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Integratable fast algorithm for denoising electrocardiosignal and identifying QRS waves

A technology of electrocardiographic signals and fast algorithms, applied in computing, electrical digital data processing, medical science, etc., can solve problems that are difficult to implement, and achieve the effects of improving detection accuracy, fast execution speed, and improving execution speed

Inactive Publication Date: 2013-05-15
ZHUHAI COLLEGE OF JILIN UNIV
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AI Technical Summary

Problems solved by technology

It avoids the problem of using complex algorithms such as wavelet and artificial neural network for identification, which is difficult to realize on the existing hardware platform

Method used

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Embodiment Construction

[0021] The following describes in detail the steps of an integrated fast algorithm for ECG signal denoising and QRS complex identification according to the present invention.

[0022] Step (1), perform N-layer wavelet lifting decomposition on the ECG sampling signal X using DB4 wavelet. where N is the condition The smallest positive integer of , F is the sampling frequency of ECG signal. According to the general normal ECG signal in the frequency range of 0.05Hz ~ 100Hz, and 90% of the ECG frequency energy is concentrated between 0.25Hz ~ 40Hz. Among them, the higher frequency of QRS wave is about 3~40Hz, and the frequency of P and T wave is about 0.7~10Hz. The principle of determining the number of decomposition layers is that, after wavelet lifting decomposition, the highest frequency of the low-frequency coefficients of the highest layer can be made less than or equal to 0.5 Hz.

[0023] Step (2), obtain the threshold for processing the high-frequency coefficients of ea...

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Abstract

The invention combines a wavelet lifting process and a difference process and provides an integratable fast algorithm for denoising electrocardiosignal and identifying QRS waves. The invention provides a method for denoising a weighted threshold value and solves the problem of signal distortion after denoising by using a global threshold value. The invention proposes to realize the lifting transformation of an electrocardiosignal by using a DB4 first-order derivative, avoid a wavelet lifting and re-decomposing process of the denoised signal and speed up the identification of QRS waves by combining the difference process in a DB4 wavelet lifting transformation process; meanwhile, an improved self-adaptive threshold updating method is adopted to improve the accuracy of QRS wave identification. In the invention, both the wavelet lifting process and a difference process, which are adopted, have the characteristics of high computing speed, low memory space occupation and capacity of realizing integer operation; therefore, the integrated application of the algorithm can be realized easily on the conventional hardware platform and can be realized easily in applications of computers and large processors.

Description

technical field [0001] The invention belongs to the fields of information processing and medical signal processing, in particular to an integrated fast algorithm for denoising electrocardiogram signals and QRS wave identification. Background technique [0002] Conventional ECG signals are mV-level signals. In the process of collecting the ECG signal, due to the interference of the outside world and the human body itself, the collected ECG signal is mixed with a large number of noise signals. Noise changes the own characteristics of ECG signal, which affects the analysis and diagnostic accuracy of ECG. Because the ECG signal has non-stationary characteristics and the pollution noise has a large distribution range, which limits the use of traditional linear filters, wavelet analysis has been widely used in the denoising of ECG signals in the past few years. The literatures A Wavelet-Based ECG Delineator Evaluation on Standard Databases, A New Wavelet Based Method for Denoisi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0452G06F19/00
Inventor 司玉娟姚成郎六琪施蕾韩松洋
Owner ZHUHAI COLLEGE OF JILIN UNIV
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