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Related Concept Videos

Cardiovascular System Abnormal Findings II: Auscultation01:25

Cardiovascular System Abnormal Findings II: Auscultation

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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:
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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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Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
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Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

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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.
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.
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Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

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Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
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Pulse rhythm01:30

Pulse rhythm

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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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Updated: Jan 15, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial Intelligence.

Melissa Valaee1, Shahram Shirani2

  • 1Faculty of Health Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada.

Diagnostics (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study introduces an AI model for early heart murmur detection using Mel spectrograms and a Vision Transformer. The AI model significantly improves diagnostic accuracy and efficiency for cardiovascular disease screening.

Keywords:
artificial intelligenceheart murmur detectionheart valvesmachine learningphonocardiogram

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Cardiovascular disease is the leading cause of global mortality, necessitating early detection.
  • Cardiac auscultation is a primary method for identifying heart murmurs, indicative of cardiac conditions.
  • Current diagnostic methods can be improved for accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate an AI model for streamlining cardiac auscultation.
  • To enhance the accuracy and efficiency of heart murmur detection.
  • To support early diagnosis of cardiovascular conditions.

Main Methods:

  • Utilized phonocardiogram recordings from the 2022 PhysioNet Heart Sound Classification Challenge.
  • Processed audio data into Mel spectrograms and input them into a pre-trained Vision Transformer and MiniROCKET model.
  • Applied data augmentation to expand the dataset from 928 to 14,848 spectrograms.

Main Results:

  • The AI model demonstrated superior quality assessment metrics (Weighted Accuracy, Sensitivity, F-Score) compared to existing methods.
  • Achieved a rapid evaluation speed of 0.02 seconds per patient.
  • Significantly enhanced diagnostic performance in heart murmur detection.

Conclusions:

  • The AI model can supplement physician diagnosis for heart murmurs.
  • Facilitates earlier detection of cardiovascular conditions.
  • Offers increased scalability and adaptability for clinical use.