Electrocardiogram monitoring system with electrocardiosignal quality discrimination function

Abstract

The invention discloses an electrocardiogram monitoring system with an electrocardiosignal quality discrimination function. The electrocardiogram monitoring system comprises an electrocardiogram monitor and an intelligent terminal. The electrocardiogram monitor is in wireless communication with the intelligent terminal. The intelligent terminal internally comprises a processor and a Bluetooth communication module connected with the processor. The processor comprises a signal collection module, a feature extraction module, a neural network training module and a discrimination module. An independent single-channel electrocardiosignal is converted into three feature values, namely, the QRS energy specific value, the signal kurtosis and the base line energy specific value, before learning through the technological means of the power spectral density quadrature and the kurtosis coefficient, and therefore a discrimination model is accurately established through the artificial neural network learning algorithm in a gradient descent optimizing mode according to the feature values, the discrimination method of the electrocardiosignal quality is achieved by reducing a system model, and therefore whether the electrocardiosignal can be used for 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 electrocardiographic monitoring system with the function of 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...

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

Images