A Pathologically Adaptive Heart Sound Total Variational Filtering Method

Abstract

The invention relates to a pathologically self-adaptive heart sound full-variation filtering method, which belongs to the technical field of medical computing biometric feature recognition; the method first adds additive Gaussian white noise to the input original heart sound signal, and screens out the added noise that meets the requirements Then the improved empirical mode decomposition is performed on the above-mentioned noise-added signal, and the eigenmode function at the time of maximum similarity is obtained by screening, and the original heart sound signal is segmented; the original heart sound signal is calculated using the tracking evolution algorithm based on linear coefficients. The information entropy of the segment, and use the information entropy as the weight value to construct the l of the heart sound signal 1 and limit l 1 Regularize the total variational equation, and use the improved Split-Bregman algorithm to solve it separately, and finally use the moving average method to obtain the heart sound signal that combines the results of the two filters. Compared with the prior art, the present invention can perform more accurate denoising processing on the original heart sound signal, and can realize the preservation of pathological information, providing a basis for digital analysis of the phonocardiogram.

Description

technical field [0001] The invention relates to a heart sound full variation filtering method based on pathological self-adaptation, in particular to a heart sound filtering method which integrates different pathological features of heart sound and full variation filtering, and belongs to the technical field of medical computing biological feature recognition. Background technique [0002] For a long time, cardiovascular disease has seriously threatened human health and life because of its high morbidity and high mortality. According to the World Health Organization (WHO), 36 million people die from non-communicable diseases such as cardiovascular diseases, diabetes, respiratory diseases and malignant tumors every year in the world, accounting for 2 / 3 of the total global deaths. By 2020, The number will climb to 44 million. [0003] "China Cardiovascular Disease Report (2018)" shows that in 2016, the mortality rate of cardiovascular disease in China still ranked first in th...

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Problems solved by technology

[0006] However, the traditional frequency-domain noise reduction methods (Butterworth filter, Chebyshev filter, etc.) have the following problems: (1) Pathological noises are not considered during denoising, such as hyperactivity and beating in mitral valve stenosis When the amplitude of the S3 segment is high, it will be misdiagnosed as the noise of the measurement and filtered out; (2) when the heart sound is collected by mobile , due to the close distance between the heart and lungs, the mitral valve area is easily affected by the left lung, and the heart sounds and lung sounds are aliased. Since the frequencies of the two are similar, the denoising algorithm based on frequency domain analysis cannot effectively remove the lung sounds

[0007] And at present, because the accuracy and reliability of the intelligent analysis of phonocardiogram cannot be compared with the auscultation analysis of cardiologists, so its proportion in actual clinical application is not large

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