Intelligent terminal device for evaluating and distinguishing quality of electrocardiosignal

Abstract

The invention discloses an intelligent terminal device for evaluating and distinguishing the quality of an electrocardiosignal. The intelligent terminal device for evaluating and distinguishing the quality of the electrocardiosignal comprises a processor and a bluetooth communication unit, wherein the processor is loaded with a signal collecting module, a feature extracting module, a neural network training module and a distinguishing module. By using the intelligent terminal device which is provided by the invention, through a technical means of solving an integral and a kurtosis coefficient by power spectral density, the electrocardiosignal of an autonomous single channel is converted into three feature values of a QRS (magnetic resonance angiography) energy specific value, a signal kurtosis and a baseline energy specific value before learning; furthermore, according to the feature values, the distinguishing module is accurately established in a gradient-descent optimizing manner by utilizing an artificial neural network learning algorithm; through the recovery of a system model, a method for distinguishing the quality of the electrocardiosignal is realized; furthermore, whether the electrocardiosignal can be used for carrying out diagnosis or not is effectively discriminated.

Description

technical field [0001] The invention belongs to the technical field of medical equipment, and in particular relates to an intelligent terminal equipment used for evaluating and discriminating the quality of electrocardiographic signals. Background technique [0002] ECG signals are often subject to severe noise and artifact interference, and filtering algorithms often cannot remove these interferences well, especially because interference signals and ECG signals often have similar frequency components and similar shapes. Therefore, the interference will reduce the quality of the ECG signal, and affect the automatic disease diagnosis based on the ECG, thus causing more false alarms (false positives). For example, if the ECG signal is too poor, it will cause a large number of false alarms in the ICU, and the false alarms in the ICU may even be as high as 86%. [0003] With the gradual increase in human life expectancy, healthy aging in modern society will become a global focu...

AI Technical Summary

Problems solved by technology

For a specific signal, it may contain many factors that affect the quality, how to classify it into a category that is more similar to it, the previous processing method is difficult to do this

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