Feces gas component ratio analysis method for bristol type classification

Abstract

The present invention relates to a feces gas analysis method for classifying Bristol type and analyzing a body's condition and, more specifically, to a feces gas component ratio analysis method capable of classifying the Bristol type of feces according to a component ratio of gas for each feces and analyzing a body's condition. When using a feces gas component ratio analysis method for classifying Bristol type and analyzing a body's condition, according to one embodiment of the present invention, not only the Bristol type of a feces can be classified regardless of measurement time, person, diet, age, and feces weight, but also an eating habit of an individual can be identified, and thus, the analysis method can be applied to personal health monitoring through classification of the Bristol type of feces.

Description

Fecal gas composition ratio analysis method for Bristol type classification

[0001] This application relates to a method for analyzing fecal gas for Bristol type classification and body condition analysis, and more particularly, to a method for analyzing fecal gas component ratios that can classify stool into Bristol types and analyze body conditions based on the component ratios of gas in each stool. This application claims priority to Korean Patent Application No. 10-2023-0073105, filed on June 7, 2023, the entire contents of which are incorporated herein by reference.

[0002] Due to changing eating habits and the various stresses of modern people, digestive disorders are on the rise. To determine the progression and causes of these digestive disorders, it's important to monitor the condition of your daily bowel movements, the result of the digestion of the food you consume.

[0003] It would be nice if individuals could check the condition of their stool directly after defecation, but some people are reluctant to do so, and it is difficult for individuals to consistently collect information such as the time, number of times, and type of odor of defecation.

[0004] Another way to assess stool quality is to use fecal gas. This method uses one or more gas sensors to measure the absolute value of gas concentration directly from the stool, allowing for a more accurate assessment of stool quality. However, the absolute value of gas concentration varies depending on various measurement conditions and circumstances, leading to a limitation in accuracy.

[0005] Examples of technologies developed to analyze fecal gas include an analysis system, a fecal odor gas analysis system, and an exhaled gas analysis system (Japanese Patent Laid-Open No. 2020-187074). However, in-depth development and extensive research into more accurate and effective analysis methods for fecal gas analysis are still needed.

[0006] Meanwhile, the concentration of fecal gas is very sensitive to time, and the amount of gas decreases over time, and it changes sensitively depending on the amount of feces, type of feces, and measurement environment. However, the existing fecal gas analysis method measured feces collected in a toilet in a toilet installed in a laboratory, so it was difficult to accurately analyze with only absolute measurements because the gas concentration was not measured immediately after defecation.

[0007] Accordingly, the inventors of the present invention have made great efforts to develop a fecal gas analysis method that complements the shortcomings of the prior art, and as a result, when the measured values ​​[ppm] of the sensors used for analysis in each fecal gas are converted into a component ratio [%], the change over time is not large and is constant, so that the fecal gas component ratio analysis method that can more accurately classify the Bristol type of feces or determine health status is hardly affected by other environmental factors such as time or fecal weight during fecal gas analysis, has been developed, thereby completing the present invention.

[0008] The present invention relates to a method for analyzing fecal gas for Bristol type classification and body condition analysis, and more specifically, to provide a method for analyzing fecal gas component ratios that can classify the Bristol type of feces and analyze body condition according to the component ratio of gas in each feces.

[0009] However, the problems that the present invention seeks to solve are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0010] The first aspect of the present invention provides a method for analyzing the proportion of fecal gas components for Bristol type classification.

[0011] By using the fecal gas component ratio analysis method for Bristol type classification and body condition analysis according to one embodiment of the present invention, it is possible to classify the Bristol type of feces regardless of measurement time, person, diet, age, and fecal weight, and also to identify an individual's eating habits. Therefore, the above analysis method can be applied to personal health monitoring through Bristol type classification of feces, etc.

[0012] Figure 1 is a diagram showing the Bristol stool scale.

[0013] Figures 2a to 2d are diagrams showing a graph of gas concentration over time output from 11 sensors when measuring gas from one stool (78g of 6Type stool from person C) and a graph of the total gas concentration measured by adding the measurement values ​​of the 11 sensors in real time (Figure 2d).

[0014] Figures 2e to 2g are graphs showing the component ratio over time of 11 sensors when measuring the gas of one stool (78g of 6Type stool of person C).

[0015] Figures 3a to 3c are drawings showing a graph of gas concentration over time output from seven sensors when measuring gas from one stool (22.4 g of Type 4 stool from person A) and a graph of the total gas concentration measured by adding the measurement values ​​of the seven sensors in real time (Figure 3c).

[0016] Figures 3d and 3e are graphs showing the component ratio over time of seven sensors when measuring the gas of one stool (22.7 g of Type 4 stool of person A).

[0017] Figure 4a is a diagram showing a graph of the fecal gas concentration measurement of the H2S sensor [ppm] and a graph of the sum of the sensor set measurement values ​​[ppm].

[0018] Figure 4b is a diagram showing a real-time component ratio graph [%] of the H2S sensor.

[0019] Figure 5a is a diagram showing a graph of the fecal gas concentration measurement of the C2H4 sensor [ppm] and a graph of the sum of the sensor set measurement values ​​[ppm].

[0020] Figure 5b is a diagram showing a real-time component ratio graph [%] of the C2H4 sensor.

[0021] Figures 6a to 6f are diagrams comparing real-time component ratio graphs by stool type (Type 2, Type 4, and Type 6).

[0022] Figures 6g to 6l are drawings comparing real-time component ratio graphs by stool type (Type 2, Type 4, and Type 6) in which the component ratios were reanalyzed excluding NH3.

[0023] Figure 7 is a diagram showing a bar graph of the component ratio of the CH2O sensor (data from person A).

[0024] Figure 8 is a diagram showing a bar graph of the component ratio of the NO2 sensor (data from people A, B, C, and D).

[0025] Figure 9 is a diagram showing a bar graph of the component ratio of the NH3 sensor (data from person A).

[0026] Figure 10 is a diagram showing a bar graph of the component ratio of the H2S sensor (data from person A).

[0027] Below, with reference to the attached drawings, embodiments of the present invention are described in detail to facilitate easy implementation by those skilled in the art. However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, irrelevant parts have been omitted for clarity, and similar reference numerals have been used throughout the specification to indicate similar elements.

[0028] Throughout this specification, when it is said that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0029] Throughout this specification, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated. The terms "about," "substantially," and the like, as used throughout this specification, are used to mean at or near the numerical values ​​indicated when manufacturing and material tolerances inherent to the meanings referred to, and are used to prevent unscrupulous infringers from unfairly exploiting disclosures that contain precise or absolute values ​​to aid understanding of this specification. The terms "step of doing" or "step of" as used throughout this specification do not mean "step for."

[0030] Throughout this specification, the term "combination(s) thereof" included in the expressions in the Makushi format means one or more mixtures or combinations selected from the group consisting of the components described in the expressions in the Makushi format, and means including one or more selected from the group consisting of said components.

[0031] Throughout this specification, references to “A and / or B” mean “A or B, or A and B.”

[0032] Hereinafter, implementation examples and embodiments of the present invention will be described in detail with reference to the attached drawings. However, the present invention may not be limited to these implementation examples and embodiments and drawings.

[0033] The first aspect of the present invention provides a method for analyzing the proportion of fecal gas components for Bristol type classification.

[0034] According to one embodiment of the present invention, the present invention provides a fecal gas component ratio analysis method for Bristol type classification and body condition analysis, comprising the steps of: (a) measuring a gas concentration value (ppm) of feces using a gas sensor; (b) deriving a real-time component ratio graph for each gas using the gas concentration value measured above; (c) obtaining a component ratio value from the real-time component ratio graph derived above; (d) deriving a graph using the component ratio value obtained above; and (e) classifying the Bristol type of feces by comparing the graph derived above with the component ratio graph for each Bristol type of feces.

[0035] According to one implementation example of the present invention, the composition ratio analysis method of the present invention is a method of monitoring human health through stool gas data, and is a method of determining the constitution, health status, or presence of disease of a subject by analyzing the gas measurement value [ppm] of each sensor as a composition ratio in comparison to the total gas concentration measurement graph measured by 11 gas sensors constituting the Sensor Array.

[0036] Fecal gas concentration is very sensitive to time, and the amount of gas decreases over time. It also changes sensitively depending on the amount of stool, type of stool, and measurement environment. Therefore, it is difficult to analyze for health monitoring using only the absolute measurement value [ppm]. However, when the gas measurement graph [ppm] output by each sensor over time was converted into a component ratio graph [%] over time (real-time component ratio graph), as in the component ratio analysis method of our hospital, it was confirmed that the value output tended to be considerably more constant over time compared to the gas concentration measurement value [ppm], regardless of the measurement time after defecation.

[0037] According to one embodiment of the present invention, the gas sensor of the step (a) of the present invention may be at least one selected from the group consisting of NH3, CO, H2, VOC, NO, NO2, H2S, SO2, C2H4, CH2O and ETO gas sensors, but is not limited thereto, and in addition to the above sensor set, any combination of sensors consisting of multiple or more (including partial combinations of 11 sensors or other sensor combinations not mentioned herein) can be effectively applied to monitor and determine the human body condition.

[0038] According to one embodiment of the present invention, the step (b) of the present invention can be performed according to the following [Formula 1] and [Formula 2],

[0039] [Formula 1]

[0040]

[0041] [Formula 2]

[0042]

[0043] Preferably, it can be performed according to [Formula 3] and [Formula 4], but is not limited thereto.

[0044] [Formula 3]

[0045]

[0046] [Formula 4]

[0047]

[0048] According to one implementation example of the present invention, the component ratio value of step (c) of the present invention may be obtained from a single data value in the real-time component ratio graph for each gas derived from step (b), or may be obtained by averaging values ​​within a certain range, but is not limited thereto. The range for obtaining the average value may vary depending on data analysis.

[0049] According to one implementation example of the present invention, the graph of the above step (d) of the present invention refers to all graphs that can be expressed in a pictorial form, such as a bar graph, a line graph, a pie graph, a band graph, etc., and is not limited to a specific graph.

[0050] Hereinafter, the present invention will be described in more detail through examples of the present invention. However, the following examples are provided only to help understanding of the present invention, and the contents of the present invention are not limited to the following examples.

[0051] [Example]

[0052] Example 1. Gas sensor setup and sample placement

[0053] (1) Preparation of gas sensor

[0054] After connecting the power and stabilizing the gas sensor, the gas sensor array was made to measure a person's exhaled breath to confirm whether the gas sensor was connected and functioning normally.

[0055] (2) Sample preparation and storage

[0056] After installing the stool sampler on the toilet, the switch was turned on to begin receiving data from the gas sensor. After 2 minutes, the fan was turned on to allow outside air to be sucked and circulated through the gas sensor array. After 2 minutes, the prepared stool was placed on the stool sampler and the toilet lid was closed to create a sealed environment. A tube was attached to the toilet lid to connect the toilet and the gas sensor array so that the stool gas in the toilet could be sucked into the sensor array. The CO, H2, NH3, VOC, NO, NO2, H2S, SO2, C2H4, CH2O, and ETO sensors were selected for component ratio analysis. The tube was disconnected after measuring the stool gas for approximately 3 to 30 minutes. After 2 minutes, the switch of the gas sensor was turned off to stop data transmission.

[0057] (3) Determining whether there is a response by sensor

[0058] When measuring fecal gas, the response of each sensor was checked, and the measurement values ​​of sensors that were deemed not to have responded were all corrected to 0 ppm over time. This is because when the measurement values ​​were not corrected to 0 ppm, the noise of the sensor was read as the measurement value and affected the analysis of the component ratio. At this time, the standard for judging whether there was a response was that a response occurred if the signal that responded when measuring fecal gas was greater than the maximum signal noise value X 0.8 before measuring the fecal gas for each sensor.

[0059] (Maximum signal noise before fecal gas measurement for each sensor) X 0.8 <(Signal size that responded when fecal gas was measured)

[0060] Example 2. Real-time component ratio graph analysis

[0061] (1) Real-time component ratio graph output

[0062] After determining whether each sensor responded to the fecal gas, a real-time component ratio graph was output as shown below. In the following experiments, the experiment was performed based on the NH3 sensor (i.e., the experiment was performed with NH3 as the ex_sensor).

[0063] When calculating the composition ratio of the NH3 sensor (ex_sensor in Equation 1 below), the real-time composition ratio graph of the NH3 sensor was output as a graph of the sum of the measurement values ​​of all sensors (including NH3) that make up the sensor set as in [Equation 1] below. In order to output the real-time composition ratio of the NH3 sensor (non-responsive sensors are 0 ppm), the calculation was based on the sum of the gas measurement concentration (Total_ppm) that is obtained by adding up all the measurement values ​​of 11 sensors in real time (see Equation 1).

[0064] [Formula 1]

[0065]

[0066] On the other hand, when calculating the component ratio of sensors other than NH3, in order to prevent other sensors from being greatly affected by the NH3 sensor, the component ratio graph of the remaining sensors was output as a "NH3-excluding sensor set measurement total graph [ppm]" by adding up the measurements of other sensors excluding the NH3 sensor. The remaining sensors were calculated based on the no_ex_sensor_Total ppm (in this case, no_NH3_Total ppm), which is the real-time sum of the measurement values ​​of 10 sensors excluding the NH3 sensor (non-responsive sensors are 0 ppm) (see Equation 2).

[0067] [Formula 2]

[0068]

[0069] In the gas concentration measurement graphs of [Figs. 2a] to [Fig. 2d] and [Figs. 3a] to [Fig. 3c], the measured values ​​of the sensors continuously decreased over time throughout the entire period of measuring fecal gas. However, in [Figs. 2e] to [Fig. 2g] and [Fig. 3d], and [Fig. 3e], it was confirmed that the component ratio graphs of each sensor hardly decreased over time from a certain point (about 10 minutes in [Figs. 2e] to [Fig. 2g]) and output a fairly constant value. In addition, it was confirmed that a constant component ratio value was output over time even when the sensor set used for the component ratio analysis in [Figs. 2a] to [Fig. 2d] and [Figs. 2e] to [Fig. 2g] was changed.

[0070] * What it means that the component ratio in the real-time component ratio graph outputs a constant value over time

[0071] "It does not decrease or increase consistently over time until the feces are removed (the feces measurement period)." [Fig. 3d] and [Fig. 3e] In the component ratio graphs, there is a slight fluctuation in the component ratio signal of the NH3 sensor in particular, but since there is no tendency to continuously decrease or continuously increase over time, it can be seen that this is not due to the effect of time. The reason why the sensor outputs a slightly fluctuating signal is thought to be due to the influence of the air flow in the surrounding environment, etc., during the relatively long measurement period of 30 minutes.

[0072] In [Fig. 2e] to [Fig. 2g], the component ratios of some sensors decreased during the first 5 minutes of measurement. Since the decrease did not continue throughout the entire 30-minute fecal gas measurement period in the experiment, but only occurred at the beginning of the measurement when the gas concentration [ppm] changed rapidly, it was determined that this was not due to the effect of time, but rather to the difference in the rate and slope of decrease of the "measurement graph of one sensor [ppm]" compared to the "summary graph of measurement values ​​[ppm]" that adds up all the measurement values ​​of the sensor set. For example, as shown in [Fig. 4a], the "H2S sensor measurement concentration [ppm]" decreased more rapidly than the "summary graph of sensor set measurements [ppm]", and therefore it appears to decrease over time in the beginning in the component ratio graph of [Fig. 4b]. On the other hand, the "C2H4 sensor measured concentration [ppm]" decreased at a rate almost identical to the "sensor set measured value total graph [ppm]", so there was no change in the component ratio value at the beginning of the measurement in the real-time component ratio graph [Fig. 5b].

[0073] As seen in [Fig. 6a] to [Fig. 6f], as the stool type gets closer to Type 6 from Type 2, the component ratios of the sensors, especially the component ratio of the NH3 sensor, change significantly. This confirms that it is possible to understand a person's health status through the component ratio analysis method. In the real-time component ratio graphs of Type 4 and Type 6 stool gas, the section where the sensor signal jumps in the middle is a signal that is generated by repeatedly measuring and not measuring stool gas during the experiment, so the section was not considered when analyzing the component ratio.

[0074] [Table 1]

[0075]

[0076] As shown in [Table 1] above, except for the NH3 sensor, the component ratio relationships of other sensors did not change in all the measured feces, but the NH3 sensor's component ratio relationships with other sensors changed for each feces. In [Figures 6a] to [Figures 6f], the NH3 sensor output a component ratio value exceeding approximately 90% as the feces type approached Type 1 to Type 2, and the value was below 10% as the feces type approached Type 6. Because the NH3 sensor changed significantly depending on the feces type, it was found that the component ratios of other sensors were more affected by the NH3 component ratio than by the feces type. Therefore, in this study, the NH3 sensor was classified as a specific sensor in the analyzed 11 sensor sets, and the component ratios were reanalyzed by excluding the specific sensor (NH3 sensor) from the analysis of the remaining sensor component ratios to analyze the trends and characteristics according to the feces type or the health status of the feces collector. In addition to the above sensor set, any sensor combination consisting of more than one sensor (including partial combinations of 11 sensors or other sensor combinations not mentioned herein) can be analyzed for component ratio by selecting and classifying specific sensors.

[0077] Meanwhile, as seen in [Fig. 6g] to [Fig. 6l], it was difficult to analyze the trends according to each stool type or the characteristics of each stool through a real-time component ratio graph for sensors other than the NH3 sensor. Therefore, the inventors of the present invention quantified the previously analyzed real-time component ratio graph, organized the component ratio values ​​occupied by each sensor constituting the sensor set in one stool gas, and analyzed them by representing them in a bar graph.

[0078] Example 3. Real-time component ratio bar graph analysis

[0079] Real-time component ratio bar graph analysis was performed through the following steps.

[0080] Step 1. Collect stool data

[0081] To analyze the fecal gas composition ratio, data were collected from 18 stool samples collected on different days from three individuals, as shown in Table 2 below. The collected stool samples varied in Bristol type, diet, individual, gender, age, and stool weight.

[0082] [Table 2]

[0083]

[0084] Step 2. For each stool, a real-time component ratio graph is generated, then the component ratio is quantified and converted into a bar graph.

[0085] Step 3. Bar Graph Analysis

[0086] When analyzing the component ratio histogram of 18 stool samples collected from the three stool samplers using 11 sensor sets, the NO2 and NH3 sensors showed a tendency for the component ratio values ​​to gradually decrease as the stool approached diarrheal stool (Type 6-7). In addition, the CH2O sensor confirmed that Type 6-7 and Type 3-4 could be distinguished to some extent when analyzing only the stool data of Person A.

[0087] When only Person A's data was analyzed, it was easier to identify the Bristol type and the subject's health status. The bar graph of the CH2O sensor allowed for the distinction between Bristol Types 3 and 4, and Types 6 and 7 (see Figure 7), and the H2S sensor also provided more detailed information, such as Type 4, which is usually a healthy stool type, but with bright-colored stool and an upset stomach (see Figure 10). In addition, the bar graph of the NH3 sensor also provided information that Type 4 had bright-colored stool and an upset stomach, as well as Type 6 stool that had been consumed after eating spicy food (see Figure 9). In this way, it was found that it was possible to distinguish and identify a person's health status or Bristol stool type through analysis of the component ratios of 11 sensors.

[0088] In conclusion, it was confirmed that the fecal gas component ratio analysis method of this hospital can classify the type of feces or determine a person's health status regardless of the measurement time of fecal gas or the weight of the feces.

[0089] The above description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0090] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

Claims

1. In the analysis of the proportion of fecal gas components for Bristol type classification, (a) A step of measuring the gas concentration value (ppm) of stool using a gas sensor; (b) a step of deriving a real-time component ratio graph for each gas using the gas concentration values ​​measured above; (c) a step of obtaining a component ratio value from the real-time component ratio graph derived above; (d) a step of deriving a graph using the component ratio values ​​obtained above; and (e) A method for classifying Bristol types and analyzing the proportion of fecal gas components for body condition analysis, including a step of classifying the Bristol types of feces by comparing the graph derived above with the graph of proportion of feces by Bristol type.

2. In paragraph 1, A method for analyzing the ratio of fecal gas components for Bristol type classification and body condition analysis, wherein the gas sensor of step (a) is at least one selected from the group consisting of NH3, CO, H2, VOC, NO, NO2, H2S, SO2, C2H4, CH2O and ETO.

3. In paragraph 1, The above step (b) is a method for analyzing the proportion of fecal gas components for Bristol type classification and body condition analysis, which is performed according to the following [Formula 1] and [Formula 2]. [Formula 1] [Formula 2]

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