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Related Concept Videos

Korotkoff Sounds01:12

Korotkoff Sounds

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Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...
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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.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
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Soundness of Cement01:17

Soundness of Cement

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The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Sound Waves01:01

Sound Waves

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Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
Sound waves are longitudinal in most fluids because fluids cannot sustain any lateral pressure. In solids, however, shear forces help in propagating the disturbance in the lateral direction as well....
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Sound Intensity00:58

Sound Intensity

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Related Experiment Video

Updated: Feb 8, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

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Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.

Yan Shi1,2,3, Guoliang Wang4, Jinglong Niu1

  • 1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

International Journal of Biological Sciences
|July 11, 2018
PubMed
Summary

This study introduces an automated method for detecting sputum in respiratory sounds using wavelet transform and artificial neural networks (ANN). The system achieved an 84.53% precision rate, improving efficiency for intensive care unit staff.

Keywords:
Artificial neural networkAuscultationDiscrete wavelet transformRespiratory system diagnosisSputum sound analysis

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment
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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Sputum deposition in the respiratory system requires timely clearance, especially in mechanically ventilated patients.
  • Intensive care unit (ICU) staff efficiency can be enhanced by automated monitoring of respiratory secretions.
  • Accurate detection of sputum is crucial for effective respiratory care.

Purpose of the Study:

  • To develop an automated method for detecting sputum using sound signals.
  • To improve the efficiency of secretion clearance in mechanically ventilated patients.
  • To optimize the performance and design of automatic sputum detection technology.

Main Methods:

  • Sputum sound signals were analyzed using wavelet transform for feature extraction.
  • Artificial Neural Network (ANN) with Back Propagation (BP) algorithm was employed for classification.
  • Features were extracted from frequency subbands representing wavelet coefficient distribution.

Main Results:

  • The proposed method achieved a maximum precision rate of 84.53% in automatic sputum sound recognition.
  • Wavelet transform effectively decomposed sputum sound signals into relevant frequency subbands.
  • The ANN system successfully classified the existence of sputum sounds based on extracted features.

Conclusions:

  • The developed automated system shows significant potential for sputum detection using respiratory sound analysis.
  • This technology can aid ICU staff in timely secretion management for ventilated patients.
  • Further optimization in performance and design can enhance the reliability of automated sputum detection.