Method for realizing sudden death risk prediction on mini dynamic electrocardiogram monitoring equipment

Abstract

The invention discloses a method for realizing sudden death risk prediction on mini dynamic electrocardiogram monitoring equipment. The method includes: building a sudden cardiac death data base and an MIT-BIH normal sinus rhythm database into a training data sample and a cross validation sample; randomly setting a weight value for each layer of an artificial neural network, inputting the training data sample to repeatedly and iteratively correct the weight value of each layer until training errors are less than a certain specified value, and finding a weight value matrix capable of predicting sudden death risk; utilizing the weight value matrix, and adding the same into an original artificial neural network to build a new artificial neural network; using collected electrocardiosignals of a target human body, processing the electrocardiosignals of the human body, acquiring a target human body feature vector X, and performing prediction operation according to the target human body feature vector X and the new artificial neural network to finally acquire a prediction value.

Description

technical field [0001] The invention relates to sudden death risk prediction, in particular to a method for realizing sudden death risk prediction on a miniature dynamic electrocardiogram monitoring device. Background technique [0002] Sudden cardiac death (sudden cardiac death, SCD) refers to the natural death caused by cardiac causes, which is characterized by sudden loss of consciousness and occurs within 1 hour after the onset of acute symptoms. According to statistics, there are about 7 million SCD patients in the world every year, accounting for 1 / 4 of all deaths, which seriously threaten people's lives. At present, the average success rate of rescue in the world is less than 1%. [0003] Patients with sudden cardiac death are usually healthy (50% of cardiac arrests occur in individuals without known heart disease) or in stable condition, and there may be manifestations of heart disease before sudden death, but a considerable number of heart disease patients may have ...

AI Technical Summary

Problems solved by technology

[0006] Although SCD directly endangers the personal safety of patients and has very large harm and relatively serious consequences, the early identification technology of SCD in clinical medicine mainly lies in stratified long-term risk management and prediction, and short-term prediction before the occurrence of SCD Technology, which obviously lags behind modern treatment technology is in the process of exploration. The main difficulty and key of this short-term prediction of SCD is how to timely and accurately identify the population at high risk of sudden death, and take intervention measures to reduce the occurrence of sudden death

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