Automatic classification method, system and device of electrocardiosignal ST band

Abstract

The invention discloses an automatic classification method of an electrocardiosignal ST band. The automatic classification method is characterized by comprising the following steps: S1. acquiring an electrocardiosignal wave form of a human body and pretreating the electrocardiosignal wave form; S2. performing characteristic point detection to the pretreated electrocardiosignal wave form; S3. based on the characteristic point detection in the step S2, determining the wave form of the electrocardiosignal ST band, and acquiring the characteristic parameters on the wave form of the electrocardiosignal ST band so as to establish a to-be-classified characteristic input matrix; S4. classifying the wave form of the electrocardiosignal ST band into a training sample and a testing sample, and establishing a classifier model based on the training sample; and S5. inputting the testing sample into the classifier model for testing, and completing the final classification by combining decision fusion. The invention further discloses an automatic classification system and device of the electrocardiosignal ST band. By establishing the classifier model and decision fusion by using a nerve network method, calculation can be effectively reduced, the time cost can be decreased, the classification precision of the ST band can be improved, and the classification is easier.

Description

Technical field [0001] The present invention relates to the field of medical signal processing. Specifically, the present invention relates to a method, system and device for automatically classifying ST segments of ECG signals. Background technique [0002] In recent years, the auxiliary diagnostic equipment for electrocardiogram has developed rapidly. With the advancement of science and technology in the information field, especially with the development of pattern recognition technology, the function of electrocardiogram equipment is no longer just to obtain ECG signals and print ECG, but to Mining the effective data in the ECG and the development of automatic recognition and feature classification. The analysis equipment with feature classification function can provide doctors with more intuitive and effective ECG information, effectively save diagnosis time, and improve the diagnosis efficiency of doctors. It is one of the important auxiliary medical equipment. [0003] The a...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems of difficult learning rules and limited classification conditions of electrocardiogram morphological features, the present invention provides a method, system and device for automatically classifying ECG signals based on ST segments, by extracting the three The parameters of the feature information are used as input, the classifier model is established, and the classification results are combined for decision-making

Images