Device for classifying dynamic electrocardio data

Abstract

The invention discloses a device for classifying dynamic electrocardio data. The device comprises an electrocardio data collecting device, an electrocardio information database, an electrocardio index obtaining device, an electrocardio index screening device, a feature combination obtaining device, a classifier screening device and a classification result output device, wherein the electrocardio index screening device is used for carrying out difference analysis on electrocardio indexes to screen out electrocardio indexes with significance differences, the feature combination obtaining device is used for carrying out feature combination on at least two items in the electrocardio indexes, with the significance differences, screened by the electrocardio index screening device to obtain a plurality of feature combinations, the classifier screening device adopts a plurality of classifiers for testing the feature combinations obtained by the feature combination obtaining device so as to screen out the optimum classifier and the optimum feature combination, the classification result output device is used for receiving the personnel information of patients and pathological data relevant to the heart activity state, classifying the pathological data, relevant to the heart activities, of the patients according to the optimum feature combination and the optimum classifier screened out by the classifier screening device, and outputting the classification results.

Description

technical field [0001] The invention relates to the medical field of heart disease, in particular to a device for classifying dynamic electrocardiographic data. Background technique [0002] At present, clinical indicators for risk stratification of patients at risk of sudden death include left ventricular ejection fraction, heart rate variability, sinus heart rate oscillations, heart rate deceleration force, T wave alternation, etc. Most of the above indicators can be obtained from dynamic electrocardiogram Calculated. However, these current risk stratification methods are difficult to accurately distinguish patients with high risk of sudden death from those with low risk of sudden death. Therefore, it is necessary to integrate various technical methods to stratify patients at risk of sudden death. The key factor for stratification is to use a classifier to classify the dynamic ECG data of patients at risk of sudden death, and then use a certain method to stratify the clas...

AI Technical Summary

Problems solved by technology

However, its disadvantage is that some inspections are invasive, while some non-invasive inspections are not accurate. The combination of two or three inspections is only a simple "yes or no" judgment, although the risk stratification ability has been improved. , but its sensitivity is still not satisfactory

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