Personalized electrocardiogram intelligent auxiliary diagnosis device and method

Abstract

The invention discloses a personalized electrocardiogram intelligent auxiliary diagnosis device which comprises a feature vector extraction module used for extracting a to-be-trained electrocardiogram feature vector, a heart state classifier generation module used for training the to-be-trained feature vector to generate a heart state classifier, a user identification module used for obtaining user identity label, a heart state classifier storage module used for storing a heart state classifier of a corresponding user based on the user identity label, and a diagnosis module used for obtaining the heart state classifier of the corresponding user based on the user identity label and diagnosing a to-be-diagnosed electrocardiogram feature vector through the heart state classifier to obtain heart state classification, wherein the feature vector extraction module is also used for extracting the to-be-diagnosed electrocardiogram feature vector and a feedback electrocardiogram feature vector, and the heart state classifier generation module is also used for training the feedback electrocardiogram feature vector and updating the heart state classifier. According to the personalized electrocardiogram intelligent auxiliary diagnosis device and method, individual diagnosis can be performed on different individuals, and reference is provided for diagnosis of doctors.

Description

technical field [0001] The invention relates to the field of electrocardiogram diagnosis, in particular to a personalized electrocardiogram intelligent auxiliary diagnosis device and method. Background technique [0002] The electrocardiogram has important reference value for the research of the basic function of the heart and the diagnosis of heart disease. However, due to individual innate differences, even in the case of a healthy heart, the parameters of a single heartbeat in the ECG of each normal person are different. Some electrocardiogram diagnosis systems in the prior art directly use previous electrocardiograms for learning and classification, but ignore the influence of individual differences on electrocardiograms, so that each patient uses the same index for diagnosis. Such electrocardiogram diagnosis methods are not scientific. [0003] Therefore, designing an electrocardiogram diagnostic device that can perform individualized diagnosis on different individuals...

AI Technical Summary

Problems solved by technology

Some electrocardiogram diagnosis systems in the prior art directly use the previous electrocardiogram for learning classification, but ignore the influence of ind

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