Electrocardiogram pseudo-difference signal identification method and device

Abstract

The invention relates to the field of electrocardiosignal processing and provides an electrocardiogram pseudo-difference signal identification method and an electrocardiogram pseudo-difference signal identification device for solving the problems such as high calculation complexity and low robustness and adaptability existing in the conventional ECG pseudo-difference signal identification method. The method mainly comprises the following steps: initializing an ECG signal; respectively calculating the first information parameter Rmax, the second information parameter Plambda 1 and the third information parameter Psn of the selected segmentation ECG signal by virtue of correlation analysis, principal component analysis and frequency-domain analysis; and identifying the ECG segmented signal according to the previous three information parameter values of the segment. The experimental result proves that the accuracy, sensitivity and positive predictive value of the identified ECG pseudo-difference signal respectively reach 97.42 percent, 69.21 percent and 92.06 percent; and therefore, the identification accuracy is high. In addition, according to the technical scheme provided by the invention, prior information is not needed, and the method and device have excellent real-time property, robustness and adaptability.

Description

technical field [0001] The invention relates to the field of electrocardiogram signal processing, in particular to a method and device for identifying an electrocardiogram artifact signal. Background technique [0002] Electrocardiogram (ECG) is the potential difference generated on the body surface when the heart is excited, and it can reflect the electrophysiological activity of the heart. The methods of collecting ECG mainly include 3-lead ECG, 5-lead ECG and 12-lead ECG. Existing studies have shown that the ECG signal collected by any method may contain various interferences, which are not changes on the ECG due to cardiac excitation, so they are called "artifacts". There are many reasons for artifacts, for example, the body movement of the person being collected will cause motion artifacts in the ECG; a person's breathing will cause respiratory artifacts in the ECG; if the electrodes fall off during the ECG acquisition process, the recorded ECG Only distraction. From...

AI Technical Summary

Problems solved by technology

The ECG cycle method is a recently proposed method that can automatically detect ECG artifacts. This method is designed based on fuzzy analysis, repolarization analysis and multi-parameter decision-making theory. Its advantage is that it does not require the setting of empirical thresholds in the judgment process. However, it must rely on ECG prior knowledge to complete artifact detection, and the length of input data is limited, so it cannot achieve good artifact location results for shorter ECG records

The dynamic ECG signal artifact recognition algorithm based on the principle of least squares can quickly and effectively identify the artifact components in the ECG signal caused by various reasons, but this method is easy to misjudge the ECG of arrhythmia patients as artifacts, which may Causes a high rate of missed diagnoses for subsequent disease diagnoses

The automatic artefact identification method based on wavelet transform can effectively and accurately identify sudden change artefacts at scales 5 and 6 by performing wavelet transform on the input signal. The effect is poor, so the robustness and adaptability of the method are poor

[0005] It can be seen from this that the existing ECG artifact recognition methods either require prior knowledge, have a limit on the length of the data to be detected, or cannot be adapted to the ECG artifact recognition of arrhythmia patients, or are computationally complex and poor in robustness and adaptability

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