EMD and energy thereof based electrocardiosignal denoising algorithm and equipment, and storage medium

Abstract

The invention discloses an EMD and energy thereof based electrocardiosignal denoising algorithm and equipment, and a storage medium. The algorithm includes the following steps: performing signal averaging processing and determining a tolerance value; performing EMD on a signal to obtain IMF at all levels; according to the energy of the IMF at all levels after EMD, calculating, except for boundarypoints, the order where the first maximum point and the first minimum point are located of an energy curve, combining the tolerance value, judging an IMF order change point that needs to be denoised,and presetting the IMF order change point if the first maximum point and the first minimum point of the energy curve do not exist; performing threshold denoising on the IMF at all levels before the IMF order change point; and reconstructing the IMF at all levels after threshold denoising, the IMF without performing threshold denoising and residual, and generating a denoised electrocardiosignal. Through the scheme, the information greater than the threshold part can be completely reserved, additional shock and jump points cannot be generated, the smoothness of the original signals can be well guaranteed, and denoising quality can be enhanced.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic signal denoising, in particular to an electrocardiographic signal denoising algorithm, device and storage medium based on EMD and its energy. Background technique [0002] Electrocardiogram (ECG) is a physiological electrical signal that was studied earlier by humans and applied to clinical medicine. Electrocardiogram signals are widely used in medicine to diagnose heart disease. However, due to human breathing, movement, and electromagnetic interference in the surrounding environment, etc., the electrocardiogram often contains a lot of noise. Common noises include power frequency interference, myoelectric interference, and baseline drift. All of this noise can cause distortions in the ECG waveform, masking tiny features that are important for diagnosis. Therefore, it is necessary to eliminate the noise in the ECG signal. [0003] Due to the complex types of ECG signal noise sourc...

AI Technical Summary

Problems solved by technology

The traditional hard threshold method is better than the soft threshold method in the sense of mean square error, but the hard threshold method will generate additional oscillations and jump points, and it is difficult to have the smoothness of the original signal

The denoised signal obtained by the soft threshold method is relatively smoother, but it will compress the signal and produce a certain deviation, which directly affects the approximation between the reconstructed signal and the real signal

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