Respiration sound signal recognition method and system based on visualization

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 ...

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 generally use the time-domain waveform and spectrum diagram of the signal to conduct manual analysis and judgment, and have not realized the combined analysis of automation and visualization.

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