Adaptive collection empirical mode decomposition based electrocardiosignal characteristic point identification method

Abstract

The invention provides an adaptive collection empirical mode decomposition based electrocardiosignal characteristic point identification method. The method comprises the steps that two parameters of Ratio and NEEMD combining empirical mode decomposition are calculated adaptively according to the self characteristics of to-be-identified electrocardiosignal, AEEMD noise-reduction processing is conducted on the to-be-identified electrocardiosignal according to the determined parameters, an electrocardiogram noise-reduction signal is obtained, AEEMD noise-reduction processing and characteristic point identification of the electrocardiosignal are combined, hierarchies large in QRS waveform energy ratio are extracted from intrinsic mode function components of all hierarchies obtained in the AEEMD noise-reduction process are overlapped to serve as a detection layer, after difference operation is conducted, an adaptive section division mode is adopted for conducting section processing, electrocardiogram difference section signals are obtained, and finally, by combining the electrocardiogram noise-reduction signal with the electrocardiogram difference section signals, positioning identification of QRS waves in the to-be-identified electrocardiosignal is achieved according to distances between all the characteristic points in the electrocardiosignal and a waveform slope relation; the amount of computation can be subjected to optimal identification, the identification efficiency is improved, and the identification accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of physiological signal acquisition and analysis, in particular to a method for identifying feature points of electrocardiographic signals based on self-adaptive set empirical mode decomposition. Background technique [0002] For pathological ECG signals with high complexity collected in real time in clinical practice, it is often impossible to accurately locate the feature points due to the presence of noise and complex pathological conditions that cause signal features to be submerged or severely deformed. [0003] Typical noises that exist during the acquisition of ECG signals generally include power frequency interference, baseline drift, and high-frequency interference. The existence of these noises will seriously affect the identification of signal feature points and clinical diagnosis based on signal feature point information. The noise reduction methods of ECG signal mainly include frequency domain ...

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Problems solved by technology

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for identifying ECG signal feature points based on self-adaptive ensemble empirical mode decomposition, in order to solve the pr

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