Classification method, device and equipment based on multi-lead electrocardiosignal and medium

Abstract

The invention relates to artificial intelligence, and provides a classification method, device and equipment based on a multi-lead electrocardiosignal and a medium. The method comprises the following steps of: preprocessing electrocardiosignal data in each lead to obtain standard data and generate a standard sample; performing sample enhancement on the standard sample to obtain a signal training sample; performing feature extraction on the training data based on the network extraction layer to obtain signal features; performing mapping processing on the signal features based on the network classification layer to obtain lead category features; and inputting the lead category features into a decision fusion layer to obtain a prediction result, adjusting parameters in the preset network until the similar distance is smaller than the preset distance and the loss value of the preset network is not reduced any more, obtaining a signal classification model, and inputting the multi-lead data into the signal classification model to obtain a target category. According to the invention, the target category can be accurately determined. In addition, the invention further relates to a blockchain technology, and the target category can be stored in the blockchain.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a classification method, device, equipment and medium based on multi-lead ECG signals. Background technique [0002] In the current multi-lead ECG signal classification method, since the signal features cannot be accurately extracted from the ECG signals, and the signal features cannot be accurately associated with the classification results, it is impossible to accurately determine the multi-lead ECG signal classification method. The category of electrical signals, resulting in low credibility in smart medical care. [0003] Therefore, how to accurately construct a classification method for multi-lead ECG signals has become an urgent technical problem to be solved. Contents of the invention [0004] In view of the above, it is necessary to provide a classification method, device, equipment and medium based on multi-lead ECG signals, which can accuratel...

AI Technical Summary

Problems solved by technology

[0002] In the current multi-lead ECG signal classification method, since the signal features cannot be accurately extracted from the ECG signals, and the signal features cannot be accurately associated with the classification results, it is impossible to accurately determine the multi-lead ECG signal classification method. Types of electrical signals, resulting in less credibility in smart healthcare

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