A bioelectrical impedance data analysis method for intestinal inflammation detection

Abstract

The present application relates to the technical field of data processing, in particular to a bioelectrical impedance data analysis method for intestinal inflammation detection, bioelectrical impedance data of multiple different monitoring points in the intestinal region are acquired, and the fluctuation normality of the bioelectrical impedance data of each monitoring point is determined; the corresponding relationship between the fluctuation normality of the bioelectrical impedance data of each monitoring point and the food residue accumulation degree of each monitoring point is determined, and initial abnormal monitoring points are acquired; the initial abnormal monitoring points are sorted according to the flow order of food in the intestinal tract, the association between each initial abnormal monitoring point and the abnormal reason of the adjacent initial abnormal monitoring point under the sorting is determined, the target abnormal monitoring points are screened from the initial abnormal monitoring points, the accurate acquisition of the abnormal monitoring points is realized, and the accuracy of the bioelectrical impedance data acquisition is ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a bioelectrical impedance data analysis method for detecting intestinal inflammation. Background Technology

[0002] Currently, intestinal bioelectrical impedance data is commonly used for detecting intestinal inflammation. Bioelectrical impedance analysis (BIA) measures the response of biological tissues to external electrical stimuli (resistance, reactance, phase angle, etc.) to reflect their composition (such as water, fat, and muscle) and structural characteristics. Typically, multiple monitoring points are set up at different locations within the intestinal region, and bioelectrical impedance data are acquired at each point using a bioelectrical impedance analyzer. However, bioelectrical impedance data from different monitoring points are susceptible to signal interference from other factors, with physiological factors, such as the distribution of food residue, having a significant impact. The presence of food residue in the intestine can interfere with the transmission of bioelectrical impedance signals at different monitoring points, leading to fluctuations in the data and the identification of abnormal monitoring points. The presence of abnormal monitoring points affects the accuracy of bioelectrical impedance data. Therefore, identifying abnormal monitoring points is crucial. However, existing methods for obtaining abnormal monitoring points rely solely on the presence of abnormal bioelectrical impedance data, neglecting the interference of food residue, resulting in low accuracy in identifying abnormal monitoring points. Summary of the Invention

[0003] To address the issue of low accuracy in acquiring existing anomaly monitoring points, the present invention aims to provide a bioelectrical impedance data analysis method for detecting intestinal inflammation. The specific technical solution adopted is as follows:

[0004] In a first aspect of the present invention, a bioelectrical impedance data analysis method for detecting intestinal inflammation is provided, comprising:

[0005] Bioelectrical impedance data were acquired from multiple monitoring points within the intestinal region, and the degree of normality of fluctuations in bioelectrical impedance data at each monitoring point was determined.

[0006] Determine the normality of fluctuations in bioelectrical impedance data at each monitoring point and the correlation between the degree of food residue accumulation at each monitoring point to obtain initial abnormal monitoring points;

[0007] The association between each initial abnormal monitoring point in the target sequence of initial abnormal monitoring points and the abnormal causes of adjacent initial abnormal monitoring points is determined, and target abnormal monitoring points are obtained by screening from the initial abnormal monitoring points; the target sequence of initial abnormal monitoring points is obtained by sorting the initial abnormal monitoring points according to the flow order of food in the intestine.

[0008] In an exemplary embodiment, the process of obtaining the initial anomaly monitoring point includes:

[0009] The monitoring points were arranged in order of increasing food residue accumulation to obtain the first monitoring point sequence;

[0010] The monitoring points are arranged in descending order of the degree of normality of fluctuation to obtain the second monitoring point sequence;

[0011] The monitoring points with different position numbers in the first and second monitoring point sequences are identified as the initial anomaly monitoring points.

[0012] In an exemplary embodiment, the association between the anomaly causes of each initial anomaly monitoring point in the initial anomaly monitoring point target sequence and the adjacent initial anomaly monitoring points is determined sequentially in reverse order of the initial anomaly monitoring point target sequence.

[0013] In an exemplary embodiment, if the Rth initial anomaly monitoring point in the target sequence of the initial anomaly monitoring points belongs to the second type of anomaly, then the Rth initial anomaly monitoring point is not a target anomaly monitoring point; otherwise, it is a target anomaly monitoring point; the number of the initial anomaly monitoring points is R.

[0014] For the r-th initial anomaly monitoring point in the target sequence of the initial anomaly monitoring points, r∈[2,R-1], if the r-th initial anomaly monitoring point belongs to the first type of anomaly, and the (r+1)-th initial anomaly monitoring point belongs to the second type of anomaly, then the r-th initial anomaly monitoring point is not the target anomaly monitoring point; otherwise, it is the target anomaly monitoring point.

[0015] If the r-th initial anomaly monitoring point belongs to the second type of anomaly, and the (r-1)-th initial anomaly monitoring point belongs to the first type of anomaly, then the r-th initial anomaly monitoring point is not the target anomaly monitoring point; otherwise, it is the target anomaly monitoring point.

[0016] The first type of anomaly is when the initial anomaly monitoring point's position number in the first monitoring point sequence is less than its position number in the second monitoring point sequence; the second type of anomaly is when the initial anomaly monitoring point's position number in the first monitoring point sequence is greater than its position number in the second monitoring point sequence; the first monitoring point sequence is obtained by arranging the monitoring points in order of increasing food residue aggregation degree; the second monitoring point sequence is obtained by arranging the monitoring points in order of decreasing fluctuation normality degree.

[0017] In an exemplary embodiment, if the first initial anomaly monitoring point in the target sequence of the initial anomaly monitoring points belongs to the first type of anomaly, and if the second initial anomaly monitoring point belongs to the second type of anomaly, then the first initial anomaly monitoring point is not a target anomaly monitoring point; otherwise, it is a target anomaly monitoring point.

[0018] In an exemplary embodiment, the process of obtaining the normality of fluctuations in bioelectrical impedance data at each monitoring point includes:

[0019] Obtain the average amplitude of the bioelectrical impedance data at the first monitoring point, and determine the number of instantaneous amplitude points that are greater than the average amplitude; the first monitoring point can be any monitoring point.

[0020] The bioelectrical impedance data of the first monitoring point is divided into periods, and the periodic stability of the bioelectrical impedance data of the first monitoring point is obtained based on the similarity between any two periods.

[0021] The degree of normality of the fluctuation of the bioelectrical impedance data of the first monitoring point is obtained based on the number of instantaneous amplitude points and the periodic stability; the degree of normality of the fluctuation is inversely proportional to the number of instantaneous amplitude points and directly proportional to the periodic stability.

[0022] In an exemplary embodiment, the process of obtaining the periodic stability includes: obtaining the similarity between any two periodic data and calculating the average similarity as the periodic stability.

[0023] In an exemplary embodiment, the calculation process for the degree of normality of the fluctuation includes:

[0024] Based on the number of instantaneous amplitude points and the total number of amplitude points in the bioelectrical impedance data of the first monitoring point, the percentage of the number of instantaneous amplitude points is obtained;

[0025] Calculate the difference between the value 1 and the stated quantity percentage;

[0026] The product of the difference and the periodic stability is calculated to obtain the degree of normality of the fluctuation.

[0027] In one exemplary embodiment, the bioelectrical impedance data analysis method further includes:

[0028] Compare the degree of normality of fluctuations at each target anomaly monitoring point with the preset normal fluctuation range;

[0029] Delete the bioelectrical impedance data of the target abnormal monitoring points that are not within the preset normal fluctuation range.

[0030] In an exemplary embodiment, the bioelectrical impedance data analysis method further includes: after deleting the bioelectrical impedance data of the target abnormal monitoring point that is not within the preset normal fluctuation range, re-collecting the bioelectrical impedance data of the target abnormal monitoring point that is not within the preset normal fluctuation range.

[0031] The present invention has the following beneficial effects: First, it determines the normality of fluctuations in bioelectrical impedance data at multiple different monitoring points within the intestinal region. Under normal circumstances, there is a corresponding relationship between the normality of fluctuations in bioelectrical impedance data at the same monitoring point and the degree of food residue accumulation. Therefore, based on this correspondence, initial abnormal monitoring points can be preliminarily screened from each monitoring point. Then, the initial abnormal monitoring points are sorted according to the order of food flow in the intestine. Under this sorting, adjacent monitoring points will inevitably have an influence due to the flow of food in the intestine, and there will inevitably be a correlation between the abnormal causes of adjacent monitoring points. Based on this correlation, target abnormal monitoring points can be screened from the initial abnormal monitoring points, thereby achieving accurate acquisition of abnormal monitoring points and ensuring the accuracy of bioelectrical impedance data acquisition. Attached Figure Description

[0032] Figure 1 This is a flowchart of a bioelectrical impedance data analysis method for detecting intestinal inflammation provided in one embodiment of the present invention;

[0033] Figure 2 This is a flowchart illustrating the process of obtaining the normality of fluctuations according to an embodiment of the present invention;

[0034] Figure 3 This is a specific calculation flowchart of the degree of normality of fluctuation provided by one embodiment of the present invention;

[0035] Figure 4 This is an impedance waveform diagram in a bioelectrical impedance waveform diagram provided in an embodiment of the present invention;

[0036] Figure 5 This is a voltage waveform diagram in a bioelectrical impedance waveform diagram provided in an embodiment of the present invention;

[0037] Figure 6 This is a flowchart of the process for obtaining initial anomaly monitoring points provided in one embodiment of the present invention;

[0038] Figure 7 This is a flowchart illustrating the steps of a bioelectrical impedance data analysis method for detecting intestinal inflammation provided in one embodiment of the present invention. Detailed Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. All data and information collected in this application have been obtained with full consent, and the collection, use, and processing of such information must comply with relevant regulations.

[0041] like Figure 1 As shown, this embodiment provides a bioelectrical impedance data analysis method for detecting intestinal inflammation, including the following steps:

[0042] Step 1: Obtain bioelectrical impedance data from multiple monitoring points within the intestinal region and determine the normality of fluctuations in bioelectrical impedance data at each monitoring point;

[0043] Step 2: Determine the normality of the fluctuations in bioelectrical impedance data at each monitoring point and the correlation between the degree of food residue accumulation at each monitoring point to obtain the initial abnormal monitoring points;

[0044] Step 3: Determine the correlation between the causes of abnormality of each initial abnormal monitoring point in the target sequence of initial abnormal monitoring points and the adjacent initial abnormal monitoring points, and select target abnormal monitoring points from the initial abnormal monitoring points; the target sequence of initial abnormal monitoring points is obtained by sorting the initial abnormal monitoring points according to the flow order of food in the intestine.

[0045] The following detailed description, in conjunction with the accompanying drawings, outlines each step of a bioelectrical impedance data analysis method for detecting intestinal inflammation provided in this embodiment.

[0046] Step 1: Obtain bioelectrical impedance data from multiple monitoring points within the intestinal region and determine the normality of fluctuations in bioelectrical impedance data at each monitoring point.

[0047] In a specific application, multiple monitoring points are set up in the intestinal region of the person being tested. A multi-electrode array is used, with each electrode positioned at a corresponding monitoring point. Each electrode is connected to a bioelectrical impedance analysis (BIA) instrument, and bioelectrical impedance data for each monitoring point is collected through the BIA and the electrodes. In an exemplary embodiment, the operating frequency range of the BIA instrument is 100Hz-1000Hz to ensure that dynamic changes in the bioelectrical impedance signal can be captured. Moreover, the sampling frequency of each electrode is set according to the actual situation. This embodiment uses a frequency of 10 times per second as an example to collect bioelectrical impedance data. Thus, bioelectrical impedance data for various monitoring points in the intestinal region are obtained. Therefore, for any monitoring point, the bioelectrical impedance data for that monitoring point consists of bioelectrical impedance data from multiple sampling times. The value of the bioelectrical impedance at each sampling time is an amplitude value, and each sampling time is an amplitude point.

[0048] Furthermore, this embodiment also acquires the degree of food residue aggregation at each monitoring point. It should be understood that the degree of food residue aggregation varies at different locations within the intestinal region; that is, different types of intestines have different functions, resulting in varying degrees of food residue aggregation. For example, common knowledge suggests that the duodenum has a very low degree of food residue aggregation, the jejunum has a relatively low degree, the ileum has a moderate degree, the cecum and ascending colon have a moderate degree, the transverse colon has a relatively high degree, the descending colon and sigmoid colon have the highest degree, the rectum has a very low degree, and so on. Therefore, based on common knowledge and the corresponding intestinal type within the intestinal region, the degree of food residue aggregation at each monitoring point can be determined. It should be understood that since the monitoring points need to be ranked according to their degree of food residue aggregation, it is necessary to ensure that the degree of food residue aggregation at different monitoring points is different to facilitate ranking. As another implementation method, the testing personnel can also roughly determine the degree of food residue accumulation at each monitoring point in the intestinal region by obtaining the actual food residue accumulation in the actual ultrasound image of the intestinal region of the person being tested.

[0049] It is important to note that the water and electrolytes in food residue in the intestines can significantly alter the conductivity of local tissues. Therefore, excessive food residue in the intestines can lead to significant deviations in bioelectrical impedance data measured by multiple electrodes. Consequently, it is necessary to pre-treat the subjects to prevent excessive accumulation of food residue at each monitoring point. For example, subjects could fast for a certain period before testing, thus ensuring that the accumulation of food residue at each monitoring point is not excessive.

[0050] The number of monitoring points is set to M (i.e., the number of electrodes is M), and the degree of food residue accumulation at each monitoring point is expressed as follows: .

[0051] Normally, the waveform of bioelectrical impedance data obtained under compliant operation, i.e., the fluctuation of the bioelectrical impedance waveform, is relatively stable. However, the presence of food residue in the intestines can create areas of high local resistance, affecting the uniform conduction of current throughout the body and causing fluctuations in bioelectrical impedance data. Therefore, it is necessary to determine the normality of the fluctuations in bioelectrical impedance data at each monitoring point. In an exemplary embodiment, such as... Figure 2 The diagram illustrates a specific process for obtaining the degree of normality of fluctuations:

[0052] Step 1-1: Obtain the average amplitude of the bioelectrical impedance data of the first monitoring point and determine the number of instantaneous amplitude points that are greater than the average amplitude.

[0053] For ease of explanation, the first monitoring point is set to any monitoring point, and the waveform corresponding to the bioelectrical impedance data of the first monitoring point is the first bioelectrical impedance waveform.

[0054] The average amplitude of the first bioelectrical impedance waveform is obtained. In this embodiment, wavelet transform is used to obtain the average amplitude of the first bioelectrical impedance waveform. Since wavelet transform is an existing algorithm, it will not be described in detail. Moreover, the wavelet transform method is used to obtain the frequency, period, phase and other characteristics of the first bioelectrical impedance waveform.

[0055] Each amplitude point of the first bioelectrical impedance waveform is compared with its average amplitude. Amplitude points exceeding the average amplitude are identified, and their number is recorded as the instantaneous amplitude point count. Since the fluctuations in the bioelectrical impedance waveform are relatively stable under normal conditions, a higher number of instantaneous amplitude points indicates greater instability in the waveform. Therefore, the degree of normality of fluctuation is inversely proportional to the number of instantaneous amplitude points.

[0056] Step 1-2: Divide the bioelectrical impedance data of the first monitoring point into periods, and obtain the periodic stability of the bioelectrical impedance data of the first monitoring point based on the similarity between any two periods.

[0057] Using the period of the first bioelectrical impedance waveform obtained in step 1-1, the first bioelectrical impedance waveform is divided into multiple periodic data. Then, the similarity between any two periodic data is obtained, and finally, the periodic stability of the first bioelectrical impedance waveform is obtained based on the similarity between any two periodic data. Specifically, the mean similarity between any two periodic data is used as the periodic stability of the first bioelectrical impedance waveform.

[0058] Obtaining the similarity between any two periods of data specifically involves acquiring the DTW (Dynamic Time Warping) distance between them, and then negatively correlating the DTW distances to obtain the similarity. In an exemplary embodiment, the formula for calculating periodic stability is as follows:

[0059] ;

[0060] in, This represents the periodic stability of the bioelectrical impedance waveform of the m-th electrode. and These represent the k-th and j-th periodic data in the bioelectrical impedance waveform of the m-th electrode, respectively. Essentially, they represent any two periodic data. K represents the number of periods in the bioelectrical impedance waveform of the m-th electrode. express and DTW distance value, This represents an exponential function with base e. Indicates to The negative correlation normalization.

[0061] Therefore, the higher the periodic stability of the first bioelectrical impedance waveform, the more stable the period of the first bioelectrical impedance waveform, and the higher the degree of normality of the fluctuation of the first bioelectrical impedance waveform. That is, the degree of normality of fluctuation is directly proportional to the periodic stability.

[0062] Steps 1-3: Based on the number of instantaneous amplitude points and periodic stability, obtain the normality of the fluctuation of the bioelectrical impedance data of the first monitoring point.

[0063] The degree of normality of fluctuations in the bioelectrical impedance data of the first monitoring point is obtained based on the number of instantaneous amplitude points and periodic stability. In an exemplary embodiment, such as... Figure 3 The diagram illustrates a specific calculation process for the degree of normality in fluctuations:

[0064] Steps 1-4: Based on the number of instantaneous amplitude points and the total number of amplitude points in the bioelectrical impedance data of the first monitoring point, obtain the percentage of the number of instantaneous amplitude points;

[0065] Steps 1-5: Calculate the difference between the value 1 and the quantity percentage;

[0066] Steps 1-6: Calculate the product of the difference and the periodic stability to obtain the degree of normality of the fluctuation.

[0067] Taking the bioelectrical impedance waveform of the m-th electrode as an example, the number of instantaneous amplitude points of the bioelectrical impedance waveform of the m-th electrode is set to... The number of bioelectrical impedances in the bioelectrical impedance waveform of the m-th electrode is obtained, which is the number of sampling times in the bioelectrical impedance waveform of the m-th electrode, i.e., the total number of amplitude points, denoted as . .calculate and The ratio of the quantities is denoted as . .

[0068] The formula for calculating the normality of fluctuations in the bioelectrical impedance waveform of the m-th electrode is as follows:

[0069] ;

[0070] in, This indicates the normality of fluctuations in the bioelectrical impedance waveform of the m-th electrode. When... The larger the value, the more The smaller the value, the greater the normality of the fluctuation in the bioelectrical impedance waveform of the m-th electrode, and the more normal the bioelectrical impedance waveform of the m-th electrode.

[0071] Using the above process, the degree of normality of fluctuations in bioelectrical impedance data at each monitoring point was obtained.

[0072] It should be understood that, generally speaking, bioelectrical impedance data mainly includes impedance data and voltage data. Therefore, the bioelectrical impedance waveform at the first monitoring point includes both impedance and voltage waveforms. For example... Figure 4 and Figure 5 As shown, Figure 4 It is an impedance waveform diagram. Figure 5 It is a voltage waveform diagram. Figure 4 and Figure 5 In the graph, the horizontal axis represents time, and the vertical axis represents the amplitude value of the corresponding data. Using the above process, the normality of fluctuations in the impedance waveform and voltage waveform of the first monitoring point are obtained. These two normality levels are then normalized, and the average of the two normality levels after normalization is calculated as the normality of fluctuations in the bioelectrical impedance waveform of the first monitoring point. Similarly, the normality of fluctuations in the bioelectrical impedance waveforms of other monitoring points is obtained in the same way.

[0073] Step 2: Determine the normality of the fluctuations in bioelectrical impedance data at each monitoring point and the correlation between the degree of food residue accumulation at each monitoring point to obtain the initial abnormal monitoring points.

[0074] Under normal circumstances, there is a correlation between the degree of fluctuation in bioelectrical impedance data at the same monitoring point and the degree of food residue accumulation. Therefore, based on this correlation, initial abnormal monitoring points can be preliminarily screened from various monitoring points. In an exemplary embodiment, such as... Figure 6 The diagram illustrates a specific process for obtaining initial anomaly monitoring points:

[0075] Step 2-1: Arrange the monitoring points in order of increasing food residue aggregation to obtain the first monitoring point sequence;

[0076] Step 2-2: Arrange the monitoring points in descending order of the degree of normality of fluctuation to obtain the second monitoring point sequence;

[0077] Step 2-3: Determine the monitoring points with different position numbers in the first monitoring point sequence and the second monitoring point sequence as the initial abnormal monitoring points.

[0078] The greater the accumulation of food residue, the more unstable the fluctuations in bioelectrical impedance data obtained from each monitoring point. Therefore, the sorting results of the first and second monitoring point sequences should be the same, that is, the position number of each monitoring point in the first monitoring point sequence and the position number in the second monitoring point sequence should be the same.

[0079] Using any sequence as a reference, such as the first monitoring point sequence, find the monitoring points in the second monitoring point sequence that have different position numbers from those in the first monitoring point sequence. The monitoring points that have different position numbers in the first and second monitoring point sequences are used as the initial abnormal monitoring points.

[0080] Let the initial number of abnormal monitoring points be R. When R is 0, it means that the sorting results of the first and second monitoring point sequences are the same, the two sequences are completely identical, and the bioelectrical impedance data of all corresponding monitoring points are valid. In this case, the validity of the bioelectrical impedance data of all monitoring points is 1. When R is not 0, it means that the validity of the bioelectrical impedance data of the acquired R monitoring points may be abnormal, and the validity of the bioelectrical impedance data of other non-abnormal monitoring points is recorded as 1.

[0081] Step 3: Determine the correlation between the causes of abnormality of each initial abnormal monitoring point in the target sequence of initial abnormal monitoring points and the adjacent initial abnormal monitoring points, and select target abnormal monitoring points from the initial abnormal monitoring points; the target sequence of initial abnormal monitoring points is obtained by sorting the initial abnormal monitoring points according to the flow order of food in the intestine.

[0082] Because the intestines are constantly in motion during the acquisition of bioelectrical impedance data at various monitoring points using a multi-electrode array, the food residue around each monitoring point will change position due to intestinal peristalsis, after the degree of food residue accumulation at each monitoring point is obtained above. This may cause deviations in the normality of fluctuations at some initial abnormal monitoring points, potentially leading to abnormalities in the validity of bioelectrical impedance data from certain initial abnormal monitoring points. Therefore, to select accurate abnormal monitoring points, i.e., target abnormal monitoring points, from the initial abnormal monitoring points, the initial abnormal monitoring points are sorted according to the order of food flow in the intestine, resulting in the target order of initial abnormal monitoring points. In this target order, the first initial abnormal monitoring point is the position that food reaches first, and so on, with the Rth initial abnormal monitoring point (i.e., the last initial abnormal monitoring point) being the position that food reaches last. It should be understood that the order of food flow in the intestine is common knowledge and will not be elaborated further.

[0083] In this embodiment, the correlation between the abnormal causes of each initial abnormal monitoring point and its adjacent initial abnormal monitoring point is determined sequentially in the reverse order of the initial abnormal monitoring point target sequence, that is, in the order from the Rth initial abnormal monitoring point to the first initial abnormal monitoring point, and the target abnormal monitoring point is obtained by screening from the initial abnormal monitoring points.

[0084] First, considering the different position indices of the initial abnormal monitoring point in the first and second monitoring point sequences, two abnormal scenarios were identified: the first scenario and the second scenario. The first scenario occurs when the position indices of the initial abnormal monitoring point in the first sequence are less than those in the second sequence. In other words, the initial abnormal monitoring point is positioned to the right in the second sequence compared to its position in the first sequence. This rightward shift may be due to reduced food residue at the location of the initial abnormal monitoring point in the intestine caused by intestinal peristalsis. The second scenario occurs when the position indices of the initial abnormal monitoring point in the first sequence are greater than those in the second sequence. In other words, the initial abnormal monitoring point is positioned to the left in the second sequence compared to its position in the first sequence. This leftward shift may be due to increased food residue at the location of the initial abnormal monitoring point in the intestine caused by intestinal peristalsis, resulting in an increase in food residue at the location of the initial abnormal monitoring point upstream of it.

[0085] The specific process for selecting target anomaly monitoring points from the initial anomaly monitoring points is as follows:

[0086] The analysis begins with the Rth initial abnormality monitoring point in the target sequence of initial abnormality monitoring points, which is the monitoring point at the very end of intestinal peristalsis.

[0087] Since the Rth initial anomaly monitoring point in the target sequence is the last initial anomaly monitoring point, if the Rth initial anomaly monitoring point has an anomaly, it can only belong to the second type of anomaly. Therefore, if the Rth initial anomaly monitoring point belongs to the second type of anomaly, it means that the actual situation of the Rth initial anomaly monitoring point is not abnormal, that is, the Rth initial anomaly monitoring point is not the target anomaly monitoring point, and the validity of the bioelectrical impedance data of the Rth initial anomaly monitoring point is recorded as 1. Otherwise, the anomaly of the Rth abnormal electrode is a real anomaly, the Rth initial anomaly monitoring point is the target anomaly monitoring point, and the validity of the bioelectrical impedance data of the Rth initial anomaly monitoring point is recorded as 0.

[0088] For the r-th initial anomaly monitoring point in the target sequence of initial anomaly monitoring points, r∈[2,R-1], where ∈ indicates that it belongs to, that is, for any initial anomaly monitoring point from the second-to-last initial anomaly monitoring point to the second-to-last initial anomaly monitoring point in the target sequence of initial anomaly monitoring points, the analysis process is the same, as follows:

[0089] When the r-th initial anomaly monitoring point belongs to the first type of anomaly, then, based on the fact that the r-th initial anomaly monitoring point belongs to the first type of anomaly, the anomaly of the (r+1)-th initial anomaly monitoring point is analyzed. If the (r+1)-th initial anomaly monitoring point belongs to the second type of anomaly, then the true situation of the r-th initial anomaly monitoring point is not abnormal, and the r-th initial anomaly monitoring point is not the target anomaly monitoring point. Correspondingly, the validity of the bioelectrical impedance data of the r-th initial anomaly monitoring point is recorded as 1. Otherwise, the anomaly of the r-th initial anomaly monitoring point is a true anomaly, and the r-th initial anomaly monitoring point is the target anomaly monitoring point. The validity of the bioelectrical impedance data of the r-th initial anomaly monitoring point is recorded as 0.

[0090] When the r-th initial anomaly monitoring point belongs to the second type of anomaly, then based on the fact that the r-th initial anomaly monitoring point belongs to the second type of anomaly, the anomaly of the (r-1)-th initial anomaly monitoring point is analyzed. If the (r-1)-th initial anomaly monitoring point belongs to the first type of anomaly, it means that the actual situation of the r-th initial anomaly monitoring point is not abnormal, and the r-th initial anomaly monitoring point is not the target anomaly monitoring point. Correspondingly, the validity of the bioelectrical impedance data of the r-th initial anomaly monitoring point is recorded as 1. Otherwise, the anomaly of the r-th initial anomaly monitoring point is a real anomaly, and the r-th initial anomaly monitoring point is the target anomaly monitoring point. The validity of the bioelectrical impedance data of the r-th initial anomaly monitoring point is recorded as 0.

[0091] Using the above process, each initial anomaly monitoring point in the target sequence of initial anomaly monitoring points is analyzed sequentially, from the second-to-last initial anomaly monitoring point to the second-to-last initial anomaly monitoring point. For r∈[2,R-1], taking r as R-1 as an example, the R-1th initial anomaly monitoring point is the second-to-last initial anomaly monitoring point, i.e., taking the second-to-last initial anomaly monitoring point as an example.

[0092] When the (R-1)th initial abnormal monitoring point belongs to the first type of abnormality, then, based on the fact that the (R-1)th initial abnormal monitoring point belongs to the first type of abnormality, the abnormality of the (R)th initial abnormal monitoring point is analyzed. If the (R)th initial abnormal monitoring point belongs to the second type of abnormality, then the true situation of the (R-1)th initial abnormal monitoring point is not abnormal, and the (R-1)th initial abnormal monitoring point is not the target abnormal monitoring point. Correspondingly, the validity of the bioelectrical impedance data of the (R-1)th initial abnormal monitoring point is recorded as 1. Otherwise, the abnormality of the (R-1)th initial abnormal monitoring point is a true abnormality, and the (R-1)th initial abnormal monitoring point is the target abnormal monitoring point. The validity of the bioelectrical impedance data of the (R-1)th initial abnormal monitoring point is recorded as 0.

[0093] When the (R-1)th initial abnormal monitoring point belongs to the second type of abnormality, then, based on the fact that the (R-1)th initial abnormal monitoring point belongs to the second type of abnormality, the abnormality of the (R-2)th initial abnormal monitoring point is analyzed. If the (R-2)th initial abnormal monitoring point belongs to the first type of abnormality, it means that the actual situation of the (R-1)th initial abnormal monitoring point is not abnormal, and the (R-1)th initial abnormal monitoring point is not the target abnormal monitoring point. Correspondingly, the validity of the bioelectrical impedance data of the (R-1)th initial abnormal monitoring point is recorded as 1. Otherwise, the abnormality of the (R-1)th initial abnormal monitoring point is a real abnormality, and the (R-1)th initial abnormal monitoring point is the target abnormal monitoring point. The validity of the bioelectrical impedance data of the (R-1)th initial abnormal monitoring point is recorded as 0.

[0094] For the first initial abnormal monitoring point in the target sequence, since there are no more initial abnormal monitoring points preceding it, the following analysis is performed: Since the first initial abnormal monitoring point is located at the very beginning of intestinal peristalsis, if the first initial abnormal monitoring point is abnormal, it can only belong to the first type of abnormality. Therefore, if the first initial abnormal monitoring point belongs to the first type of abnormality, and if the second initial abnormal monitoring point belongs to the second type of abnormality, it indicates that the actual situation of the first initial abnormal monitoring point is not abnormal, and the first initial abnormal monitoring point is not the target abnormal monitoring point. Correspondingly, the validity of the bioelectrical impedance data of the first initial abnormal monitoring point is recorded as 1. Otherwise, the abnormality of the first initial abnormal monitoring point is a true abnormality, and the first initial abnormal monitoring point is the target abnormal monitoring point. The validity of the bioelectrical impedance data of the first initial abnormal monitoring point is recorded as 0.

[0095] Using the above process, each initial anomaly monitoring point is analyzed to determine whether it is a real anomaly. The initial anomaly monitoring points with real anomalies are identified as target anomaly monitoring points. This allows for the selection of target anomaly monitoring points from the initial anomaly monitoring points, ensuring accurate acquisition of anomaly monitoring points and guaranteeing the accuracy of bioelectrical impedance data acquisition.

[0096] In one exemplary embodiment, such as Figure 7 As shown, bioelectrical impedance data analysis methods may also include:

[0097] Step 4: Compare the degree of normality of fluctuations at each target anomaly monitoring point with the preset normal fluctuation range;

[0098] Step 5: Delete the bioelectrical impedance data of the target abnormal monitoring points that are not within the preset normal fluctuation range.

[0099] Since the degree of normality of fluctuation characterizes the normality of bioelectrical impedance data at monitoring points, a normal fluctuation range is preset. The boundary values ​​at both ends of this preset normal fluctuation range and the length of the range are set according to actual judgment needs. Then, the degree of normality of fluctuation at each target abnormal monitoring point is compared with the preset normal fluctuation range. If it falls within the preset normal fluctuation range, the degree of normality of fluctuation is considered normal; if it falls outside the preset normal fluctuation range, the degree of normality of fluctuation is considered abnormal, indicating excessive fluctuation. The bioelectrical impedance data of the target abnormal monitoring points corresponding to the degree of normality of fluctuation outside the preset normal fluctuation range are then deleted. Furthermore, after deleting the bioelectrical impedance data of the target abnormal monitoring points corresponding to the degree of normality of fluctuation outside the preset normal fluctuation range, the bioelectrical impedance data of these target abnormal monitoring points corresponding to the degree of normality of fluctuation outside the preset normal fluctuation range are re-collected.

[0100] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A bioelectrical impedance data analysis method for detecting intestinal inflammation, characterized in that, include: Bioelectrical impedance data were acquired from multiple monitoring points within the intestinal region, and the degree of normality of fluctuations in bioelectrical impedance data at each monitoring point was determined. The degree of food residue accumulation at each monitoring point was also acquired. The correlation between the normality of fluctuations in bioelectrical impedance data at each monitoring point and the degree of food residue accumulation at each monitoring point was determined to obtain initial abnormal monitoring points. Specifically, this included: arranging the monitoring points in ascending order of food residue accumulation to obtain a first monitoring point sequence; arranging the monitoring points in descending order of fluctuation normality to obtain a second monitoring point sequence; and identifying the monitoring points with different position numbers in the first and second monitoring point sequences as initial abnormal monitoring points. Based on two abnormal situations—that the position number of the initial abnormal monitoring point in the first monitoring point sequence is less than or greater than the position number in the second monitoring point sequence—the correlation between the abnormal causes of each initial abnormal monitoring point in the target sequence of the initial abnormal monitoring points and the adjacent initial abnormal monitoring points is determined, and target abnormal monitoring points are obtained from the initial abnormal monitoring points; the target sequence of the initial abnormal monitoring points is obtained by sorting the initial abnormal monitoring points according to the flow order of food in the intestine.

2. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 1, characterized in that, Following the reverse order of the initial anomaly monitoring point target sequence, the correlation between each initial anomaly monitoring point in the initial anomaly monitoring point target sequence and the anomaly cause of adjacent initial anomaly monitoring points is determined sequentially.

3. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 2, characterized in that, If the Rth initial anomaly monitoring point in the target sequence of the initial anomaly monitoring points belongs to the second type of anomaly, then the Rth initial anomaly monitoring point is not a target anomaly monitoring point; otherwise, it is a target anomaly monitoring point. The number of the initial anomaly monitoring points is R. For the r-th initial anomaly monitoring point in the target sequence of the initial anomaly monitoring points, r∈[2,R-1], if the r-th initial anomaly monitoring point belongs to the first type of anomaly, and the (r+1)-th initial anomaly monitoring point belongs to the second type of anomaly, then the r-th initial anomaly monitoring point is not the target anomaly monitoring point; otherwise, it is the target anomaly monitoring point. If the r-th initial anomaly monitoring point belongs to the second type of anomaly, and the (r-1)-th initial anomaly monitoring point belongs to the first type of anomaly, then the r-th initial anomaly monitoring point is not the target anomaly monitoring point; otherwise, it is the target anomaly monitoring point. The first anomaly is when the initial anomaly monitoring point's position number in the first monitoring point sequence is less than its position number in the second monitoring point sequence; the second anomaly is when the initial anomaly monitoring point's position number in the first monitoring point sequence is greater than its position number in the second monitoring point sequence; the first monitoring point sequence is obtained by arranging the monitoring points in ascending order of food residue aggregation degree. The second monitoring point sequence is obtained by arranging the monitoring points in descending order of the degree of normal fluctuation.

4. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 3, characterized in that, If the first initial anomaly monitoring point in the target sequence of the initial anomaly monitoring points belongs to the first type of anomaly, and if the second initial anomaly monitoring point belongs to the second type of anomaly, then the first initial anomaly monitoring point is not a target anomaly monitoring point; otherwise, it is a target anomaly monitoring point.

5. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 1, characterized in that, The process of obtaining the normality of fluctuations in bioelectrical impedance data at each monitoring point includes: Obtain the average amplitude of the bioelectrical impedance data at the first monitoring point, and determine the number of instantaneous amplitude points that are greater than the average amplitude; the first monitoring point can be any monitoring point. The bioelectrical impedance data of the first monitoring point is divided into periods, and the periodic stability of the bioelectrical impedance data of the first monitoring point is obtained based on the similarity between any two periods. The degree of normality of the fluctuation of the bioelectrical impedance data of the first monitoring point is obtained based on the number of instantaneous amplitude points and the periodic stability; the degree of normality of the fluctuation is inversely proportional to the number of instantaneous amplitude points and directly proportional to the periodic stability.

6. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 5, characterized in that, The process of obtaining the periodic stability includes: obtaining the similarity between any two periodic data and calculating the average similarity as the periodic stability.

7. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 5, characterized in that, The calculation process for the normality of the fluctuation includes: Based on the number of instantaneous amplitude points and the total number of amplitude points in the bioelectrical impedance data of the first monitoring point, the percentage of the number of instantaneous amplitude points is obtained; Calculate the difference between the value 1 and the stated quantity percentage; The product of the difference and the periodic stability is calculated to obtain the degree of normality of the fluctuation.

8. The bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 1, characterized in that, The bioelectrical impedance data analysis method also includes: Compare the degree of normality of fluctuations at each target anomaly monitoring point with the preset normal fluctuation range; Delete the bioelectrical impedance data of the target abnormal monitoring points that are not within the preset normal fluctuation range.

9. A bioelectrical impedance data analysis method for detecting intestinal inflammation as described in claim 8, characterized in that, The bioelectrical impedance data analysis method further includes: after deleting the bioelectrical impedance data of the target abnormal monitoring points that are not within the preset normal fluctuation range, re-collecting the bioelectrical impedance data of the target abnormal monitoring points that are not within the preset normal fluctuation range.

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