Cardiac sound diagnostic system based on depth confidence network and diagnostic method

Abstract

The invention discloses a cardiac sound diagnostic system based on a depth confidence network and a diagnostic method of the cardiac sound diagnostic system. The cardiac sound diagnostic system comprises a cardiac sound diagnostic terminal, a network interface module, a database module, a data analyzing module, and a data management module; the data management module is connected with the database module, the data analyzing module and the network interface module at the same time, and the database is connected with the data analyzing module at the same time; the network interface module is in signaling connection with the cardiac sound diagnostic terminal. The diagnostic method is based on a depth confidence network model consisting of restricted Boltzmann machine layering; the diagnostic method comprises the steps: using a patient's cardiac sound file database; training the established depth confidence network by using a layer-by-layer greedy algorithm; inputting the cardiac sound signal to be diagnosed to the depth confidence network model after training; obtaining the final diagnostic result at an output layer and returning to the cardiac sound diagnostic terminal. The cardiac sound diagnostic system based on the depth confidence network and the diagnostic method can realize the remote diagnosis of patient's cardiac sound signals; the operation is convenient and simple, the diagnostic accuracy is high, the cost is low; besides, the device is convenient to maintain and upgrade.

Description

Technical field [0001] The invention relates to a heart sound diagnosis technology in the field of medical equipment, in particular to a heart sound diagnosis system based on a deep confidence network and a diagnosis method thereof, and belongs to the innovative technology of a heart sound diagnosis system and a diagnosis method based on the deep confidence network. Background technique [0002] Heart sound signal is one of the important physiological signals of the human body, which contains a large amount of original pathological information of the human body. Therefore, the diagnosis of heart sound has irreplaceable advantages and clinical value. Through the analysis of heart sounds, in medicine, the diagnosis of heart sounds can generally be roughly divided into five conditions: normal heart sounds, abnormal heart sounds, extra heart sounds, heart murmurs, and pericardial friction sounds. In the present invention, the five diagnostic categories of heart sounds are divided into...

AI Technical Summary

Problems solved by technology

[0008] 3. Additional heart sounds

[0012] 4. Heart murmur

[0015] 3) Valvular insufficiency: valvular insufficiency (such as aortic insufficiency), or relative insufficiency due to the expansion of the valve orifice due to large blood vessels or heart enlargement, blood regurgitation forms eddies, and produces noise

However, these stethoscopes cannot solve the key problem that requires professional doctors to spend a lot of time to identify the complex pathological features of heart sounds, and cannot intelligently extract pathological features to assist diagnosis

And this stethoscope is expensive due to technological monopoly, cannot be widely used, and is difficult to upgrade and maintain

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