A characteristic wave identification system for colonic manometry

Abstract

The present invention discloses a characteristic wave recognition system for colon dynamic pressure measurement, wherein a data sampling module samples pressure data of the sigmoid colon, ascending colon, transverse colon and descending colon of a human body through a pressure measuring catheter; a data display module processes and converts the received pressure data into display data including a pressure curve image and a pressure high-resolution cloud map image; and a data analysis module automatically analyzes the stored display data. The present invention discloses a characteristic wave recognition system for colon dynamic pressure measurement, which automatically recognizes colon resting pressure, high-amplitude peristaltic contraction pressure waves, and low-amplitude peristaltic contraction pressure waves from the peristaltic pressures produced by the descending colon, transverse colon, ascending colon and sigmoid colon, and calculates the parameters of each characteristic wave, including the average amplitude, continuous contraction time and dynamic index of pressure, and observes the coordination of colon peristaltic contraction movements.

Description

Technical Field

[0001] The invention belongs to the technical field of colon pressure measurement, and in particular relates to a characteristic wave recognition system for colon dynamic pressure measurement. Background Art

[0002] Colonic manometry is a method that can measure the pressure in the colon cavity and the pressure waves of the colon muscles in real time. This method can be used to qualitatively and quantitatively evaluate colonic motility. This technology has become one of the most direct and important means of evaluating colonic motility.

[0003] Many current colon dynamic pressure measurements are relatively simple and cannot accurately analyze and automatically obtain the parameters of characteristic waves. Therefore, further improvements are made to address the above problems. Summary of the invention

[0004] The main purpose of the present invention is to provide a characteristic wave identification system for colon dynamic pressure measurement, which can be combined with a pressure measuring catheter for accurate pressure measurement, and can pass through the anorectum in turn through the sigmoid colon, ascending colon, transverse colon and descending colon (the total length of the normal adult colon ranges from 130 to 150 cm). The catheter has a built-in pressure sensor, and the peristaltic extrusion of the colon has a built-in pressure sensor on the pressure measuring catheter, and its pressure signal is transmitted to the computer for display. The computer receives the data for effective analysis and processing. Its analysis module automatically identifies the colon resting pressure, high-amplitude peristaltic contraction pressure wave, and low-amplitude peristaltic contraction pressure wave from the peristaltic pressure produced by the descending colon, transverse colon, ascending colon and sigmoid colon, and calculates the parameters of each characteristic wave, including the average amplitude, continuous contraction time and the dynamic index of pressure, and observes the coordination of the peristaltic contraction action of the colon.

[0005] To achieve the above objectives, the present invention provides a characteristic wave identification system for colon dynamic pressure measurement, comprising a data sampling module, a data display module and a data analysis module, wherein:

[0006] The data sampling module samples the pressure data of the sigmoid colon, ascending colon, transverse colon and descending colon of the human body through the pressure measuring catheter to obtain corresponding pressure data and transmit the pressure data to the data display module; the data display module processes the received pressure data and converts it into display data including a pressure curve image and a pressure high-resolution cloud image;

[0007] The data analysis module automatically analyzes the stored display data, thereby obtaining pressure information of the sigmoid colon, ascending colon, transverse colon and descending colon respectively.

[0008] As a further preferred technical solution of the above technical solution, the automatic analysis of the data analysis module is specifically implemented as follows:

[0009] Identify the resting pressure, find the time start of the first resting pressure mark range in the obtained display data, set a virtual analysis frame forward and set the time width to T1, and calculate the integrated mean of the area of ​​the pressure curve of each channel within the T1 time width. The integrated mean of channel Ci is confirmed as the resting pressure Pi of the channel, where i is the channel number, which is numbered from i=1, 2, 3...;

[0010] Identify the effective peak values ​​of each channel: identify the effective peak value from the first channel, and search for the peak value in the Ci channel in sequence, k is the current valid data index number, first locate the index position ki where the defined threshold value P1 is located, and then take the maximum colon pressure value Pmax in the interval from ki to the first time, this pressure value is the effective peak value of the high-amplitude peristaltic characteristic wave, the index number k is the location of the peak value, and determine the starting position of the peristaltic wave to the left, and determine the end position of the peristaltic wave to the right; then locate the index position ki where the defined threshold value P2 is located, and then take the maximum colon pressure value Pmax in the interval from ki to the second time, this pressure value is the effective peak value of the low-amplitude peristaltic characteristic wave, the index number k is the location of the peak value, and determine the starting position of the peristaltic wave to the left, and determine the end position of the peristaltic wave to the right, and record the effective peak value Pmax of each high-amplitude peristaltic characteristic wave and low-amplitude peristaltic characteristic wave;

[0011] Identifying high amplitude peristaltic contraction pressure waves:

[0012] Find the effective peak of the identified high-amplitude peristaltic characteristic wave from the first channel, otherwise, start from the second channel in sequence until the n-6 channels find the channel with the effective peak of the starting high-amplitude peristaltic characteristic wave, recorded as H1; take the H1 channel as the starting channel, search downward for at least the first number of channels and the above adjacent continuous channels to meet the defined threshold P1 in sequence, and determine the start time and end time of each channel. If there is a discontinuous channel i in the middle, then Hi is the starting channel, recorded as H1, and continue to search downward for the first number and above of continuous channels; the continuous contraction time T is the peak time corresponding to the channel Hn minus the peak time corresponding to the channel H1, and the peristaltic speed V of the high-amplitude peristaltic contraction pressure wave HAPCs =(Hn-H1)×△h / T, △h is the distance between adjacent channels, and the continuous contraction time of the high-amplitude peristaltic contraction pressure wave is the time difference ΔT from the peak start time to the peak end time HAPCs =T end -T start ;

[0013] Identifying low amplitude peristaltic contraction pressure waves:

[0014] Find the effective peak of the identified low-amplitude peristaltic characteristic wave from the first channel, otherwise, start from the second channel in sequence until the n-6 channel finds the channel with the effective peak of the starting low-amplitude peristaltic characteristic wave, which is recorded as H1; take the H1 channel as the starting channel, and search downward for at least the second number of channels and the above adjacent continuous channels to successively meet the defined threshold P2, and the pressure wave speed is greater than the preset speed, and determine the start time and end time of each channel; if there is a discontinuous channel i in the middle, then Hi is the starting channel, recorded as H1, and continue to search downward for the second number of consecutive channels or more; the continuous contraction time T is the peak time corresponding to the channel Hn minus the peak time corresponding to the channel H1, and the peristaltic speed V of the low-amplitude peristaltic contraction pressure wave ALPCs =(Hn-H1)×△h / T, △h is the distance between adjacent channels, and the continuous contraction time of the low-amplitude peristaltic contraction pressure wave is the time difference ΔT from the peak start time to the peak end time LAPCs =T end -T start .

[0015] As a further preferred technical solution of the above technical solution, the pressure information analysis of the high-amplitude peristaltic contraction pressure wave and the low-amplitude peristaltic contraction pressure wave is specifically implemented as follows:

[0016] Calculate the frequency. The frequency of high-amplitude peristaltic contraction pressure waves is obtained by counting the identified high-amplitude peristaltic contraction pressure wave markers. The number of high-amplitude peristaltic contraction pressure waves N HAPCs , frequency R HAPCs =N HAPCs ×24h / T HAPCs , T HAPCs is the actual detection time; the low-amplitude peristaltic contraction pressure wave frequency is obtained by counting the identified low-amplitude peristaltic contraction pressure wave markers, and the number of low-amplitude peristaltic contraction pressure waves N LAPCs , frequency R LAPCs =N LAPCs ×24h / T LAPCs , T LAPCs The actual detection time;

[0017] Calculate the average velocity, the peristaltic velocity V of the high-amplitude peristaltic contraction pressure wave HAPCs =(Hn-H1)×△h / T, then the average speed V avg =V HAPCs / n, where n is the number of peristalsis of the high-amplitude peristaltic contraction pressure wave; the peristaltic velocity V of the low-amplitude peristaltic contraction pressure wave LAPCs =(Hn-H1)×△h / T, then the average speed V avg =V LAPCs / n, where n is the number of peristalsis of the low-amplitude peristaltic contraction pressure wave;

[0018] Calculate the average duration of contraction, the average duration of high-amplitude peristaltic pressure waves, by dividing the duration of each high-amplitude peristaltic pressure wave by the number of peristalsis T. HAPCs =T sum / n; the average duration of the low-amplitude peristaltic pressure wave, which is the duration of each low-amplitude peristaltic pressure wave divided by the number of peristalsis T LAPCs =T sum / n;

[0019] Calculate the sustained contraction length, sustained contraction length S = (Hn-H1) × △h, Hn is the nth channel, H1 is the starting channel, △h is the distance between adjacent channels;

[0020] The pressure dynamic index was calculated by multiplying the peak pressure by the duration of contraction P. max ×T peak , T peak is the duration of contraction of a single peak;

[0021] Calculate the average pressure dynamic index and sum the above pressure dynamic indexes: ∑P max ×T peak , and then divided by the total number of effective peak channels n, that is: P avg =∑P max ×T peak / n.

[0022] As a further preferred technical solution of the above technical solution, the pressure curve image is to connect the pressure data sampled by each channel through linear connection to present a pressure curve and observe the data fluctuation through the linear pressure curve;

[0023] The high-resolution pressure cloud image is obtained by linearly interpolating the adjacent channels in each channel data with the smallest pixel as the unit, and then obtaining a set of pressure values ​​for each pixel point through linear interpolation. Each pressure value in the set is then matched with the pre-defined pressure color band mapping set table to look up the RGB color value of the corresponding pressure, and the obtained RGB color value replaces the current pressure value to present the pressure cloud image.

[0024] As a further preferred technical solution of the above technical solution, it also includes a report printing module, and the report printing module is used to print the pressure information analyzed and obtained by the data analysis module. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of a high-amplitude peristaltic contraction pressure wave of a characteristic wave identification system for colon dynamic pressure measurement of the present invention.

[0026] Figure 2It is a schematic diagram of a low-amplitude peristaltic contraction pressure wave of a characteristic wave identification system for colon dynamic pressure measurement of the present invention.

[0027] Figure 3 This is a schematic diagram of the existing human colon structure. DETAILED DESCRIPTION

[0028] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0029] In the preferred embodiment of the present invention, those skilled in the art should note that the pressure measuring catheter, sensor, pressure curve image and pressure high-resolution cloud map image involved in the present invention can be regarded as prior art.

[0030] At present, there are many types of colon manometry catheters, including water perfusion catheters, solid-state catheters, and fiber optic catheters. Different types of catheters have their own advantages and disadvantages. For example, water perfusion catheters are cheap, but the patient's activities are limited, and intubation and perfusion of liquids are required. The operation is complicated and the patient is in great pain. Water perfusion manometry may produce pseudo-pressure due to the stimulation of the manometry tube on the digestive tract, affecting the accuracy of the manometry results. Most existing technologies cannot achieve 24-hour continuous monitoring, and the detection time is relatively short, which may not fully reflect the motility and pressure changes of the colon. Although solid-state and fiber optic catheter manometry methods are more accurate, the high cost of the equipment limits their popularization and application in clinical practice.

[0031] The emergence of the wireless power capsule system has made it possible to conduct long-term non-invasive examinations of colon motility. It does not require intubation and provides greater patient comfort, but the technical maturity and popularity still need to be further improved.

[0032] With the continuous advancement of medical technology and the increasing needs of patients, colon manometry technology is developing in a more efficient, accurate and non-invasive direction. The present invention focuses on the following aspects and makes further improvements to the above problems:

[0033] Non-invasive pressure measurement technology: The development and application of non-invasive detection systems such as solid-state catheter pressure measurement and wireless power capsule will greatly improve patient comfort and acceptance.

[0034] High-resolution manometry technology: By increasing the number and density of sensors, detailed recording and precise analysis of colon movement patterns can be achieved.

[0035] Intelligent data analysis: Utilize advanced algorithms and artificial intelligence technologies to automatically process and analyze pressure measurement data to improve the accuracy and efficiency of diagnosis.

[0036] Preferred embodiments.

[0037] The present invention discloses a characteristic wave identification system for colon dynamic pressure measurement, comprising a data sampling module, a data display module and a data analysis module, wherein:

[0038] The data sampling module samples the pressure data of the sigmoid colon, ascending colon, transverse colon and descending colon of the human body through the pressure measuring catheter to obtain corresponding pressure data and transmit the pressure data to the data display module; the data display module processes the received pressure data and converts it into display data including a pressure curve image and a pressure high-resolution cloud image;

[0039] The data analysis module automatically analyzes the stored display data, thereby obtaining pressure information of the sigmoid colon, ascending colon, transverse colon and descending colon respectively.

[0040] Specifically, the automatic analysis of the data analysis module is specifically implemented as follows:

[0041] Identify the resting pressure. Find the time start of the first resting pressure mark range (one mark corresponds to one box, such as the "resting pressure" mark, and each mark box has a start and end position) in the obtained display data, set a virtual analysis frame forward and set the time width to T1. Within the T1 time width, calculate the integral mean of the area of ​​the pressure curve (obtained from the pressure linear diagram) of each channel (referring to the pressure channel corresponding to the sensor). The integral mean of channel Ci is confirmed as the resting pressure Pi of the channel, where i is the channel number, which is numbered from i=1, 2, 3... in sequence;

[0042] Identify the effective peak value of each channel: identify the effective peak value from the first channel (the first channel is C1, the total number of channels is n, and the number of the i-th channel is Ci), and search for the peak value in the Ci channel in sequence, k is the current valid data index number, first locate the index position ki where the defined threshold P1 (≥75mmHg) is located, and then take the maximum colon pressure value Pmax in the interval from ki to the first time (20s) thereafter. This pressure value is the effective peak value of the high-amplitude peristaltic characteristic wave, and the index number k is the location of the peak value. The starting position of the peristaltic wave is determined to the left, and the ending position of the peristaltic wave is determined to the right (determine the left The index number is k-1, and the index number is k+1 to the right); then locate the index position ki where the threshold value P2 (5mmHg≤P2<75mmHg) is defined, and then take the maximum colon pressure value Pmax in the interval from ki to the second time (20s) thereafter. This pressure value is the effective peak value of the low-amplitude peristaltic characteristic wave, and the index number k is the location of the peak value. The starting position of the peristaltic wave is determined to the left, and the ending position of the peristaltic wave is determined to the right (determined to the left as index number k-1, and determined to the right as index number k+1), and the effective peak value Pmax of each high-amplitude peristaltic characteristic wave and low-amplitude peristaltic characteristic wave is recorded;

[0043] Identifying High Amplitude Peristaltic Pressure Waves (HAPCs):

[0044] Find the effective peak of the identified high-amplitude peristaltic characteristic wave from the first channel, otherwise, start from the second channel in sequence until n-6 channels (the condition is that there are at least 6 or more continuous channels, and if no channels are found, they are defined as invalid peristaltic waves) to find the channel with the effective peak of the starting high-amplitude peristaltic characteristic wave, recorded as H1; take the H1 channel as the starting channel, and search downward (the number of channels is +1 in sequence) for at least the first number of channels (preferably 6) and above adjacent continuous (at least ≥30cm, the shortest distance for clinical applications, and a configurable threshold is set according to needs) channels that meet the defined threshold P1 in sequence, and determine the start time and end time of each channel. If a discontinuous channel i appears in the middle, Hi is the starting channel, recorded as H1, and continue to search downward for the first number of continuous channels or above; the continuous contraction time T is the peak time corresponding to the channel Hn minus the peak time corresponding to the channel H1, and the peristaltic velocity V of the high-amplitude peristaltic contraction pressure wave HAPCs =(Hn-H1)×△h / T, △h is the distance between adjacent channels, and the continuous contraction time of the high-amplitude peristaltic contraction pressure wave is the time difference ΔT from the peak start time to the peak end time HAPCs =T end -T start ;

[0045] Identifying Low Amplitude Peristaltic Pressure Waves (LAPCs):

[0046] Find the effective peak of the identified low-amplitude peristaltic characteristic wave from the first channel, otherwise, start from the second channel in sequence until the channel with the effective peak of the starting low-amplitude peristaltic characteristic wave is found in the n-6 channels, which is recorded as H1; take the H1 channel as the starting channel, and search downward for at least the second number (preferably 6) channels and the above adjacent continuous channels to successively meet the defined threshold value P2, and the pressure wave speed is greater than the preset speed (5mm / s), and determine the start time and end time of each channel; if there is a discontinuous channel i in the middle, then Hi is the starting channel, recorded as H1, and continue to search downward for the second number or more consecutive channels; the continuous contraction time T is the peak time corresponding to the channel Hn minus the peak time corresponding to the channel H1, and the peristaltic speed V of the low-amplitude peristaltic contraction pressure wave LAPCs =(Hn-H1)×△h / T, △h is the distance between adjacent channels, and the continuous contraction time of the low-amplitude peristaltic contraction pressure wave is the time difference ΔT from the peak start time to the peak end time LAPCs =T end -T start .

[0047] More specifically, the pressure information analysis of the high-amplitude peristaltic contraction pressure wave and the low-amplitude peristaltic contraction pressure wave is specifically implemented as follows:

[0048] The frequency (unit: times / d) of high-amplitude peristaltic pressure wave is obtained by counting the identified high-amplitude peristaltic pressure wave (HAPCs) markers. The number of high-amplitude peristaltic pressure waves N HAPCs , frequency R HAPCs =N HAPCs ×24h / T HAPCs , T HAPCs is the actual detection time; the low-amplitude peristaltic pressure wave frequency is obtained by counting the identified low-amplitude peristaltic pressure wave (LAPCs) markers, and the number of low-amplitude peristaltic pressure waves N LAPCs , frequency R LAPCs =N LAPCs ×24h / T LAPCs , T LAPCs The actual detection time;

[0049] Calculate the average velocity (unit: cm / s), the peristaltic velocity V of the high-amplitude peristaltic contraction pressure wave HAPCs =(Hn-H1)×△h / T, then the average speed V avg =V HAPCs / n, where n is the number of peristalsis of the high-amplitude peristaltic contraction pressure wave; the peristaltic velocity V of the low-amplitude peristaltic contraction pressure wave LAPCs =(Hn-H1)×△h / T, then the average speed V avg =V LAPCs / n, where n is the number of peristalsis of the low-amplitude peristaltic contraction pressure wave;

[0050] Calculate the average sustained contraction time (unit: s), the average sustained contraction time of the high-amplitude peristaltic contraction pressure wave, which is the duration of each high-amplitude peristaltic contraction pressure wave divided by the number of peristalsis T HAPCs =T sum / n; the average duration of the low-amplitude peristaltic pressure wave, which is the duration of each low-amplitude peristaltic pressure wave divided by the number of peristalsis T LAPCs =T sum / n;

[0051] Calculate the sustained contraction length (unit: cm), sustained contraction length S = (Hn-H1) × △h, Hn is the nth channel, H1 is the starting channel, △h is the distance between adjacent channels;

[0052] Calculate the pressure dynamic index (unit: mmHg*s), the peak pressure value multiplied by the sustained contraction time P max ×T peak , T peak is the duration of contraction of a single peak;

[0053] Calculate the average pressure dynamic index and sum the above pressure dynamic indexes: ∑P max ×T peak , and then divided by the total number of effective peak channels n, that is: P avg =∑P max ×T peak / n.

[0054] Furthermore, the pressure curve image is obtained by connecting the pressure data sampled from each channel linearly to present a pressure curve and observing data fluctuations through the linear pressure curve;

[0055] The high-resolution pressure cloud image is obtained by linearly interpolating the adjacent channels in each channel data with the smallest pixel as the unit, and then obtaining a set of pressure values ​​for each pixel point through linear interpolation. Each pressure value in the set is then matched with the pre-defined pressure color band mapping set table to look up the RGB color value of the corresponding pressure, and the obtained RGB color value replaces the current pressure value to present the pressure cloud image.

[0056] Furthermore, it also includes a report printing module, which is used to print the pressure information analyzed and obtained by the data analysis module.

[0057] It is worth mentioning that the technical features such as the pressure measuring catheter, sensor, pressure curve image and pressure high-resolution cloud map image involved in the patent application of the present invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional methods in the field, and should not be regarded as the inventive point of the patent of the present invention. The patent of the present invention will not be further elaborated.

[0058] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A characteristic wave identification system for colon dynamic pressure measurement, characterized in that: It includes data sampling module, data display module and data analysis module, among which: The data sampling module samples the pressure data of the sigmoid colon, ascending colon, transverse colon and descending colon of the human body through the pressure measuring catheter to obtain corresponding pressure data and transmit the pressure data to the data display module; the data display module processes the received pressure data and converts it into display data including a pressure curve image and a pressure high-resolution cloud image; The data analysis module automatically analyzes the stored display data, thereby obtaining pressure information of the sigmoid colon, ascending colon, transverse colon and descending colon respectively; The automatic analysis is specifically performed by identifying the resting pressure corresponding to the pressure channel Ci of each sensor, identifying the effective peak value of the pressure curve corresponding to each pressure channel Ci to record the effective peak value of each high-amplitude peristaltic characteristic wave and low-amplitude peristaltic characteristic wave, identifying the high-amplitude peristaltic contraction pressure wave and the low-amplitude peristaltic contraction pressure wave, and also calculating the frequency of the high-amplitude and low-amplitude peristaltic contraction pressure waves, the average peristaltic speed, the average continuous contraction time, the continuous contraction length, the pressure dynamic index and the average pressure dynamic index to obtain the pressure information, where i is the channel number, and the numbers are sequentially obtained from Carry out numbering; Among them, the high-resolution pressure cloud map image is obtained by linearly interpolating the adjacent channels in each channel data with the minimum pixel as the unit, and then obtaining the pressure value set of each pixel point through linear interpolation. Each pressure value in the set is then matched with the pre-defined pressure color band mapping set table to look up the RGB color value of the corresponding pressure, and the obtained RGB color value replaces the current pressure value to present the pressure cloud map.

2. A characteristic wave identification system for colon dynamic pressure measurement according to claim 1, characterized in that: The automatic analysis of the data analysis module is specifically implemented as follows: To identify the resting pressure, find the time start of the first resting pressure mark range in the obtained display data, set a virtual analysis frame forward and set the time width to T1. Within the T1 time width, calculate the integrated mean of the area of ​​the pressure curve of each channel. The integrated mean of channel Ci is confirmed as the resting pressure Pi of the channel, where i is the channel number, and the resting pressure Pi of the channel is determined in sequence. Carry out numbering; Identify the effective peak values ​​of each channel: identify the effective peak value from the first channel, and search for the peak value in the Ci channel in sequence, k is the current valid data index number, first locate the index position ki where the defined threshold value P1 is located, and then take the maximum colon pressure value Pmax in the interval from ki to the first time, this pressure value is the effective peak value of the high-amplitude peristaltic characteristic wave, the index number k is the location of the peak value, and determine the starting position of the peristaltic wave to the left, and determine the end position of the peristaltic wave to the right; then locate the index position ki where the defined threshold value P2 is located, and then take the maximum colon pressure value Pmax in the interval from ki to the second time, this pressure value is the effective peak value of the low-amplitude peristaltic characteristic wave, the index number k is the location of the peak value, and determine the starting position of the peristaltic wave to the left, and determine the end position of the peristaltic wave to the right, and record the effective peak value Pmax of each high-amplitude peristaltic characteristic wave and low-amplitude peristaltic characteristic wave; Identifying high amplitude peristaltic contraction pressure waves: Find the effective peak of the identified high-amplitude peristaltic characteristic wave from the first channel, otherwise, start from the second channel in sequence until the n-6 channels find the channel with the effective peak of the starting high-amplitude peristaltic characteristic wave, recorded as H1; take the H1 channel as the starting channel, search downward for at least the first number of channels and the above adjacent continuous channels to meet the defined threshold P1 in sequence, and determine the start time and end time of each channel. If there is a discontinuous channel i in the middle, then Hi is the starting channel, recorded as H1, and continue to search downward for the first number and above of continuous channels; the continuous contraction time T is the peak time corresponding to the channel Hn minus the peak time corresponding to the channel H1, and the peristaltic speed of the high-amplitude peristaltic contraction pressure wave , is the distance between adjacent channels, and the duration of high-amplitude peristaltic contraction pressure wave is the time difference between the peak start time and the peak end time. ; Identifying low amplitude peristaltic contraction pressure waves: Find the effective peak of the identified low-amplitude peristaltic characteristic wave from the first channel, otherwise, start from the second channel in sequence until the n-6 channel finds the channel with the effective peak of the starting low-amplitude peristaltic characteristic wave, recorded as H1; take the H1 channel as the starting channel, and search downward for at least the second number of channels and the above adjacent continuous channels to successively meet the defined threshold P2, and the pressure wave speed is greater than the preset speed, and determine the start time and end time of each channel; if there is a discontinuous channel i in the middle, then Hi is the starting channel, recorded as H1, and continue to search downward for the second number or more consecutive channels; the continuous contraction time T is the peak time corresponding to the channel Hn minus the peak time corresponding to the channel H1, and the peristaltic speed of the low-amplitude peristaltic contraction pressure wave , is the distance between adjacent channels, and the duration of the low-amplitude peristaltic pressure wave is the time difference between the peak start time and the peak end time. .

3. A characteristic wave identification system for colon dynamic pressure measurement according to claim 2, characterized in that: The specific implementation of the pressure information analysis of the high-amplitude peristaltic contraction pressure wave and the low-amplitude peristaltic contraction pressure wave is as follows: Calculate the frequency. The frequency of high-amplitude peristaltic contraction pressure waves is obtained by counting the identified high-amplitude peristaltic contraction pressure wave markers. ,frequency , The actual detection time; The frequency of low-amplitude peristaltic contraction pressure waves is obtained by counting the identified low-amplitude peristaltic contraction pressure wave markers. ,frequency , The actual detection time; Calculate the average velocity, the peristaltic velocity of the high amplitude peristaltic contraction pressure wave , then the average speed , where n is the number of peristalsis of high-amplitude peristaltic contraction pressure waves; Peristaltic velocity of low amplitude peristaltic pressure wave , then the average speed , where n is the number of peristalsis of the low-amplitude peristaltic contraction pressure wave; Calculate the average duration of contraction, the average duration of high-amplitude peristaltic pressure waves, by dividing the duration of each high-amplitude peristaltic pressure wave by the number of peristalsis ; The average duration of the low-amplitude peristaltic pressure wave is calculated by dividing the duration of each low-amplitude peristaltic pressure wave by the number of peristalsis. ; Calculate the sustained contraction length, sustained contraction length , Hn is the nth channel, H1 is the starting channel, and △h is the distance between adjacent channels; Calculate the pressure dynamic index by multiplying the peak pressure by the duration of contraction , is the continuous contraction time of a single peak; Calculate the average pressure dynamic index and sum the above pressure dynamic indexes: , and then divided by the total number of effective peak channels ,Right now: .

4. A characteristic wave identification system for colon dynamic pressure measurement according to claim 3, characterized in that: The pressure curve image connects the pressure data sampled from each channel linearly to present a pressure curve and observes data fluctuations through the linear pressure curve.

5. A characteristic wave identification system for colon dynamic pressure measurement according to claim 4, characterized in that: It also includes a report printing module, which is used to print the pressure information analyzed and obtained by the data analysis module.

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