Respiration sound signal recognition method and system based on visualization

A signal recognition and breath sound technology, which can be used in the evaluation of respiratory organs, medical science, stethoscope, etc. It can solve the problems of lack of visualization, weak robustness, and lack of automation and visualization.

Active Publication Date: 2020-01-31
SOUTH CHINA NORMAL UNIVERSITY +1
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AI Technical Summary

Problems solved by technology

The features extracted by these methods are not visualized, and the robustness is not strong, and the information for clinicians is of little reference value
The methods currently used gen

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  • Respiration sound signal recognition method and system based on visualization
  • Respiration sound signal recognition method and system based on visualization
  • Respiration sound signal recognition method and system based on visualization

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Embodiment Construction

[0032]The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly.

[0033] see figure 1 As shown, a visualization-based breathing sound signal recognition method includes: S1, collecting the original breathing sound signal, filtering and separating the signal to obtain a pre-processed breathing sound signal; S2, performing periodic division on the pre-processing breathing sound signal to obtain Set the breath sound signal of the cycle, and determine the segmentation point of the breath sound signal; S3, perform Fourier transform on the breath sound signal of the set cycle, and obtain the frequency information of the breath sound signal; S4, according to the segmentation of the breath sound cycle signal point, proc...

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Abstract

The invention relates to the field of audio signal identification. The invention discloses a method and a system for identifying respiratory sound signals based on visualization, the short-time Fourier transform is used to perform the time-frequency analyze of the cut respiratory sound period signal, and the one-dimensional audio signal is converted into the two-dimensional visual signal. Throughthe image processing and analysis, the data set is formed, and the convolution neural network picture classification is carried out to realize the distinction between normal and three pathological respiratory sounds. Pathological respiratory sound signal murmur is obvious, the murmur formed during exhalation and inhalation has special spectral information. A time-frequency analysis method is used,the short-time Fourier transform is used to perform the time-frequency analyze of the cut respiratory sound period signals, and the one-dimensional audio signal is converted into the two-dimensionalvisual signal. Through the processing and analysis of the images, a data set is formed, and the visual images are classified based on convolution neural network to distinguish normal and three pathological respiratory sounds.

Description

technical field [0001] The present invention relates to the field of audio signal recognition, and more specifically, to a visualization-based breathing sound signal recognition method and system. Background technique [0002] The breath sound signal is a physiological signal produced by the human respiratory system and the outside world during the ventilation process. Breath sounds contain a lot of physiological and pathological information, which can well reflect the health of the human respiratory system, so it has very important research significance in breath sounds and clinical medicine. In recent years, the increase in the incidence of respiratory diseases caused by environmental problems such as frequent smog weather has also greatly increased the demand for the rapidity and accuracy of respiratory disease diagnosis. [0003] Cardiopulmonary auscultation has aroused people's widespread attention again due to its excellent features such as quickness, convenience and ...

Claims

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Application Information

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IPC IPC(8): A61B5/08A61B5/00A61B7/04
CPCA61B5/08A61B5/7257A61B5/7267A61B7/04
Inventor 张金区欧建荣宋立国罗虎鲁玉佳钱朗
Owner SOUTH CHINA NORMAL UNIVERSITY
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