Related Concept Videos
Cardiovascular System Abnormal Findings II: Auscultation
Abnormal Heart Sounds
Gallops:
Heart Sounds
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)...
Equipments Used To Measure Blood Pressure
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...
Assessment of the Cardiovascular System IV: Auscultation
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.
Aortic Regurgitation II: Clinical Features and Diagnostic Tests
Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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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.
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.
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.


