChongQing Institute of Technology, China. yzh816@msn.com

Computer Methods and Programs in Biomedicine
|October 27, 2009
PubMed
Summary

This study introduces a new method using the Viola integral waveform to analyze heart sounds, enabling faster and more accurate segmentation of heart sound cycles (S1 and S2). The approach utilizes characteristic waveform and multi-scale moment analysis for improved automated detection.

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

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Last Updated: Jun 19, 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 Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Accurate segmentation of heart sounds (S1 and S2) is crucial for diagnosing cardiac conditions.
  • Traditional methods for heart sound segmentation can be complex and time-consuming.

Purpose of the Study:

  • To develop a novel, fast, and accurate method for segmenting heart sound cycles.
  • To apply the Viola integral waveform method combined with multi-scale moment analysis for heart sound analysis.

Main Methods:

  • Application of the Viola integral waveform method to analyze electric stethoscope recordings.
  • Multi-scale moment analysis to locate heart sound cycles using characteristic waveform (CW) and characteristic moment waveform (CMW).
  • Identification of local extreme points (LEPs) on CMW for cycle detection and segmentation of S1 and S2 sounds.

Main Results:

  • A fast algorithm for calculating CW and CMW with calculation time independent of scale.
  • Efficient identification of heart sound cycles via CMW's LEPs.
  • High accuracy in segmenting S1 and S2 sounds using LEPs and CW information.
  • Validated optimal parameters: time scale delta=0.05s for CW and l=0.45s for CMW.

Conclusions:

  • The proposed method enables fast and automated segmentation of heart sound signals with precise boundaries.
  • The rhythm-based analysis of CMW curves results in a higher success rate compared to wavelet methods.
  • The new approach is simpler and faster than existing wavelet-based segmentation techniques.