Baseline drift correction method for electrocardiosignal

Abstract

The invention discloses a baseline drift correction method for electrocardiosignal. The method includes the steps of conducting improved self-adaption noise set empirical mode decomposition on the original electrocardiosignal to obtain intrinsic mode functions and residual components, counting the zero-crossing rates of all the intrinsic mode functions and all the residual components, and removing the intrinsic mode functions with the zero-crossing rates smaller than the set threshold value and the residual components with the zero-crossing rates smaller than the set threshold value from the original electrocardiosignal to obtain the electrocardiosignal where baseline drift is removed. According to the method, by conducting improved self-adaption noise set empirical mode decomposition on the original electrocardiosignal, the mode aliasing phenomenon is eliminated, and residual noise is reduced; the step of conducting self-adaption baseline drift amount selection according to the zero-crossing rates is added, and therefore the problem of the lack of effective baseline drift component selection means in the prior art is solved. The method can be widely applied to the field of baseline drift correction of the electrocardiosignal.

Description

Technical field [0001] The invention relates to the field of signal processing, in particular to a method for correcting baseline drift of an ECG signal. Background technique [0002] The electrocardiogram is a data record of heart activity, which plays a very important role in the diagnosis of heart health in clinical medicine. However, in the process of human body ECG signal acquisition, due to the influence of medical equipment and the human body's own factors, it is unavoidable to be interfered by various noises, such as baseline drift, myoelectric interference and power frequency interference. Among them, the baseline drift is mainly caused by the human body's breathing motion and the sliding of the collection electrode, which is a slowly changing ultra-low frequency interference signal. This interference will raise the ST band of the ECG, causing serious distortion of the ECG trajectory, thus affecting the normal medical diagnosis. Therefore, preprocessing the ECG data to...

AI Technical Summary

Problems solved by technology

Although the method of applying median filtering to remove baseline drift has the advantages of small amount of calculation and fast speed, it will produce "step-like" distortion and low precision

When applying wavelet transform to remove baseline drift, it is necessary to use high-scale approximation components to approximate the baseline drift signal. At this time, it is necessary to select the appropriate wavelet function and the number of decomposition layers, and the wavelet function and the number of decomposition layers have a great influe

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