Method for classifying imbalance heart beats based on multi-module neural network

Abstract

The invention discloses a method for classifying imbalance heart beats based on a multi-mode neural network. The method involves an electrocardiosignal preprocessing module, an imbalance data processing module and a feature extracting and classifying module. The preprocessing module conducts denoising and segmenting on an electrocardiosignal; the imbalance data processing module is a core of a system, and sequentially introduces three kinds of methods for processing imbalance in combination with the features of the electrocardiosignal and the features of an algorithm, wherein the methods include boundary sample feature linear synthesis (BLSM), a context feature comprehensive module (CTFM) and a two-phase training (2PT); the feature extracting and classifying module obtains high-order features of all categories of heart beats and realizes final heart beat classification. The method has the advantages that in the prior art, the heart beats cannot be well classified, the corresponding solutions are provided from the aspects of sampling, features and algorithms, and therefore the accuracy of classifying is improved. The method is suitable for solving of the problem of classifying imbalance of time sequence data, images and others, and has generality.

Description

technical field [0001] The present invention designs an electrocardiogram classification method, and proposes a set of solutions for the influence of unbalanced heartbeat data on classification results, which belongs to the interdisciplinary field of engineering application and information science. Background technique [0002] Cardiovascular disease (CVD) is a disease with the highest mortality rate globally, and the number of people dying from CVD is increasing every year in many developing countries, and death from CVD is more common at certain ages. In 2015, CVD deaths reached 17.9 million (32.1%), surpassing the 12.3 million (25.8%) in 1990. Cardiovascular diseases include coronary heart disease, cerebrovascular disease, rheumatic heart disease and other diseases. Cardiac arrhythmia is a type of cardiovascular disease in which the heart beats too fast, too slow, or with an irregular rhythm. A fast heart rate, that is, more than 100 beats per minute in an adult, is cal...

AI Technical Summary

Problems solved by technology

In addition, most machine learning algorithms assume that training on balanced data, highly skewed training data will make the learned algorithm more biased towards the majority class

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