ECG signal quality detection method, device, computer equipment and storage medium

Abstract

The present application relates to a method, a device, a computer device and a storage medium for detecting the quality of an electrocardiographic signal. The method includes: acquiring an electrocardiographic signal of a first period of preset length; performing filter processing and histogram matrix conversion on the electrocardiographic signal to obtain histogram distribution data including a plurality of time domain values ​​and corresponding amplitude vectors; Calculate the information entropy of the ECG signal according to the time domain value and the amplitude vector; obtain the entropy threshold, and calculate the quality index of the ECG signal according to the information entropy and the entropy threshold; when the quality index does not reach the quality threshold, update the entropy threshold according to the quality index, And collect the ECG signals of the adjacent second period; repeat the steps of filtering and converting the adjacent ECG signals to the histogram matrix; until the quality index meets the quality threshold, obtain the ECG signal corresponding to the quality index, and convert the ECG The signal is added to the target signal group. The method can effectively improve the efficiency and accuracy of the ECG signal quality detection.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method, device, computer equipment and storage medium for detecting the quality of electrocardiographic signals. Background technique [0002] The electrocardiographic signal refers to the signal waves of various forms of potential changes that are elicited from the body surface by the electrocardiograph in a certain period, accompanied by changes in bioelectricity, to capture the reflection of the heart over a period of time. ECG signal is the process of electrical activity reflecting the excitement of the heart, and it has important reference value for the study of the basic functions of the heart and its pathology. [0003] With the rapid development of social economy, the pace of people's life and work is getting faster and faster, and many people's bodies are in a sub-healthy state. Therefore, some monitoring applications based on HRV (Heart Rate Variability, ...

AI Technical Summary

Problems solved by technology

Therefore, some monitoring applications based on HRV (Heart Rate Variability, heart rate variability) have emerged. The way to detect ECG signals is usually to construct a neural network model through the characteristics of the QRS waveform to judge the signal quality. In this way, based on the QRS The way of waveform features is easily disturbed by noise, and the way of building an ECG signal quality judgment model based on neural network is difficult to obtain training data sets, and the data calculation amount is large, and the calculation speed is slow. The efficiency and accuracy of ECG signal quality detection rate will be affected

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