Digital lung sound characteristic dimension reducing method based on relevance vector machine

Abstract

The invention discloses a digital lung sound characteristic dimension reducing method based on a relevance vector machine, wherein the method belongs to the field of digital medical technology. According to the method of the invention, a characteristic space is mapped to a sample space; a mutual information characteristic kernel function is utilized for representing association strength between lung sound sample characteristic attributes; and finally dimension reduction of the lung sound characteristic is finally realized. The method comprises the steps of selecting lung sound data from a lung sound database as a lung sound sample, and establishing the relevance vector machine; establishing a lung sound characteristic vector sample set; and reducing the dimension based on an RVM lung sound characteristic vector. The digital lung sound characteristic dimension reducing method has advantages of high robustness, sparsity, low sensitivity to data noise, effective reduction of uncertainty caused by acquired noise, and high suitability for processing high-dimension lung sound characteristics. According to the digital lung sound characteristic dimension reducing method, mutual information between the lung sound characteristics is utilized as a kernel function; association strength between the characteristics is sufficiently considered in the model; not only is conciseness of a dimension reduction result ensured, but also completeness of reserved characteristics is ensured.

Description

technical field [0001] The invention belongs to the technical field of digital medical treatment, relates to the field of digital diagnosis and treatment of lungs, and in particular relates to a method for reducing the features contained in lung sounds by using a correlation vector machine, so as to provide an accurate data source for diagnosis by using digital lung sounds. Background technique [0002] Lung sounds contain a wealth of pathological information, and the use of lung sounds to detect the health of the human lung has the advantages of non-invasiveness to the subject and predictability of the disease. The use of collected digital lung sounds for lung diagnosis has the characteristics of intelligence, which is called lung sound recognition internationally. For the study of lung sound recognition, domestic and foreign scholars have done a lot of research work and put forward some practical theories and methods. In the field of pattern recognition, theories and metho...

AI Technical Summary

Problems solved by technology

PCA, LDA, and ICA methods all use linear changes to reduce the dimensionality of features, and are not suitable for dealing with features with nonlinear relationships between attributes.

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