Detection evaluation and correction method based on cell bioelectricity frequency

Abstract

The invention discloses a cell bioelectricity frequency-based detection evaluation and correction method, and relates to the field of cell bioelectricity frequency detection evaluation, and the method comprises the steps: carrying out the multi-dimensional focus region division according to the influence of cell bioelectricity abnormity on a human body, carrying out the critical quantitative marking of a focus region division result, and carrying out the correction of the cell bioelectricity frequency. Selecting a cell bioelectricity monitoring part; a correction scheme is selected according to pathological symptoms and severity levels of the focus areas; selecting a test sample, and establishing cell bioelectricity health characteristics of different focus areas; analyzing the bioelectricity signal characteristics of the detected person, and evaluating the cell bioelectricity signal characteristic deviation condition of the detected person; counting the continuous similarity of the cell bioelectricity frequency of the focus area, and evaluating the frequency disorder degree of the cell bioelectricity signal of the focus area; and setting a focus area disorder degree gradient threshold, matching and corresponding the gradient threshold with the pathological symptom severity level, and retrieving a corresponding correction scheme.

Description

Technical Field

[0001] The present invention relates to the field of detection and evaluation of cell bioelectric frequency, and in particular to a detection, evaluation and correction method based on cell bioelectric frequency. Background Art

[0002] Living cells, whether at rest or in motion, experience electrical activity, known as bioelectricity. Bioelectricity primarily occurs across the cell membrane, hence the name transmembrane potential, or simply membrane potential. It primarily consists of resting potential and action potential.

[0003] Disruptions in cellular bioelectrical signaling can have profound impacts on a variety of physiological functions. Bioelectrical signals play a crucial role in communication between cells and tissues, particularly in nerves, muscles, and heart tissue. Disruptions in these signals can lead to serious consequences, including neurological and cardiovascular problems and muscle dysfunction.

[0004] Current cell electrical signal analysis generally only monitors cell electrical signals in a single part of the human body and is unable to perform simultaneous analysis and screening monitoring of multiple parts. Therefore, how to analyze the cell electrical signal characteristics of various regions of the human body to achieve simultaneous detection, evaluation, and correction method selection of bioelectricity in multiple parts is an urgent problem that needs to be solved. Summary of the Invention

[0005] In order to solve the above technical problems, a detection, evaluation and correction method based on cell bioelectric frequency is provided. This technical solution solves the problems raised in the above background technology.

[0006] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0007] Detection, evaluation and correction methods based on cell bioelectric frequency, including:

[0008] Based on the impact of abnormal cell bioelectricity on the human body, multi-dimensional lesion area division is carried out, and the results of lesion area division are quantitatively marked as critical, and cell bioelectricity monitoring sites are selected;

[0009] Select the correction plan based on the pathological symptoms and severity of each lesion area;

[0010] Divide the basic information conditions of the human body into conditional combinations, select test samples, and establish the cell bioelectric health characteristics of different lesion areas;

[0011] Analyze the bioelectric signal characteristics of the tested person and evaluate the deviation of the cellular bioelectric signal characteristics of the tested person;

[0012] Count the continuous similarity of the bioelectrical frequency of cells in the lesion area of ​​the sub-health characteristic of the tested person at the same time scale, and evaluate the degree of disorder of the bioelectrical signal frequency of cells in the lesion area;

[0013] According to the degree of disorder in the frequency of bioelectric signals in the lesion area, a gradient threshold of the disorder degree in the lesion area is set, the gradient threshold is matched with the severity level of the pathological symptoms, and the corresponding correction plan is retrieved.

[0014] Preferably, the multi-dimensional lesion area division is performed according to the impact of abnormal cell bioelectricity on the human body, and the lesion area division results are quantitatively labeled as critical. The cell bioelectricity monitoring sites are specifically selected to include:

[0015] Multi-dimensional lesion area division is carried out according to the body parts, functional areas, and human system components affected by abnormal bioelectricity of human cells;

[0016] Based on medical standards, all lesion classification methods are individually evaluated for the severity of cellular bioelectrical abnormalities in the lesion area, and the risk level standards for each lesion classification method are set, and all lesion areas are quantitatively marked according to the lesion classification method to which they belong;

[0017] Screen all lesion areas that exceed the set risk level standards after quantitative annotation and mark them as high-risk lesion areas;

[0018] According to the body parts where all lesion areas are located, the cell bioelectric monitoring sites are selected, and the parts where high-risk lesion areas are located and the parts that overlap with the lesion areas in each lesion division method are set as mandatory cell bioelectric monitoring sites, and the remaining lesion areas are set as optional cell bioelectric monitoring sites.

[0019] Preferably, the correction scheme selected according to the pathological symptoms and severity level of each lesion area specifically includes:

[0020] Obtain typical pathological symptoms and correction plans corresponding to abnormal bioelectricity of cells in all lesion areas of the human body through historical cases;

[0021] The severity level of pathological symptoms is set based on the severity of the pathological symptoms in each lesion area. The correction scheme is manually selected and adjusted according to the severity level of the pathological symptoms. At the same time, the correction scheme combination is selected based on the risk level order after quantitative annotation of the superimposed lesion area;

[0022] Establish a hash mapping relationship between the severity of pathological symptoms in all lesion areas and their corresponding correction plans.

[0023] Preferably, the method of dividing the basic information conditions of the human body into conditional combinations, selecting test samples, and establishing the cell bioelectric health characteristics of different lesion areas specifically includes:

[0024] Set height, weight, and age gradient intervals, and completely permutate and combine the height, weight, and age in different gradient intervals into basic information gradient combinations. For each basic information gradient combination, select one or more healthy individuals as test samples for the gradient combination.

[0025] Establish cell bioelectric signal feature vectors for all test samples. The specific steps for establishing cell bioelectric signal feature vectors are as follows:

[0026] Set a time interval T1, perform cell bioelectricity monitoring on the test sample according to the selected cell bioelectricity monitoring site within T1, and obtain a time series change curve of the cell bioelectricity signal waveform of each monitoring site according to the monitoring results;

[0027] Set a unit time scale, segment the time series change curves of the cell bioelectric signal waveforms of all monitored parts, count the number of complete cycle oscillations of the waveform within each unit time scale, and calculate the cell bioelectric signal frequency based on the ratio of the number of oscillations to the unit time scale;

[0028] The average oscillation amplitude of the bioelectric wave is calculated based on the peak and trough deviation of the complete waveform of the bioelectric signal of all cells within each unit time scale;

[0029] The cell bioelectric signal feature vector is established based on the changes in the cell bioelectric signal waveform, the cell bioelectric signal frequency and the average oscillation amplitude at each monitoring site. The cell bioelectric signal feature vectors of one or more test samples are aggregated to establish the cell bioelectric signal feature vector of each monitoring site of all gradient combination test samples, which are marked as healthy cell bioelectric signal feature vectors.

[0030] Preferably, analyzing the bioelectric signal characteristics of the detected person and evaluating the deviation of the cell bioelectric signal characteristics of the detected person specifically includes:

[0031] Obtain the basic information of the height, weight, and age of the person being tested, and obtain the bioelectrical signal feature vectors of healthy cells in all monitored parts based on the basic information gradient combination of height, weight, and age;

[0032] Establishing the cell bioelectric signal feature vectors of all monitored parts of the monitored person according to the cell bioelectric signal feature vector establishment steps;

[0033] The cell bioelectric signal feature vectors of all monitored parts of the tested person are compared with the healthy cell bioelectric signal feature vectors. The specific process is as follows:

[0034] Calculate the Euclidean distance and cosine similarity between the cell bioelectric signal feature vector and the healthy cell bioelectric signal feature vector. Calculate the cell bioelectric signal feature deviation index of the tested person based on the Euclidean distance and cosine similarity. The calculation formula for the cell bioelectric signal feature deviation index is:

[0035]

[0036] Where, is the characteristic deviation index of the cell bioelectric signal at the i-th monitoring site, d i 、cos(θ) i are the Euclidean distance and cosine similarity between the bioelectrical signal feature vector of the cell at the i-th monitoring site and the bioelectrical signal feature vector of the healthy cell;

[0037] A threshold value of the cell bioelectric signal characteristic deviation index is set, and the lesion areas corresponding to the monitoring parts whose cell bioelectric signal characteristic deviation index is greater than or equal to the threshold value of the cell bioelectric signal characteristic deviation index are screened and marked as sub-health characteristic lesion areas.

[0038] Preferably, the statistically analyzing the continuous similarity of the bioelectrical frequencies of cells in the lesion area of ​​the sub-health characteristic of the tested person at the same time scale and evaluating the degree of disorder of the bioelectrical signal frequencies of cells in the lesion area specifically includes:

[0039] Set the cell bioelectric signal frequency monitoring window size and establish a monitoring window queue;

[0040] Set a fixed time interval T2, and detect the number of cell bioelectric signal oscillations within this T2 time interval every T2 time. Calculate the cell bioelectric signal frequency of this detection based on the number of cell bioelectric signal oscillations, update the value to the end of the monitoring window queue, and dequeue the historical data at the head of the queue;

[0041] According to the numerical similarity of the bioelectric signal frequencies of adjacent time series cells stored in the monitoring window queue, the bioelectric signal frequency disorder index is calculated. The calculation formula of the bioelectric signal frequency disorder index is:

[0042]

[0043] Where, is the frequency disorder index of cell bioelectrical signals in the jth sub-health lesion area, f ij is the ith detected cell bioelectrical signal frequency of the jth sub-health lesion area, and n is the cell bioelectrical signal frequency monitoring window size;

[0044] The minimum threshold of the bioelectric signal frequency disorder index is set, and the lesion area whose bioelectric signal frequency disorder index is greater than or equal to the minimum threshold of the bioelectric signal frequency disorder index is screened out and marked as the lesion area to be corrected.

[0045] Preferably, the step of setting a gradient threshold of the disorder degree of the lesion area according to the disorder degree of the bioelectric signal frequency in the lesion area, matching the gradient threshold with the severity level of the pathological symptoms, and retrieving the corresponding correction plan specifically includes:

[0046] Setting the gradient threshold of the bioelectric signal frequency disorder index of all lesion areas in each lesion area division method, and establishing a corresponding matching relationship between each gradient threshold and the severity level of the pathological symptoms;

[0047] Obtaining the lesion area to be corrected, and obtaining the severity level of the corresponding pathological symptoms according to the threshold gradient interval of the bioelectric signal frequency disorder index of the lesion area to be corrected;

[0048] Obtain the mapping relationship between the severity level of the pathological symptoms in the lesion area and its corresponding correction plan, and retrieve the corresponding correction plan according to the severity level of the pathological symptoms in the lesion area to be corrected to correct the person being tested.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] According to the impact of abnormal cell bioelectricity on the human body, a multi-dimensional lesion area division is performed, and the results of the lesion area division are quantitatively labeled with the severity, marking high-risk lesions. Abnormal cell bioelectricity in high-position lesions causes greater damage to the human body. The correction plan corresponding to the theoretical state of each lesion area is obtained based on historical cases. Different test samples are selected according to the basic conditions of the human body, and healthy human test samples are selected to detect the cell electrical signal characteristics of each lesion area. By analyzing the deviation between the cell electrical signal characteristics of the tested person and the characteristics of the healthy human test samples, the cell electrical signal abnormal lesion area and location of the tested person can be quickly and accurately detected. By further evaluating the degree of electrical signal disorder in the area with abnormal cell electrical signals, a comprehensive electrical signal health assessment of the body parts can be flexibly and accurately performed, and the corresponding correction method can be retrieved for correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of the detection, evaluation and correction method based on cell bioelectric frequency of the present invention;

[0052] Figure 2 The present invention divides the lesion area into multiple dimensions according to the impact of abnormal cell bioelectricity on the human body, quantitatively labels the results of the lesion area division, and selects a flow chart of the cell bioelectricity monitoring site;

[0053] Figure 3 A flow chart of selecting a correction plan based on the pathological symptoms and severity level of each lesion area according to the present invention;

[0054] Figure 4 The present invention divides the basic information conditions of the human body into conditional combinations, selects test samples, and establishes a flow chart of cell bioelectric health characteristics in different lesion areas;

[0055] Figure 5 This is a flow chart of analyzing the bioelectric signal characteristics of the detected person and evaluating the deviation of the cell bioelectric signal characteristics of the detected person;

[0056] Figure 6 A flow chart for calculating the continuous similarity of the bioelectrical frequencies of cells in the lesion area of ​​the sub-health characteristic of the tested person at the same time scale and evaluating the degree of disorder of the bioelectrical signal frequencies of cells in the lesion area;

[0057] Figure 7 The present invention sets a gradient threshold of the disorder degree of the lesion area according to the disorder degree of the bioelectric signal frequency in the lesion area, matches the gradient threshold with the severity level of the pathological symptoms, and retrieves the corresponding correction plan flow chart. DETAILED DESCRIPTION

[0058] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0059] Reference Figure 1 As shown, the detection, evaluation and correction methods based on cell bioelectric frequency include:

[0060] Based on the impact of abnormal cell bioelectricity on the human body, multi-dimensional lesion area division is carried out, and the results of lesion area division are quantitatively marked as critical, and cell bioelectricity monitoring sites are selected;

[0061] Select the correction plan based on the pathological symptoms and severity of each lesion area;

[0062] Divide the basic information conditions of the human body into conditional combinations, select test samples, and establish the cell bioelectric health characteristics of different lesion areas;

[0063] Analyze the bioelectric signal characteristics of the tested person and evaluate the deviation of the cellular bioelectric signal characteristics of the tested person;

[0064] Count the continuous similarity of the bioelectrical frequency of cells in the lesion area of ​​the sub-health characteristic of the tested person at the same time scale, and evaluate the degree of disorder of the bioelectrical signal frequency of cells in the lesion area;

[0065] According to the degree of disorder in the frequency of bioelectric signals in the lesion area, a gradient threshold of the disorder degree in the lesion area is set, the gradient threshold is matched with the severity level of the pathological symptoms, and the corresponding correction plan is retrieved.

[0066] Reference Figure 2 As shown in the figure, according to the impact of abnormal cell bioelectricity on the human body, a multi-dimensional lesion area division is performed, and the results of the lesion area division are quantitatively labeled as critical. The specific cell bioelectricity monitoring sites include:

[0067] Multi-dimensional lesion area division is carried out according to the body parts, functional areas, and human system components affected by abnormal bioelectricity of human cells;

[0068] Based on medical standards, all lesion classification methods are individually evaluated for the severity of cellular bioelectrical abnormalities in the lesion area, and the risk level standards for each lesion classification method are set, and all lesion areas are quantitatively marked according to the lesion classification method to which they belong;

[0069] Screen all lesion areas that exceed the set risk level standards after quantitative annotation and mark them as high-risk lesion areas;

[0070] According to the body parts where all lesion areas are located, the cell bioelectric monitoring sites are selected, and the parts where high-risk lesion areas are located and the parts that overlap with the lesion areas in each lesion division method are set as mandatory cell bioelectric monitoring sites, and the remaining lesion areas are set as optional cell bioelectric monitoring sites.

[0071] Some parts of the human body rely heavily on the normal conduction and regulation of electrical signals to maintain their function. For example, electrical signals transmit information between neurons in the brain, regulating functions such as thinking, memory, emotion, and behavior. Abnormal bioelectrical signals can lead to a variety of neurological diseases, including epilepsy, Parkinson's disease, and mental disorders. The normal beating of the heart depends on electrical signals in the cardiac conduction system, which coordinate the contraction of the atria and ventricles. Problems such as arrhythmias, atrial fibrillation, and cardiac arrest are all related to abnormal cardiac electrical signals. By stratifying the risk of diseased areas in the human body, it is possible to focus on monitoring cellular bioelectrical signals in critical areas.

[0072] Reference Figure 3 As shown in the figure, the correction scheme is selected according to the pathological symptoms and severity of each lesion area, including:

[0073] Obtain typical pathological symptoms and correction plans corresponding to abnormal bioelectricity of cells in all lesion areas of the human body through historical cases;

[0074] The severity level of pathological symptoms is set based on the severity of the pathological symptoms in each lesion area. The correction scheme is manually selected and adjusted according to the severity level of the pathological symptoms. At the same time, the correction scheme combination is selected based on the risk level order after quantitative annotation of the superimposed lesion area;

[0075] Establish a hash mapping relationship between the severity of pathological symptoms in all lesion areas and their corresponding correction plans.

[0076] Historical case histories reveal that abnormalities in various bioelectric signals can lead to different symptoms, and different treatment options are available for each condition. Treatment typically includes medication, surgery, physical therapy, psychotherapy, and other interventions. Correct diagnosis and personalized treatment plans are key to managing these conditions. Mapping the severity of pathological symptoms to their corresponding corrective measures allows for rapid retrieval of corrective measures in the event of bioelectric signal abnormalities.

[0077] Reference Figure 4 As shown, the basic information conditions of the human body are divided into conditional combinations, test samples are selected, and the cell bioelectric health characteristics of different lesion areas are established, including:

[0078] Set height, weight, and age gradient intervals, and completely permutate and combine the height, weight, and age in different gradient intervals into basic information gradient combinations. For each basic information gradient combination, select one or more healthy individuals as test samples for the gradient combination.

[0079] Establish cell bioelectric signal feature vectors for all test samples. The specific steps for establishing cell bioelectric signal feature vectors are as follows:

[0080] Set a time interval T1, perform cell bioelectricity monitoring on the test sample according to the selected cell bioelectricity monitoring site within T1, and obtain a time series change curve of the cell bioelectricity signal waveform of each monitoring site according to the monitoring results;

[0081] Set a unit time scale, segment the time series change curves of the cell bioelectric signal waveforms of all monitored parts, count the number of complete cycle oscillations of the waveform within each unit time scale, and calculate the cell bioelectric signal frequency based on the ratio of the number of oscillations to the unit time scale;

[0082] The average oscillation amplitude of the bioelectric wave is calculated based on the peak and trough deviation of the complete waveform of the bioelectric signal of all cells within each unit time scale;

[0083] The cell bioelectric signal feature vector is established based on the changes in the cell bioelectric signal waveform, the cell bioelectric signal frequency and the average oscillation amplitude at each monitoring site. The cell bioelectric signal feature vectors of one or more test samples are aggregated to establish the cell bioelectric signal feature vector of each monitoring site of all gradient combination test samples, which are marked as healthy cell bioelectric signal feature vectors.

[0084] Bioelectric signal feature vectors are multidimensional vectors used to describe the characteristics of bioelectric signals. Each dimension represents a characteristic or attribute. These characteristics can be used to reflect the basic characteristics of cellular bioelectric signals in healthy individuals and serve as healthy comparison samples to assess the health of the subject's cellular bioelectric signals.

[0085] Reference Figure 5 As shown, analyzing the bioelectric signal characteristics of the detected person and evaluating the deviation of the cell bioelectric signal characteristics of the detected person specifically include:

[0086] Obtain the basic information of the height, weight, and age of the person being tested, and obtain the bioelectrical signal feature vectors of healthy cells in all monitored parts based on the basic information gradient combination of height, weight, and age;

[0087] Establishing the cell bioelectric signal feature vectors of all monitored parts of the monitored person according to the cell bioelectric signal feature vector establishment steps;

[0088] The cell bioelectric signal feature vectors of all monitored parts of the tested person are compared with the healthy cell bioelectric signal feature vectors. The specific process is as follows:

[0089] Calculate the Euclidean distance and cosine similarity between the cell bioelectric signal feature vector and the healthy cell bioelectric signal feature vector. Calculate the cell bioelectric signal feature deviation index of the tested person based on the Euclidean distance and cosine similarity. The calculation formula for the cell bioelectric signal feature deviation index is:

[0090]

[0091] Where, is the characteristic deviation index of the cell bioelectric signal at the i-th monitoring site, d i 、cos(θ) i are the Euclidean distance and cosine similarity between the bioelectrical signal feature vector of the cell at the i-th monitoring site and the bioelectrical signal feature vector of the healthy cell;

[0092] A threshold value of the cell bioelectric signal characteristic deviation index is set, and the lesion areas corresponding to the monitoring parts whose cell bioelectric signal characteristic deviation index is greater than or equal to the threshold value of the cell bioelectric signal characteristic deviation index are screened and marked as sub-health characteristic lesion areas.

[0093] Among them, the Euclidean distance can reflect the deviation of the frequency and amplitude of the cell bioelectric signal, and the cosine similarity can reflect the shape deviation of the waveform. The value range of is 0 to 1. The closer to 0, the higher the similarity of the vectors. The value range of cosine similarity is between -1 and 1. The closer to 1, the higher the similarity. Set the range shift to (The range of translation is adjustable and needs to be greater than 1), the calculated cell bioelectric signal characteristic deviation index range is between 0 and between.

[0094] The cell bioelectric signal characteristic deviation index threshold is set by professionals in this field based on the size of the cell bioelectric signal characteristic deviation index, the set range translation amount, and the monitoring sensitivity requirements.

[0095] Reference Figure 6 As shown in the figure, the continuous similarity of the bioelectrical frequency of cells in the lesion area of ​​the sub-health characteristic of the tested person at the same time scale is counted, and the degree of disorder of the bioelectrical signal frequency of cells in the lesion area is evaluated, specifically including:

[0096] Set the cell bioelectric signal frequency monitoring window size and establish a monitoring window queue;

[0097] Set a fixed time interval T2, and detect the number of cell bioelectric signal oscillations within this T2 time interval every T2 time. Calculate the cell bioelectric signal frequency of this detection based on the number of cell bioelectric signal oscillations, update the value to the end of the monitoring window queue, and dequeue the historical data at the head of the queue;

[0098] According to the numerical similarity of the bioelectric signal frequencies of adjacent time series cells stored in the monitoring window queue, the bioelectric signal frequency disorder index is calculated. The calculation formula of the bioelectric signal frequency disorder index is:

[0099]

[0100] Where, is the frequency disorder index of cell bioelectrical signals in the jth sub-health lesion area, f ij is the ith detected cell bioelectrical signal frequency of the jth sub-health lesion area, and n is the cell bioelectrical signal frequency monitoring window size;

[0101] The minimum threshold of the bioelectric signal frequency disorder index is set, and the lesion area whose bioelectric signal frequency disorder index is greater than or equal to the minimum threshold of the bioelectric signal frequency disorder index is screened out and marked as the lesion area to be corrected.

[0102] The minimum threshold of the bioelectric signal frequency disorder index is set by professional and technical personnel in this field based on the size of the bioelectric signal frequency disorder index and the requirements for accuracy and sensitivity of pathological monitoring.

[0103] The degree of disorder in bioelectric signals can reflect many aspects of physiological and pathological conditions, including disease severity and progression. Monitoring and analyzing the degree of disorder in bioelectric signals is an important means of diagnosing, evaluating, and managing many diseases.

[0104] Reference Figure 7 As shown in the figure, according to the degree of disorder of the bioelectric signal frequency in the lesion area, a gradient threshold of the disorder degree in the lesion area is set, and the gradient threshold is matched with the severity level of the pathological symptoms. The corresponding correction plan is retrieved, which specifically includes:

[0105] Setting the gradient threshold of the bioelectric signal frequency disorder index of all lesion areas in each lesion area division method, and establishing a corresponding matching relationship between each gradient threshold and the severity level of the pathological symptoms;

[0106] Obtaining the lesion area to be corrected, and obtaining the severity level of the corresponding pathological symptoms according to the threshold gradient interval of the bioelectric signal frequency disorder index of the lesion area to be corrected;

[0107] Obtain the mapping relationship between the severity level of the pathological symptoms in the lesion area and its corresponding correction plan, and retrieve the corresponding correction plan according to the severity level of the pathological symptoms in the lesion area to be corrected to correct the person being tested.

[0108] By setting the gradient threshold of the bioelectric signal disorder index to correspond to the severity level of the pathological symptoms, a more targeted correction plan can be selected.

[0109] The degree of disturbance in bioelectrical signals can indeed reflect the severity of a disease. Bioelectrical signals play a key role in systems such as the nervous system, heart, and muscles, and disturbances in these signals are associated with specific diseases or pathological conditions. By analyzing and quantifying abnormalities in these signals, we can understand the severity and progression of a disease.

[0110] Furthermore, the present solution also proposes a storage medium for a detection, evaluation and correction method based on cell bioelectric frequency, on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned detection, evaluation and correction method based on cell bioelectric frequency is executed.

[0111] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0112] In summary, the advantages of the present invention are:

[0113] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A detection, evaluation and correction method based on cell bioelectric frequency, characterized in that: include: According to the impact of abnormal cell bioelectricity on the human body, multi-dimensional lesion area division is carried out, and the results of lesion area division are quantitatively marked for severity, and cell bioelectricity monitoring sites are selected; Select the correction plan according to the pathological symptoms and severity level of each lesion area; Divide the basic information conditions of the human body into conditional combinations, select test samples, and establish the cell bioelectric health characteristics of different lesion areas; Analyze the bioelectric signal characteristics of the tested person and evaluate the deviation of the cell bioelectric signal characteristics of the tested person; Count the continuous similarity of the bioelectric frequency of cells in the lesion area of ​​the sub-health characteristics of the tested person at the same time scale, and evaluate the degree of disorder of the frequency of bioelectric signals of cells in the lesion area; According to the degree of disorder in the frequency of bioelectric signals in the lesion area, a gradient threshold of the disorder degree in the lesion area is set, the gradient threshold is matched with the severity level of the pathological symptoms, and the corresponding correction plan is retrieved.

2. The detection, evaluation and correction method based on cell bioelectric frequency according to claim 1, characterized in that: The multi-dimensional lesion area division is performed according to the impact of abnormal cell bioelectricity on the human body, and the results of the lesion area division are quantitatively labeled for criticality. The cell bioelectricity monitoring sites are specifically selected to include: Multi-dimensional lesion area division is carried out according to the body parts, functional areas, and human system components affected by abnormal bioelectricity of human cells; Based on medical standards, all lesion division methods are individually evaluated for the severity of cell bioelectric abnormalities in the lesion area, and the hazard level standard for each lesion division method is set, and all lesion areas are quantitatively marked in the lesion division method to which they belong; Screen all lesion areas that exceed the set risk level standards after quantitative annotation and mark them as high-risk lesion areas; According to the body parts where all lesion areas are located, the cellular bioelectric monitoring sites are selected, and the parts where high-risk lesion areas are located and the parts that overlap with the lesion areas in each lesion division method are set as mandatory cellular bioelectric monitoring sites, and the remaining lesion areas are set as optional cellular bioelectric monitoring sites.

3. The detection, evaluation and correction method based on cell bioelectric frequency according to claim 2, characterized in that: The correction scheme selected according to the pathological symptoms and severity level of each lesion area specifically includes: Obtain typical pathological symptoms and correction plans corresponding to abnormal bioelectricity of cells in all lesion areas of the human body through historical cases; The severity level of pathological symptoms is set based on the severity of the pathological symptoms in each lesion area, and the correction scheme is manually selected and adjusted according to the severity level of the pathological symptoms. At the same time, the correction scheme combination is selected based on the danger level order after quantitative annotation of the lesion superimposed area; Establish a hash mapping relationship between the severity of pathological symptoms in all lesion areas and their corresponding correction plans.

4. The detection, evaluation and correction method based on cell bioelectric frequency according to claim 3, characterized in that: The conditional combination division of the basic information conditions of the human body, the selection of test samples, and the establishment of the cell bioelectric health characteristics of different lesion areas specifically include: Set height, weight, and age gradient intervals, and completely arrange and combine height, weight, and age in different gradient intervals to mark them as basic information gradient combinations. For each basic information gradient combination, select one or more healthy people as test samples for the gradient combination; For all test samples, a cell bioelectric signal feature vector is established. The specific steps for establishing the cell bioelectric signal feature vector are as follows: A time interval T1 is set, and cell bioelectricity monitoring is performed on the test sample according to the selected cell bioelectricity monitoring site within T1, and a time series change curve of the cell bioelectricity signal waveform of each monitoring site is obtained according to the monitoring result; Set the unit time scale, segment the time series change curves of the cell bioelectric signal waveforms of all monitored parts, count the number of complete cycle oscillations of the waveform within each unit time scale, and calculate the cell bioelectric signal frequency based on the ratio of the number of oscillations to the unit time scale; The average oscillation amplitude of the bioelectric wave is calculated based on the peak and trough deviation of the complete waveform of the bioelectric signal of all cells within each unit time scale; The cell bioelectric signal feature vector is established based on the changes in the cell bioelectric signal waveform, the cell bioelectric signal frequency and the average oscillation amplitude at each monitoring site. The cell bioelectric signal feature vectors of one or more test samples are aggregated to establish the cell bioelectric signal feature vector of each monitoring site of all gradient combination test samples and mark it as a healthy cell bioelectric signal feature vector.

5. The detection, evaluation and correction method based on cell bioelectric frequency according to claim 4, characterized in that: The analyzing the bioelectric signal characteristics of the detected person and evaluating the deviation of the cell bioelectric signal characteristics of the detected person specifically includes: Obtain the basic information of the height, weight and age of the person being tested, and obtain the bioelectric signal characteristic vectors of healthy cells in all monitored parts according to the basic information gradient combination of height, weight and age; Establishing the cell bioelectric signal characteristic vectors of all monitored parts of the monitored person according to the cell bioelectric signal characteristic vector establishment steps; The cell bioelectric signal feature vectors of all monitored parts of the tested person are compared with the healthy cell bioelectric signal feature vectors. The specific process is as follows: The Euclidean distance and cosine similarity between the cell bioelectric signal feature vector and the healthy cell bioelectric signal feature vector are calculated, and the cell bioelectric signal feature deviation index of the tested person is calculated based on the Euclidean distance and cosine similarity. The cell bioelectric signal feature deviation index calculation formula is: In the formula, is the characteristic deviation index of the cell bioelectric signal at the i-th monitoring site, d i 、cos(θ) i are the Euclidean distance and cosine similarity between the cell bioelectric signal feature vector of the ith monitoring site and the healthy cell bioelectric signal feature vector; A threshold value of the cell bioelectric signal characteristic deviation index is set, and the lesion area corresponding to the monitoring part whose cell bioelectric signal characteristic deviation index is greater than or equal to the threshold value of the cell bioelectric signal characteristic deviation index is screened and marked as a sub-health characteristic lesion area.

6. The method for detecting, evaluating and correcting cell bioelectric frequency according to claim 5, characterized in that: The method of counting the continuous similarity of the bioelectric frequency of cells in the sub-health characteristic lesion area of ​​the detected person at the same time scale and evaluating the degree of disorder of the frequency of the bioelectric signal of cells in the lesion area specifically includes: Set the cell bioelectric signal frequency monitoring window size and establish a monitoring window queue; Set a fixed time interval T2, detect the number of cell bioelectric signal oscillations within this T2 time interval every T2 time, calculate the cell bioelectric signal frequency detected this time according to the number of cell bioelectric signal oscillations, update the value to the end of the monitoring window queue, and dequeue the historical data at the head of the queue; According to the numerical similarity of the bioelectric signal frequencies of adjacent time series cells stored in the monitoring window queue, the bioelectric signal frequency disorder index is calculated. The calculation formula of the bioelectric signal frequency disorder index is: In the formula, is the frequency disorder index of the cell bioelectric signal in the jth sub-healthy lesion area, f ij is the frequency of the ith detected cell bioelectric signal in the jth sub-healthy lesion area, and n is the monitoring window size of the cell bioelectric signal frequency; The minimum threshold of the bioelectric signal frequency disorder index is set, and the lesion area whose bioelectric signal frequency disorder index is greater than or equal to the minimum threshold of the bioelectric signal frequency disorder index is screened out and marked as the lesion area to be corrected.

7. The method for detecting, evaluating and correcting cell bioelectric frequency according to claim 6, characterized in that: According to the disorder degree of the bioelectric signal frequency in the lesion area, a gradient threshold of the disorder degree in the lesion area is set, the gradient threshold is matched with the severity level of the pathological symptoms, and the corresponding correction scheme is retrieved, which specifically includes: Setting the gradient threshold of the bioelectric signal frequency disorder index of all lesion areas in each lesion area division method, and establishing a corresponding matching relationship between each gradient threshold and the severity level of the pathological symptoms; Obtaining the lesion area to be corrected, and obtaining the severity level of the corresponding pathological symptoms according to the threshold gradient interval of the bioelectric signal frequency disorder index of the lesion area to be corrected; The mapping relationship between the severity level of the pathological symptoms in the lesion area and its corresponding correction scheme is obtained, and the corresponding correction scheme is retrieved according to the severity level of the pathological symptoms in the lesion area to be corrected to correct the person being tested.

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