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Method for extracting chaotic characteristics of human body bioelectricity signal

A bioelectrical signal and extraction method technology, applied in the field of extraction of chaotic features of human bioelectrical signals, can solve problems such as inability to integrate diseases, limited data characteristics of human bioelectrical signals, etc., and achieve the effect of precise treatment and convenience

Pending Publication Date: 2019-10-22
SHANGHAI TURING MEDICAL TECH CO LTD
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the existing technology has made some progress in the detection and use of the bioelectric signal of the human body; however, so far, there are still some unavoidable defects in the development of the bioelectric signal data characteristics of the human body in the existing technology: The data characteristics of human bioelectrical signals are too limited, and the application of quantitative indicators of obtained human bioelectrical signal data characteristics mainly depends on the experience of clinicians, which cannot be organically integrated with specific diseases, and it is difficult to give full play to its due clinical detection value Therefore, it is urgent to develop a method for extracting the characteristics of human bioelectric signal data, fully display the complex dynamic process of human bioelectric signal, combine different human health conditions, and screen out subtle changes in human bioelectric signal in the early stage of human disease , to provide convenience for doctors in the later period to carry out precise treatment of human diseases

Method used

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  • Method for extracting chaotic characteristics of human body bioelectricity signal
  • Method for extracting chaotic characteristics of human body bioelectricity signal
  • Method for extracting chaotic characteristics of human body bioelectricity signal

Examples

Experimental program
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Effect test

Embodiment 1

[0046] Example 1. Collection of dynamic data of human body bioelectric signal nonlinear system

[0047] 1. Acquisition of ECG signal

[0048] 1. Collection of ECG data

[0049] Collect n clinically known heart healthy individuals (n> 20) and m individuals with a clinically known heart disease (m> 20) ECG data as experimental data. Use the index data of the gold standard index of heart disease and the consensus of experts as the label of the experimental data.

[0050] 2. Data preprocessing

[0051] Perform preprocessing such as filtering or normalization on the obtained experimental data, and perform data processing according to different data requirements to obtain sample data that meets the requirements; for example, for the sample data of patients with myocardial ischemia, the sample data required to be collected is 10s ECG data, the time processing of 10s ECG data needs to meet the general normative requirements of ECG data; the preprocessing of ECG data also includes conventional...

Embodiment 2

[0059] Example 2. Extraction of chaotic features of dynamic data of non-linear system of human bioelectric signal

[0060] This embodiment provides a method for extracting data characteristics of human bioelectric signal data, which describes in detail the extraction of chaotic characteristics of dynamic data of a non-linear system of human bioelectric signals.

[0061] The extraction of the chaotic feature described in this embodiment is to calculate the dynamic data feature of the non-linear system of the human bioelectric signal, including the following steps:

[0062] The multi-dimensional human bioelectric signal nonlinear system dynamic data obtained in Example 1 is respectively subjected to nonlinear dynamic analysis, through complexity, entropy, phase plane method, Lyapunov exponent (or maximum Lyapunov exponent), fractal dimension Methods such as number, phase plane diagram, power spectrum analysis, Poincaré cross-section, scatter diagram, symbolic dynamics analysis and othe...

Embodiment 3

[0114] Example 3. A method for assessing the risk of myocardial ischemia in the human body

[0115] In this embodiment, the accuracy of the detection of the quantitative index of the dynamic data of the non-linear system of the human bioelectric signal and the disease-assisted judgment described in the second embodiment is described.

[0116] This embodiment provides a method for assessing the risk of myocardial ischemia, which includes the following two steps:

[0117] S1. Obtain ECG data;

[0118] S2. Use the heterogeneity analysis method described in Example 2 to extract the data features of the electrocardiographic data;

[0119] S3. Obtain quantitative indicators of data characteristics of ECG dynamics data;

[0120] Wherein, the quantitative index H of the data feature of the electrocardiographic data i =((a×F i -b×N i +c×T i )÷ln(j i ))-((d×G i +e×TF i -f×M i )÷ln(k i ))+x×PSS i +y×S i +z×SD i , C i Is the quantized value of complexity, En i Is the quantized value of entropy, L i ...

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Abstract

The invention discloses a method for extracting chaotic characteristics of a human body bioelectricity signal. The method comprises the following steps of: preprocessing human body bioelectricity signal data to obtain signal data of a target wave band; modeling the signal data of the target waveband by adopting a self-adaptive system identification method to realize local accurate model identification of the nonlinear system and obtain dynamic data of the human body bioelectricity signal nonlinear system; acquiring human body bioelectrical signal nonlinear system dynamic data, extracting chaotic features of the human body bioelectrical signal nonlinear system dynamic data by adopting a nonlinear dynamic method, acquiring quantitative index data corresponding to the chaotic features, constructing a specific human body disease risk assessment model, and finally assessing the risk of suffering from specific human body diseases of a to-be-tested person. According to the extraction method disclosed by the invention, rich chaotic features and corresponding quantitative index thresholds in the dynamic data of the human body bioelectric signal nonlinear system can be mined. A new evaluation method is provided for the risk of suffering from human body diseases of the to-be-detected personnel.

Description

Technical field [0001] The invention relates to the technical field of measurement for diagnostic purposes, in particular to a method for extracting chaotic characteristics of human bioelectric signals. Background technique [0002] When the human body is in a static state or in an active state, there will be regular electrical phenomena closely related to the state of life, called bioelectricity. The bioelectric signal of the human body includes resting potential and action potential, which is essentially the flow of ions across the membrane; the bioelectric signal of the human body can usually be picked up by electrodes, amplified by a suitable bioelectric amplifier, and recorded. The recorded human bioelectricity Signals are the main indicators to consider the physiological parameters of the human body and play an important role in people's lives. [0003] At present, the existing technology has made some progress in the detection and use of the bioelectric signal of the human ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00A61B5/00A61B5/04
CPCA61B5/7275A61B5/24G06V40/70G06F2218/08
Inventor 徐赤坤
Owner SHANGHAI TURING MEDICAL TECH CO LTD
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