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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...
Cardiovascular System Abnormal Findings II: Auscultation01:25

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:
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
Pulse rhythm01:30

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 muscle...
Pulse01:16

Pulse

When the heart pumps blood out, arterial elastic fibers play a crucial role in sustaining a high-pressure gradient. They expand to accommodate the received blood and then recoil - a process known as the pulse that can be either manually palpated or electronically quantified. Despite a reduction in its effect with increased distance from the heart, elements of the pulse's systolic and diastolic components persist, observable even at the arteriole level.
The pulse serves as a clinical indicator...
Classification of Signals01:30

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...

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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Wavelet packet entropy for heart murmurs classification.

Fatemeh Safara1, Shyamala Doraisamy, Azreen Azman

  • 1Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Islamshahr, Tehran 3314767653, Iran ; Faculty of Computer Science and Information Technology, 43400 Serdang, Selangor Darul Ehsan, Malaysia.

Advances in Bioinformatics
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wavelet packet entropy for classifying heart sounds and murmurs. The new method achieved 96.94% accuracy, showing promise for diagnosing cardiac valve disorders.

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Heart murmurs indicate cardiac valve disorders, necessitating accurate detection methods.
  • Automated analysis of heart sounds using audio features is an active research area.
  • Entropy has shown potential in distinguishing normal heart sounds from murmurs.

Purpose of the Study:

  • To introduce a new entropy measure for heart sound analysis.
  • To evaluate the feasibility of this new entropy for classifying five types of heart sounds and murmurs.
  • To assess the effectiveness of the proposed method in diagnosing common cardiac valve disorders.

Main Methods:

  • Heart sound analysis using Wavelet Packet Transform (WPT).
  • Calculation of a novel entropy measure from WPT coefficients.
  • Derivation of feature vectors using the calculated entropy.
  • Classification of five heart sound types, including four common murmurs (aortic regurgitation, mitral regurgitation, aortic stenosis, mitral stenosis).
  • Evaluation using BayesNet classifier.

Main Results:

  • The proposed wavelet packet entropy effectively derived feature vectors for heart sound classification.
  • Five distinct classification tasks were performed to assess feature discriminatory power.
  • The BayesNet classifier achieved the highest accuracy of 96.94% using the proposed features.
  • The novel entropy measure demonstrated significant potential in distinguishing between normal and abnormal heart sounds.

Conclusions:

  • The developed wavelet packet entropy is effective for classifying heart sounds and murmurs.
  • This approach shows promise for the early detection and diagnosis of cardiac valve disorders.
  • The findings support the clinical utility of advanced signal processing techniques in cardiology.