Risk stratification method for myocardial ischemia based on deterministic learning and deep learning

Abstract

The invention discloses a risk stratification method for myocardial ischemia based on deterministic learning and deep learning. The method includes the steps that conventional 12-lead electrocardiogram signals are collected, based on the deterministic learning theory, neural network modeling and identification are conducted on intrinsic electrocardiodynamic characteristics of the shallow electrocardiogram signals, and the intrinsic dynamic characteristics of ECG signals are obtained; the convolutional neural network under the framework of deep learning is used for achieving the risk stratification of myocardial ischemia. The method combines the deterministic learning dynamic modeling method and the deep learning classification method for the first time, the method is applied to early riskstratification of myocardial ischemia based on the conventional 12-lead electrocardiogram signals, no additional detection equipment is needed, and the method is easy and convenient to use and easy tooperate. Through the deterministic learning method, the dynamic characteristics more sensitive to the ischemic state are extracted, the deep neural network can learn data features independently without further data characterization, and the complexity of the system is reduced.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a myocardial ischemia risk stratification method based on definite learning and deep learning. Background technique [0002] Myocardial ischemia is a common cardiovascular disease that seriously threatens human life and health. The risk stratification of myocardial ischemia is of great significance to the diagnosis, treatment and prognosis of the disease, and is closely related to human health. In the past half a century, medical imaging technology has gradually matured, and has become the mainstream direction of diagnosis and treatment of myocardial ischemia and coronary heart disease. However, in clinical practice, some patients clinically present asymptomatic myocardial ischemia. Although there are obvious coronary changes and objective evidence of myocardial ischemia, they are not accompanied by angina pectoris, which is very easy to be ignored by pat...

AI Technical Summary

Problems solved by technology

However, this method only gives qualitative and subjective results depending on whether the graphics are scattered or not, and cannot use quantitative means to carry out standard planning

In China's invention patent application: A quantitative analysis method for electrocardiographic data (application number: 201710587538.0), a quantitative analysis method for electrocardiographic data graphs is proposed, and quantitative indicators are extracted from the above results, but the index extraction process still remains There is considerable subjectivity, and it is impossible to give the exact number of indicators to extract, as well as the relationship between the number of indicators and the final diagnosis accuracy

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