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Related Concept Videos

Heart Sounds01:15

Heart Sounds

4.0K
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)...
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Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

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Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
2.9K
Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

1.8K
In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
1.8K
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:
721
Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

2.3K
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.
2.3K
Sound as Pressure Waves01:17

Sound as Pressure Waves

4.7K
Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
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Related Experiment Video

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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A lung sound classification system based on the rational dilation wavelet transform.

Sezer Ulukaya, Gorkem Serbes, Ipek Sen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new wavelet transform for classifying lung sounds, achieving high accuracy in distinguishing normal, crackle, and wheeze respiratory conditions. The advanced Rational Dilation Wavelet Transform improves upon traditional methods for respiratory sound analysis.

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    Area of Science:

    • Medical signal processing
    • Respiratory acoustics

    Background:

    • Accurate classification of respiratory sounds (crackle, normal, wheeze) is crucial for diagnosing lung conditions.
    • Previous wavelet-based systems used low Q-factor wavelets, limiting frequency resolution and performance with oscillatory signals.

    Purpose of the Study:

    • To develop and evaluate a novel wavelet-based classification system for respiratory sounds.
    • To improve the accuracy and robustness of lung sound classification using tunable Q-factor wavelets.

    Main Methods:

    • Implementation of a classification system utilizing the Rational Dilation Wavelet Transform (RDWT) with tunable Q-factors.
    • Feature extraction using an energy feature subset.
    • Classification of crackle, normal, and wheeze lung sound signals.

    Main Results:

    • The proposed system achieved high classification accuracies: 95% for crackle, 97% for wheeze, and 93.50% for normal lung sounds.
    • The overall accuracy for total sound signals reached 95.17%.
    • The RDWT approach demonstrated superiority over conventional low Q-factor wavelet analysis.

    Conclusions:

    • The tunable Q-factor Rational Dilation Wavelet Transform offers a more effective method for respiratory sound classification.
    • This advanced wavelet analysis enhances the discrimination of various lung sound types, improving diagnostic potential.