R wave detection algorithm based on extremum field mean mode decomposition and improved Hilbert enveloping

Abstract

The invention discloses an R wave detection algorithm based on extremum field mean mode decomposition and improved Hilbert enveloping and belongs to the technical field of weak biological signal processing. An electrocardio signal pre-processing algorithm based on the extremum field mean mode decomposition and the improved Hilbert enveloping and an R wave detection algorithm based on slope threshold are provided. Detection criteria are set according to wave form characteristics and time domain distribution characters of electrocardio signals, and positions of R waves with most obvious characters and highest information amount in the electrocardio signals are detected. An extremum field mean mode decomposition algorithm improves empirical mode decomposition speed and can effectively restrain mode superimposition and boundary effect. The improved Hilbert enveloping can effectively restrain interference of noise and other characteristic waves an can also enhance energy of the R waves. The R wave detection algorithm based on the extremum field mean mode decomposition and the improved Hilbert enveloping can also detect positions of R points accurately even if interference of strong noise and large P / T waves exists. A Massachusetts institute of technology-Beth Israel hospital (MIT-BIH) data base is used for detecting the R wave detection algorithm. Sensitivity of the R wave detection algorithm is 99.94%, and positive predictive rate is 99.87%.

Description

technical field [0001] The invention belongs to the technical field of weak bioelectrical signal processing, in particular to the denoising of weak electrocardiographic signals interfered by severe noise and the detection of feature points. Background technique [0002] Heart disease is one of the diseases with the highest morbidity and mortality in medicine today, and the prevention and diagnosis of heart disease is the primary problem facing the medical field today. One of the main technologies for diagnosing heart disease is electrocardiogram (ECG). Because the method of diagnosing heart disease and cardiovascular disease by electrocardiogram is non-invasive, it is convenient and easy to perform electrocardiogram diagnosis and has been widely used clinically. The ECG signals collected by non-invasive body surface electrodes are relatively weak, only at the millivolt level, so the ECG signals are easily interfered by the external environment. These interferences include p...

AI Technical Summary

Problems solved by technology

The wavelet transform method has been widely studied and applied in the processing of ECG signals due to its superior time-frequency local characteristics, but the selection of wavelet base is an important and complicated problem, and so far there is no standard for the selection of wavelet base functions

The empirical mode decomposition method has the same time-frequency local characteristics as the wavelet transform method. The classic empirical mode decomposition method proposed by Huang in 1998 needs two cubic spline fitting operations to calculate the mean value, which is time-consuming. The swing is large, and the correct mean value cannot be obtained when the waveform is asymmetrical, and it is easy to generate mode aliasing during the signal decomposition process

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