Polymorphism reconstruction and optimization method for realizing abnormal electrocardiogram template based on big data

Abstract

The invention provides a polymorphism reconstruction and optimization method for realizing an abnormal electrocardiogram template based on big data. The polymorphism reconstruction and optimization method is characterized by comprising the following steps that firstly, parameter template data with two-dimension characteristics are built according to decomposing parameters of standard electrocardiogram waveforms, and an electrocardiogram waveform template consisting of a plurality of fine nodes is built; then, the big data of the existing and edited dynamic electrocardiogram monitor are utilized for preprocessing before comparison, i.e., waveform segmentation, and next, the gradual waveform comparison with the electrocardiogram waveform template is carried out; next, three key quantities including FRR (fault read rate), FAR (false accept rate) and TH of the value range being 0 to 1 are used for counting the success rate finally matched with the assessment in a comparison algorithm, in addition, the polymorphism reconstruction is carried out, or the template data volume optimization is carried out again.

Description

technical field [0001] The present invention relates to a polymorphic reconstruction and automatic optimization method for abnormal ECG templates based on big data, mainly using big data generated by dynamic ECG monitors to dynamically construct and optimize various forms of abnormal ECG data templates The method belongs to the technical field of dynamic electrocardiogram monitoring. Background technique [0002] At present, in the medical dynamic ECG monitoring, the identification based on the abnormal ECG is often manually evaluated by professional doctors, which is not only inefficient but also requires a lot of manual labor. Although some medical special ECG software or equipment can also achieve dynamic Pre-identification, but the misrecognition rate and rejection rate are high. The reason is that most ECG recognition algorithms are based on the abnormal ECG data template of conventional standards, and there are large differences for different individuals, such as the e...

AI Technical Summary

Problems solved by technology

[0002] At present, in the medical dynamic ECG monitoring, the identification based on the abnormal ECG is often manually evaluated by professional doctors, which is not only inefficient but also requires a lot of manual labor. Although some medical special ECG software or equipment can also achieve dynamic Pre-identification, but the misrecognition rate and rejection rate are high. The reason is that most ECG recognition algorithms are based on the abnormal ECG data template of conventional standards, and there are large differences for different individuals, such as the elderly, children, and young people. Individual differences such as middle-aged and middle-aged people, the manifestations of abnormal ECG will be different

Therefore, for dynamic ECG monitoring, every movement of the human body will show more obvious differences. In this case, it is obvious that the standard ECG template cannot meet the actual needs of dynamic ECG monitoring.

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