Heart sound diagnosis system with deep learning and low-difference forest mixed

Abstract

The invention provides a heart sound diagnosis system with deep learning and a low-difference forest mixed. The system comprises a preprocessing module configured to preprocess acquired heart sound signals, and normalize, filter and down-sample the heart sound signals in sequence; a data conversion module configured to extract audio features of the down-sampled data to generate second-order spectrum data; a deep learning module configured to perform feature extraction on the second-order spectrum data by using a trained deep learning model; and a feature classification module configured to classify the extracted features by using a trained low-difference forest classifier to obtain a classified diagnosis result. The system has low requirements on professional equipment and professionality of personnel, and is portable and universal.

Description

technical field [0001] The invention belongs to the technical field of intelligent diagnosis, and in particular relates to a heart sound diagnosis system combining deep learning and low-difference forest. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Heart disease is one of the important diseases that affect human health. For patients with coronary heart disease and other heart diseases, early diagnosis and treatment can effectively prevent the deterioration of the condition and reduce the mortality rate. [0004] At present, the mainstream diagnosis method in the hospital is to conduct a preliminary diagnosis of the patient's heart through the electrocardiogram or heart sounds. However, both diagnostic methods have some problems. Although the former is accurate and easy to judge, it requires relevant professional equipment; although t...

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Problems solved by technology

Although the former is accurate and easy to judge, it requires relevant professional equipment; although the latter is low in cost, it requires a higher level of experience for doctors, and many areas cannot be equipped with professional equipment or experienced doctors in time. The correct diagnosis has a greater impact on

[0005] Some R&D personnel have begun to use deep learning models for auxiliary diagnosis, but some of the existing models have complex algorithms and huge network structures, making it difficult to deploy on mobile terminals. question of making judgments

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