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Heart Sounds01:15

Heart Sounds

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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.
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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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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.
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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification.

Philip Westphal1,2, Hongxing Luo1, Mehrdad Shahmohammadi3

  • 1Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, Netherlands.

Frontiers in Cardiovascular Medicine
|June 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method using epicardial accelerometers to accurately estimate left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) in pigs.

Keywords:
animalartificial intelligencecardiac resynchronization therapyepicardial accelerationheart soundhemodynamicsmachine learning

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Physiology
  • Machine Learning in Medicine

Background:

  • Accurate measurement of left ventricular (LV) pressure is crucial for diagnosing and managing cardiovascular diseases.
  • Current methods for measuring LV pressure can be invasive and complex.
  • Non-invasive or minimally invasive techniques are needed for continuous hemodynamic monitoring.

Purpose of the Study:

  • To develop and validate a novel method for estimating absolute left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) using data from epicardial accelerometers.
  • To assess the feasibility of employing machine learning algorithms for this estimation task.
  • To evaluate the accuracy and reliability of the proposed method under various physiological conditions.

Main Methods:

  • Acute experiments were conducted on pigs with custom-made accelerometers implanted on the epicardium.
  • Hemodynamic conditions were modulated using different pacing configurations and pharmacological agents (isoflurane, dobutamine).
  • Machine learning models (bootstrap aggregated classification tree ensembles) were trained using acceleration signal features to estimate LVPmax and LV dP/dtmax.

Main Results:

  • The algorithm identified optimal features from acceleration data for pressure estimation.
  • High accuracies were achieved for estimating LVPmax (93% at 20 mmHg interval) and LV dP/dtmax (93% at 100 mmHg/s interval).
  • Estimation accuracies were consistent across different accelerometer locations.

Conclusions:

  • The developed method reliably estimates absolute LV pressure and its first derivative using epicardial accelerometers and machine learning.
  • This approach shows promise for pre-clinical assessment of cardiac function.
  • Further research may explore its potential for clinical applications in hemodynamic monitoring.