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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: 2011-11-23
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 steps of an integrated fast algorithm for electrocardiographic signal denoising and QRS wave recognition in the present invention will be described in detail below.

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

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

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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 a fast algorithm for denoising ECG signals and QRS wave recognition that can be integrated. Background technique [0002] Conventional ECG signals are mV-level signals. During the collection of ECG signals, due to the interference of external and human factors, the collected ECG signals are mixed with a large number of noise signals. Noise changes the characteristics of the ECG signal itself and affects the analysis and diagnosis accuracy of the ECG. Because of the non-stationary characteristics of ECG signals and the large distribution range of pollution noise, the use of traditional linear filters is limited, so in the past few years, wavelet analysis has been widely used in the denoising of ECG signals. Documents A Wavelet-Based ECG Delineator Evaluation on Standard Databases, A New Wavelet Based Method for Denoising of Biological Signals,...

Claims

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Application Information

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Patent Type & Authority Applications(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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