Methods, programs and devices for detecting small intestinal bacterial overgrowth

Abstract

Embodiments include a method for detecting small intestinal bacterial overgrowth (SIBO), the method including obtaining data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by a subject, identifying the data corresponding to timing of transit through the small intestine, and determining whether the data indicates the presence of SIBO.

Description

[Technical Field]

[0001] The present invention relates to the field of medicine and healthcare, particularly to digestive and gastrointestinal health. The present invention particularly relates to the detection, diagnosis, and / or measurement of small intestinal bacterial overgrowth (SIBO). [Background technology]

[0002] Accurately diagnosing small intestinal bacterial overgrowth (SIBO) presents a challenge for clinicians and researchers. The accepted gold standard for diagnosing SIBO is small bowel aspirate, which involves using an endoscope to obtain a fluid sample from the proximal end of the small intestine, which is then analyzed for bacterial content. Aspirate tests are prone to inaccuracies, often yielding false negatives due to the inability of the endoscopic tube to reach the distal part of the small intestine, and false positives due to contamination of the sample with bacteria residing in the mouth or esophagus (Ghoshal et al., 2011). The invasive nature of small bowel aspirate tests makes them uncomfortable for patients.

[0003] An alternative diagnostic tool currently in use is breath testing, which measures the percentage of H2 or CH4 in exhaled air. Although breath testing has a low sensitivity of around 40%, it is more commonly used than aspirate testing because it is less invasive (Paterson et al. 2017). Massey et al. (2021) suggest that both diagnostic tools lack specificity for SIBO and instead only distinguish between healthy and unhealthy states.

[0004] Further complicating matters is that SIBO is often a comorbidity of other gastrointestinal disorders, including irritable bowel syndrome (IBS), which can affect diagnostic specificity. A positive outcome of this study is that many papers have investigated the co-occurrence of IBS and SIB. While the prevalence of SIBO in IBS patients may vary significantly across studies, it provides a large dataset linking SIBO to more easily diagnosed gastrointestinal disorders. It is desirable to develop a SIBO diagnostic method that compares favorably with the prevalence rates reported in the literature for IBS patients.

[0005] References: Ghoshal, U.How to Interpret Hydrogen Breath tests.J Neurogastroenterol Motil.2011 July;17(3);Paterson,W.,Camilleri,M.,Simren,M.,Boeckxstaens,G.,Vanner,S.Breath Testing Consensus Guidelines for SIBO: RES IPSA LOCQUITOR.Journal of Gastroenterology.2017 December Massey, B., Wald, A. Small Intestinal Bacterial Overgrowth Syndrome: A Guide for the Appropriate Use of Breath testing. Digestive Diseases and Sciences. 2021;66:338-347. Summary of the Invention

[0006] Embodiments include a method of detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the method including obtaining gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being obtained while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate a metric representative of fluctuations in the gas sensor data as the ingestible capsule device passes through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the metric representative of fluctuations.

[0007] The metric representing the variability can be an aggregate of the cumulative variability between two times.

[0008] The metric representing the variation may be a statistical measure such as variance or standard deviation.

[0009] Variation can be, for example, the irregular or residual component after a trend has been removed, subtracted, or compensated for. For example, a trend can be represented by a trend line (e.g., a first-order polynomial), and variation is the deviation from the trend line. A metric for representing variation is, for example, total or aggregate variation. Note that because total or aggregate is a scalar measure of variation, variations on the positive and negative sides of a reference or trend line accumulate rather than canceling out. An alternative metric includes multiple instances in which gas sensor data are at values ​​exceeding a threshold distance from a reference or trend line, where the distance is the perpendicular distance, i.e., the deviation or magnitude difference. Aggregate variation can be calculated by integrating or summing the area between the trend line and a line interconnecting the time series data points, or by summing or accumulating the distance between each time series data point and the trend line. The trend line can be a first-order polynomial. In summary, variation is the tendency to deviate from the trend line. From a physical perspective, aggregate variation represents heterogeneity in the rate of fermentation gas production and is believed to be caused by distinct clusters or populations of fermentation-causing bacteria in the small intestine.

[0010] The trend line may be a time representation or a displacement representation of the capsule within the small intestine. For example, if the capsule was stationary for a period of time, the trend line (of displacement) may have a flat region during that period.

[0011] The aggregate refers to the sum of the fluctuations over the period of time that the capsule is in the small intestine. The period can be determined on-the-fly, i.e., in near real time, by processing data captured on the capsule and identifying indicator(s) in the data of entry into or exit from the small intestine. Alternatively, the period can be determined retrospectively in post-processing of data captured by the capsule.

[0012] The gas(es) may be either carbon dioxide CO2, hydrogen H2, or both. The gas(es) may further include methane. The gas(es) may include one or more VOCs.

[0013] Alternatively, a method includes a method for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the method comprising: obtaining gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being obtained while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, each reading representing a composition of the gas mixture at a location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate the slope of a best-fit line (a first-order polynomial) fitted to the gas sensor data as the ingestible capsule device passes through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the slope of the best-fit line.

[0014] Breath testing is inaccurate because it is an indirect measurement method, requiring fermentation byproducts to be absorbed into the bloodstream before being excreted from the lungs. It is also suspected that variability in patient transit time can cause the test substrate to reach the large intestine earlier than expected, resulting in a false-positive result. This can be mistakenly interpreted as early fermentation in the small intestine, indicating SIBO. Embodiments provide a direct measurement technology that overcomes the inaccuracies of indirect measurement methods.

[0015] The following optional features can be combined with any of the above methods based on calculating a metric representative of variation and determining the presence or absence of SIBO at least partially dependent on the calculated metric representative of variation, or with any of the above methods based on calculating the slope of a best-fit line and determining the presence or absence of SIBO at least partially dependent on the calculated slope.

[0016] Optionally, the embodiment further includes measuring the level of fermentation activity detected in the subject's small intestine based on the determined metric representative of variation, and including the measured level in the generated report.

[0017] Optionally, the determining includes comparing the metric representative of variation to a predetermined threshold as a first comparison and using a result of the first comparison to determine the presence or absence of SIBO, wherein the metric representative of variation is aggregate variation.

[0018] Optionally, the gas sensor data is obtained by taking readings from an environmental temperature sensor contained within the ingestible capsule device that represents an environmental temperature of the ingestible capsule device, and compensating sample values ​​of the output signal generated by the gas sensor hardware to account for changes in the environmental temperature, and the gas sensor data is the compensated value.

[0019] Changes in environmental temperature within the small intestine, caused by the ingestion of food and beverages, cause changes in the operation of gas sensors, such as TCD gas sensors. Using lookup tables, equations, or other processing techniques, the sampled values ​​of the signal output by the gas sensor hardware can be modified to remove or cancel the changes in value caused by changes in environmental temperature, such that any remaining changes in value are attributable to changes in the capsule fluid composition.

[0020] Based on readings from an environmental temperature sensor contained within the ingestible capsule device and monitoring the environmental temperature at the ingestible capsule device, compensate the gas sensor data for changes in environmental temperature, and determine / measure / calculate the concentration of one or more gases from the compensated gas sensor data.

[0021] Embodiments include apparatus including memory hardware and processor hardware, where the memory hardware stores processing instructions that, when executed by the processor hardware, cause the processor hardware to perform methods of embodiments.

[0022] Optionally, the gas sensor data represents the concentration of a specific gas(es). Furthermore, the gas sensor data may be obtained by processing sample values ​​of the output signal generated by the gas sensor hardware to extract the concentration of the specific gas(es). Furthermore, the specific gas(es) may be one or more of carbon dioxide (CO), hydrogen (H), methane, and one or more VOCs. Furthermore, determining the presence or absence of SIBO in a subject may depend, at least in part, on the concentration of the specific gas(es) exceeding a predetermined threshold concentration at one location or a predetermined threshold number of locations during passage of the ingestible capsule device through the small intestine.

[0023] Optionally, the gas sensor data is a sample of, or is directly proportional to, the output signal generated by the gas sensor hardware.

[0024] Optionally, the method further includes fitting a trend line to the gas sensor data, wherein a determination of the presence or absence of SIBO in the subject depends at least in part on the metric representing the variation and at least in part on the slope of the trend line or the average slope of the trend line.

[0025] Optionally, the determining includes comparing the slope of the trend line to a second predetermined threshold as a second comparison, and combining the results of the first comparison with the results of the second comparison to detect the presence or absence of small intestinal bacterial dysgrowth in the subject.

[0026] Optionally, the determining includes calculating a weighted average or weighted sum of the characteristics including at least the metric representing the variation and the slope of the trend line, comparing the weighted average with a predetermined threshold, and determining the presence or absence of SIBO depending on the result of the comparison. Further, the gas sensor data can represent the concentration of a particular gas(es), and the characteristics further include the number of times or duration that the concentration of the particular gas(es) exceeds a predetermined threshold concentration during passage of the capsule through the small intestine.

[0027] Optionally, the gas sensor hardware includes a TCD gas sensor, and the gas sensor data represents a time series of readings from the TCD gas sensor.

[0028] Optionally, the method further includes detecting a gas sensor data gastroduodenal transition index from the gas sensor data and / or detecting a gas sensor data ileocecal transition index from the gas sensor data, and determining a timing of transit of the ingestible capsule device through the subject's small intestine based on a timing of the detected gas sensor data gastroduodenal transition index and / or the detected gas sensor data ileocecal transition index.

[0029] Optionally, the method further includes acquiring accelerometer data representing a time series of readings from an accelerometer housed within the ingestible capsule device, the time series of readings being acquired while the ingestible capsule device passes through the gastrointestinal tract of the subject; detecting an accelerometer data gastroduodenal index and / or an accelerometer data ileocecal index in the accelerometer data; and determining a timing of the ingestible capsule device passing through the small intestine of the gastrointestinal tract based on the accelerometer data gastroduodenal index and / or the accelerometer data ileocecal index.

[0030] Optionally, the method further includes acquiring reflectometer data representing a time series of readings from a reflectometer housed within the ingestible capsule device, the reflectometer including a transmitting antenna connected in series with a directional coupler configured to measure reflected signals from the transmitting antenna, the time series of readings being acquired while the ingestible capsule device passes through the gastrointestinal tract of the subject, detecting a reflectometer data gastroduodenal index and / or a reflectometer data ileocecal index in the reflectometer data, and determining a timing of the ingestible capsule device passing through the small intestine of the gastrointestinal tract based on the reflectometer data gastroduodenal index and / or the reflectometer data ileocecal index. Further, determining a timing of the ingestible capsule device passing through the small intestine of the subject may include determining a timing of the ingestible capsule device passing through the gastroduodenal junction based on one or more of the gas sensor data gastroduodenal index, the accelerometer data gastroduodenal index, and the reflectometer data gastroduodenal index. Determining when the ingestible capsule device passes through the subject's small intestine may include determining when the ingestible capsule device passes through the ileocecal region based on one or more of a gas sensor data ileocecal index, an accelerometer data ileocecal index, and a reflectometer data ileocecal index.

[0031] Optionally, the method further comprises quantifying the amount of small intestinal bacterial overgrowth in the subject according to the value of the metric representing the variation, quantifying the amount of small intestinal bacterial overgrowth in the subject according to the slope of the trend line, or quantifying the amount of small intestinal bacterial overgrowth in the subject according to the number of times or duration that the concentration of a particular gas(es) exceeds a predetermined threshold concentration during passage of the capsule through the small intestine.

[0032] Optionally, the method further includes generating a report including the detection of the presence or absence of small intestinal bacterial overgrowth in the subject. The method further includes measuring the level of fermentation activity detected in the subject's small intestine based on one or more of the detected fermentation indicator, a metric representing variation, the number or duration of events during passage of the capsule through the small intestine in which the concentration of a specific gas(es) represented by the gas sensor data exceeds a predetermined threshold concentration, and a slope of a trend line fitted to the gas sensor data, and including the measured level in the generated report. The method may further include determining an estimated location(s) of fermentation activity within the small intestine based on the timing of deviations from the trend line contributing to the metric representing variation and / or the timing of events in which the concentration of a specific gas(es) represented by the gas sensor data exceeds a predetermined threshold concentration. The report may further include the measured level of fermentation activity and / or the estimated location(s) of fermentation activity within the small intestine. Optionally, the method is performed by processor and memory hardware within the ingestible capsule device, and the method further comprises wirelessly transmitting the report to a receiving device outside the subject's body, which may be via a Bluetooth transceiver housed in the ingestible capsule device.

[0033] Optionally, the method is performed by a computing device that includes processor hardware and memory hardware and that receives data directly or indirectly from the ingestible capsule device.

[0034] Optionally, portions of this method are performed by processor hardware and memory hardware within the ingestible capsule device, and the method further includes wirelessly transmitting data representing the performed portions of the method from a wireless transceiver housed in the ingestible capsule device to a receiving device outside the subject's body, and receiving and using the transmitted data representing the performed portions of the method at the receiving device or a computing device in data communication therewith to complete the method.

[0035] The result of the first comparison and / or the result of the second comparison can be considered a fermentation indicator. A diagnosis of SIBO can be contingent on detection of either or both of the fermentation indicators in the data obtained from the ingestible capsule device. The method includes, in response to detecting the fermentation indicators, determining the presence of SIBO in the subject and generating a report indicating that the presence of SIBO in the subject has been determined.

[0036] Optionally, the memory hardware and processor hardware are contained within an ingestible capsule device, the ingestible capsule device further comprising a wireless transmitter, and the processor hardware is configured to perform the method while the ingestible capsule device passes through the gastrointestinal tract of the subject, and after diagnosing SIBO in the subject, transmit data indicative of the diagnosis to a receiving device via the wireless transmitter.

[0037] Embodiments include an ingestible capsule device, the ingestible capsule device including an ingestible, non-digestible, biocompatible housing, a power source within the housing, sensor hardware including gas sensor hardware, processor hardware, memory hardware, and a wireless data transceiver, the memory hardware storing process instructions that, when executed by the processor hardware, cause the processor hardware to perform a process, the process including: acquiring gas sensor data representing a time series of readings from the gas sensor hardware contained within the ingestible capsule device ingested by a subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in composition of the gas mixture at a location of the ingestible capsule device within the gastrointestinal tract of the subject; calculating a metric representative of variation in the gas sensor data as the ingestible capsule device passes through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the metric representative of variation.

[0038] Embodiments include computer programs that, when executed by a processor, cause the processor to perform the methods disclosed in this summary or the accompanying description, drawings, and claims.

[0039] Advantageously, embodiments provide a reliable and accurate diagnostic method that relies on data acquired through the ingestion (and excretion) of an ingestible capsule device by a subject. The data acquired by the capsule allows for accurate determination of the presence or absence of SIBO in a patient. The inventors have identified a technique for detecting SIBO by determining the tendency for clumps or localized patches of high and low concentrations of fermentation gas to occur in the small intestine, which is indicative of SIBO. Furthermore, the general first-order polynomial trend line, particularly its slope, provides a further indication.

[0040] In a particular example, the inventors identified a signature in the signal output by a TCD gas sensor housed within an ingestible capsule device that is diagnostic of SIBO.

[0041] Embodiments include a method for detecting small intestinal bacterial overgrowth (SIBO), the method including acquiring data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by a subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the subject's gastrointestinal tract, each reading representing a composition of the gas mixture at a location of the ingestible capsule device within the subject's gastrointestinal tract; detecting an ileocecal transition indicator from the acquired data, and based on the timing of the detected ileocecal transition indicator, detecting a fermentation indicator from the acquired data representing a reading in the time series of readings prior to the timing of the detected ileocecal transition indicator; and, in response to detecting the fermentation indicator, determining the presence of SIBO in the subject and generating a report indicating that the presence of SIBO in the subject has been determined.

[0042] Optionally, the fermentation indicator is detected by processing a time series of readings from the gas sensor hardware to obtain a time series of values ​​representing the concentration of a particular gas(es) in the gas mixture at the location of the ingestible capsule device, and detecting a concentration of the particular gas(es) above a predetermined threshold as the fermentation indicator.

[0043] Embodiments include a method for detecting small intestinal bacterial overgrowth (SIBO), the method including obtaining data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by a subject, identifying the data corresponding to timing of transit through the small intestine, and determining whether the data indicates the presence of SIBO.

[0044] An embodiment includes an ingestible capsule device that includes an ingestible, non-digestible, biocompatible housing, a power source within the housing, sensor hardware including gas sensor hardware, processor hardware, memory hardware, and a wireless data transmitter, the memory hardware storing processing instructions that, when executed by the processor hardware, cause the processor hardware to perform a method of an embodiment.

[0045] An embodiment includes a computer program that, when executed by a processor, causes the processor to perform a process for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the process including: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the subject's gastrointestinal tract, the gas sensor hardware being sensitive to changes in the composition of the gas mixture at the location of the ingestible capsule device within the subject's gastrointestinal tract; calculating a metric representative of fluctuations in the gas sensor data as the ingestible capsule device passes through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the metric representative of fluctuations. The computer program can be stored on a computer-readable medium. For example, the computer-readable medium can be a non-transitory computer-readable medium.

[0046] Embodiments include a method of detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the method comprising: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in the composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate a slope of a best-fit line of a first-order polynomial fitted to the gas sensor data as the ingestible capsule device passes through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the slope of the best-fit line.

[0047] Embodiments include a method for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the method comprising acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the subject's gastrointestinal tract, the gas sensor hardware being sensitive to changes in composition of the gas mixture at a location of the ingestible capsule device within the subject's gastrointestinal tract, the gas sensor data representing concentrations of specific gas(es), the method further comprising determining the presence or absence of SIBO in the subject based at least in part on the concentration of the specific gas(es) exceeding a predetermined threshold concentration at one location or a minimum number of locations during passage of the ingestible capsule device through the small intestine.

[0048] Optionally, the particular gas(es) is one or more of hydrogen, carbon dioxide, and methane.

[0049] Embodiments will now be described, by way of example only, with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0050] [Figure 1A] 1 shows an ingestible capsule device. [Figure 1B] FIG. 1 is a schematic diagram of the components of an ingestible capsule device. [Figure 1C] 1 shows a system including an ingestible capsule device. [Figure 2A] FIG. 1 is a schematic diagram of the components of an ingestible capsule device. [Figure 2B] FIG. 1 is a schematic diagram of the components of an ingestible capsule device. [Figure 3] The sensitivity to the constituent gases changes with operating temperature. [Figure 4A] Here's how. [Figure 4B] Here's how. [Figure 4C] Here's how. [Figure 4D] Here's how. [Figure 4E] Here's how. [Figure 4F] Here's how. [Figure 4G] Here's how. [Figure 4H] Here's how. [Figure 5] 1 shows a plot of data generated by an ingestible capsule device. [Figure 6] 1 shows a plot of data generated by an ingestible capsule device. [Figure 7] 1 shows a plot of data generated by an ingestible capsule device. [Figure 8] 1 shows a plot of data generated by an ingestible capsule device. [Figure 9] Shows a time series of readings from the gas sensor hardware. [Figure 10] Shows a time series of readings from the gas sensor hardware. [Figure 11A] Shows a time series of readings from the gas sensor hardware. [Figure 11B] Shows a time series of readings from the gas sensor hardware. [Figure 12A] Shows a time series of readings from the gas sensor hardware. [Figure 12B] Shows a time series of readings from the gas sensor hardware. [Figure 13A] Receiver operating characteristic curves of test results are shown. [Figure 13B] Receiver operating characteristic curves of test results are shown. [Figure 14A] Shows a time series of readings from the gas sensor hardware. [Figure 14B] Shows a time series of readings from the gas sensor hardware. [Figure 14C]Shows a time series of readings from the gas sensor hardware. [Figure 15A] Shows a time series of readings from the gas sensor hardware. [Figure 15B] Shows a time series of readings from the gas sensor hardware. [Figure 16A] Shows a time series of readings from the gas sensor hardware. [Figure 16B] Shows a time series of readings from the gas sensor hardware. [Figure 17] The results of a comparison between this method and small intestinal aspirate testing are shown below. [Figure 18] The hardware layout of the device is shown. DETAILED DESCRIPTION OF THE INVENTION

[0051] Overview of ingestible capsules 1A, 1B, 2A, and 2B show an ingestible capsule device 10 (also referred to as an ingestible capsule or simply a capsule). A system including the ingestible capsule device 10 is shown in FIG. 1C during the live phase of the ingestible capsule device 10 (i.e., while the ingestible capsule device 10 is taking readings from within the gastrointestinal tract of a subject mammal 40). The capsule housing is an ingestible, non-digestible, biocompatible material.

[0052] 1A, 1B, 2A, and 2B, ingestible capsule device 10 consists of a housing, such as a gas-impermeable shell 11, having an opening covered by a gas-permeable membrane 12. Membrane 111 separates an exposed interior cavity that is exposed to environmental gases entering capsule 10 through membrane 12 from a sealed interior cavity that is not exposed to environmental gases. Note that circuitry transmitting data and / or power can pass through interior membrane 111 without compromising the integrity of the seal.

[0053] 1C, in addition to the capsule, the system further includes a receiving device 30 that receives data transmitted by the capsule from within the gastrointestinal tract of the subject mammal during the live phase. The receiving device 30 can simultaneously or subsequently process the received data and also upload some or all of the received data to a remote processing device 20, such as a cloud-based service, for further processing. The remote computer 20 can be a cloud resource, or a standalone computer located on the premises of a clinician for whom the subject is a patient, or a server (cloud-based or otherwise) of a service provider for whom the clinician is a subscriber / customer / service user.

[0054] Optionally, the system may further include a remote processing device 20, such as a server forming part of a cloud computing environment or other distributed processing environment. The remote processing device 20 is a server provided by or on behalf of a clinical center where the subject 40 is a patient, and is responsible for interpreting the results (i.e., data transmission payload) generated by the capsule 10 and reporting them to the subject 40.

[0055] Data transmission payload is a term that refers to the payload of data transmitted from capsule 10 (i.e., data representing on-board sensor hardware readings, or reports or other results obtained from on-board processing of the readings).

[0056] The connection between the capsule 10 and the receiving device 30 is via a data transceiver 18 on the capsule, which may be part of a wireless transceiver (e.g., a Bluetooth transceiver) operating according to the standard Bluetooth transmission protocol or the Bluetooth long-range transmission protocol. Other operable communication technologies include LoRa, Wi-Fi, and 433 MHz radio.

[0057] The interior of the capsule 10 contains gas sensor hardware 13, which may be a TCD gas sensor 131, a VOC gas sensor 132, or another type of gas sensor sensitive to changes in the concentration of one or more gases associated with fermentation in the small intestine, such as H2 or CO2. The capsule 10 may include a temperature sensor 14a for sensing the temperature of the environment in which the capsule 10 resides. Optionally, the capsule 10 may further include a humidity sensor 14b. The capsule 10 includes processor hardware 151 and memory hardware 152, which may be separate components or both may be provided on the same single chip. The processor hardware 151 and memory hardware 152 may be microcontrollers. The processor hardware 151 may be a microprocessor. The memory hardware 152 is non-volatile memory, and data stored therein is accessible by the processor hardware 151. Processor hardware 151 processes data from signals received from gas sensor hardware 13 and temperature sensor 14a (and optionally reflectometer and accelerometer 19) and stores the processed data in memory hardware 152. The processed data, or portions thereof, are stored on memory hardware 152 as a data transmission payload for transmission by data transmitter 18 to receiving device 30. The processed data may be readings from sensor hardware (a collective term referring to gas sensor hardware 13, temperature sensor 14a, reflectometer, and accelerometer 19 included in capsule 10), the readings themselves (e.g., for a process to determine the presence or absence of SIBO performed on receiving device 30 or remote computing device 20), or a report or other data representing the results of a determination of the presence or absence of SIBO performed on capsule 10.

[0058] The TCD gas sensor 131 may be a low-temperature TCD gas sensor. The sensitivity of the TCD gas sensor may be <1% volume concentration. In the small intestine, the TCD gas sensor 131 senses carbon dioxide CO2 and hydrogen H2.

[0059] As an example, the capsule shown in Figure 2B houses sensor hardware including an environmental sensor 14 in the form of a temperature sensor 14a and / or a humidity sensor 14b, gas sensors in the form of a TCD gas sensor 131 and a VOC gas sensor 132, an accelerometer 19, and a reflectometer. Embodiments may include any or a combination of these individual sensors. Alternatively or additionally, embodiments may include one or more sensors not shown in Figure 2B, such as a spectrophotometer, a surface acoustic wave sensor, and / or a bulk acoustic resonator array.

[0060] The ingestible capsule device 10 may include an environmental sensor 14, which may be an environmental temperature sensor 14a or an environmental temperature sensor 14a and a humidity sensor 14b. The gas sensor hardware 13 may be a TCD gas sensor 131, a VOC gas sensor 132, or a TCD gas sensor 131 and a VOC gas sensor 132, or another type of gas sensor sensitive to changes in the concentration of gases associated with fermentation in the small intestine. As shown, the internal electronics also include a power source 16, such as a silver oxide battery. The internal electronics also include a wireless transceiver 18 including an antenna 17. The internal electronics may also include a reed switch or some other mechanism for activating the ingestible capsule device 10 upon removal from the packaging. Other options for keeping the device powered off (or consuming no power) during storage include a physical switch pressed through a flexible portion of the housing or a field-effect transistor coupled with a photodetector that turns on the microcontroller when exposed to light. or, for example, an NFC transceiver that responds to signals sent from the receiving device 30, triggered by an app configured to manage the storage, processing, and exchange of data between the ingestible capsule device 10 and the receiving device 30. The internal electronics may further include an accelerometer 19 that receives accelerometer data (i.e., signals) in processor hardware 151 for processing and subsequent storage in memory hardware 152 and transmission over wireless transceiver 18.

[0061] Each gas sensor 131, 132 is less than a few millimeters in size and is sensitive to specific gas components, including oxygen, hydrogen, carbon dioxide, and methane. Indeed, the VOC gas sensor 132 can be configured to provide a sensor-side reading and a driver-side or heater-side reading. The heater-side reading can be used to determine the thermal conductivity of the surrounding gas, and thus the heater-side reading of the VOC gas sensor is a TCD reading. The sensor-side reading can be used to determine the concentration of volatile organic compounds in the surrounding gas, and thus the VOC reading. The TCD gas sensor 131 can be, for example, a heating element coupled to a thermopile output, and the thermopile's temperature, and therefore its output, varies with the energy transferred to the gas at the capsule 10. The TCD gas sensor 131 measures the rate of heat diffusion from the heating element.

[0062] As shown in FIG. 3, the heater side of the VOC gas sensor 132 (operating as a TCD sensor) and the sensor side of the TCD gas sensor 131 have different operating ranges, resulting in TCD readings from the two sensors spanning a wider overall operating temperature range than either sensor would read individually. Both sensors have heating elements. The TCD gas sensor 131 has a lower operating temperature but higher accuracy. The heater side of the VOC gas sensor 132 extends the operating range but provides a lower accuracy TCD reading than the TCD sensor. By coordinating the two gas sensors 13, a wider overall thermal range is achieved, improving analyte resolution when signals from the gas sensors are processed for analyte resolution. Because the thermal conductivity of constituent gases in the gas mixture of the gastrointestinal tract varies with temperature, taking TCD readings at different operating temperatures allows different gases to be resolved from one another. This is exploited in the gas resolution processing branch, which determines the identity and concentration of constituent gases in the gas mixture surrounding the capsule 10. The gas resolution process may be performed on the gas capsule 10, on the receiving device 30, or on a remote processing device. The gas resolution process is optional, depending on the implementation. For example, the raw readings of the TCD gas sensor 131 may provide sufficient information to determine the presence of SIBO without processing to resolve changes in the concentration of specific gases.

[0063] The gas sensor hardware 13 is housed in a portion of the capsule 10 that is sealed from the power source 16 and other electronic components by an internal membrane 111. This arrangement minimizes the volume of the sensing headspace (i.e., the sealed portion) and minimizes the risk of leakage caused by a perforated membrane that would allow gastrointestinal gases from the headspace to reach the power source. However, the power source (and other internal electronics) can be configured so that exposure to gastrointestinal gases does not adversely affect performance, thereby eliminating the need for the internal membrane. Thus, the internal membrane 111 is optional, depending on the design, specifically the selection and configuration of the internal electronic components. The internal membrane 111 is permeable to the electronic circuitry necessary to connect the components housed on both sides. For example, wiring can be hermetically passed through the membrane 111. The outer surface of the sealed portion of the capsule is comprised of, or includes a portion comprised of, a selectively permeable membrane. Selective permeability herein means impermeable to liquids but permeable to gases. Selectivity can be extended to allow permeation of only a subset of gases. For example, the gas sensor hardware 13 may include heater(s) that are activated to heat the gas sensor or a sensing portion of each gas sensor to a temperature at which a sensor reading is taken (i.e., a measurement temperature). The heater(s) are pulsed to cause a time-varying change in the temperature of the sensing portion, and the measurement temperature is taken for a period of time sufficient to take a reading without consuming the power required to continuously maintain the measurement temperature.

[0064] The gas sensor 13 may be calibrated so that the gas sensor readings can be used to identify the composition and concentration of the gas to which it is exposed. Calibration coefficients are collected during manufacturing and testing and applied to the recorded readings during processing (i.e., by a server, such as on the cloud, or by the onboard processor 151). This calibration may otherwise be performed on the capsule 10, the receiving device 30, or any device with access to the calibration coefficients and the recorded readings from the gas sensor 13. Such calibration involves a gas resolution processing branch that is responsible for measuring the concentrations of the constituent gases in the gas mixture in the capsule 10. Context for the output of that processing branch is provided by a motility processing branch, which determines (or predicts, within a given confidence level) the location of the capsule 10 in the gastrointestinal tract where the gas mixture will be found. The motility (or location) processing branch may require some adjustment to find the gastroduodenal transit index because different temperatures of ingested food change the environmental temperature in the stomach, which affects the rate of thermal diffusion.

[0065] For example, if a metric representing variation or other quantity, particularly a predetermined threshold(s), is set according to a metric representing variation in raw readings (where raw readings are considered to be values ​​equal to or proportional to the signal value obtained from the output of the sensor), calibration of the readings of the gas sensor hardware to resolve a particular constituent gas(es) is not required.

[0066] For gas sensor data read after ingestion, before the gastroduodenal transition (i.e., while capsule 10 is in the stomach), processing of the readings may include applying an adjustment to the TCD reading from either gas sensor to correct for changes in environmental temperature based on the environmental temperature reading by temperature sensor 14a. Because the TCD reading effectively measures the rate of heat loss to the surroundings, measuring the ambient temperature rather than relying on assumptions (i.e., prior knowledge of the subject mammal's internal temperature) improves accuracy. However, processing may rely on assumptions, for example, if there are any issues with the temperature sensor readings or, for example, if the level of accuracy provided by the assumptions is acceptable for a particular implementation. Gastric temperature may change based, for example, on the ingestion of liquids or food by the subject mammal or physical activity by the subject mammal 40. Environmental temperature is a term used in this document and refers to the temperature of the environment in which capsule 10 is placed, as distinct from the operating temperature of gas sensor 13. The sensitivity of the gas sensor hardware 13 to different constituent gases may vary depending on the operating temperature of the sensor, and processing of the readings includes correcting (which may also be called adjusting or correcting) the readings from the gas sensor depending on the concurrent operating temperature and optionally the concurrent environmental temperature.

[0067] The sensor readings of the process may be divided into different branches for ease of reference and as long as the different branches can be physically executed in different locations, specifically on the capsule 10, on the receiving device 30, or on the remote computing device 20. The SIBO determination branch of the process processes the sensor readings to determine the presence or absence of SIBO. The motility branch of the process processes the sensor readings to determine the timing of motility events (capsule ingestion, capsule gastroduodenal transit, capsule ileocecal transit, and capsule excretion). The gas resolution branch of the process combines readings from different sensors (or from a single sensor at different temperature conditions) to analyze the concentration changes of individual gases through the GI tract and resolve individual gases from the mixture. Note that the processing branches are not independent of each other. For example, a motility indicator (i.e., a feature or characteristic of a sensor output signal used to determine the timing of a motility event) may be found in the gas resolution branch of the process, specifically, in the reading of the concentration of a single analyte gas in the gas mixture in the capsule obtained by processing the output of one or more gas sensors 13. The SIBO determination process, and in particular the time window in which readings are used in the SIBO determination process, can be based on motility events identified in the motility branch of the process.

[0068] In addition to the gas sensor 13 and environmental sensor(s) 14a, 14b, the capsule electronics further include processor hardware 151, memory hardware 152, power supply 16, antenna 17, wireless transmitter 18, and optionally a reed switch or other activation mechanism. The wireless transceiver 18 operates in conjunction with the antenna 17 to transmit readings from the sensors (which collectively refer to the gas sensor 13 and temperature sensor 14a, and optionally also to the accelerometer 19 and reflectometer) to the receiving device 30 for processing either on the receiving device or on a remote processing device with which the receiving device is in data communication; or the processor hardware 151 processes the sensor readings to determine the presence or absence of SIBO and / or identify motility indicators (or extract information from the sensor readings), and the results of that processing are transmitted to the receiving device.

[0069] The wireless transmitter (also referred to as data transmitter 18) may be provided as part of wireless transceiver 18. Wireless transceiver 18 includes antenna 17. Optionally, wireless transceiver 18 also includes directional coupler 171. Wireless transceiver 18 may transmit data according to the Bluetooth protocol, the Bluetooth Long Range (Coded-PHY) protocol, the LoRa protocol, the WiFi protocol, or using another transmission mode such as 433 MHz radio wave transmission.

[0070] 2B shows antenna 17 and directional coupler 171 as elements of wireless transmitter 18, where the antenna is the physical means by which wireless transmitter 18 transmits data to receiving device 30. Wireless transmitter 18 is also configured to buffer data for transmission. Wireless transmitter 18 may also be configured to encode data using a code unique to capsule 10 among a population of similar capsules 10.

[0071] Interconnections between electronic components may be via a central bus connection, dedicated connections between pairs of components, or a combination of both. This is an example of how power and data may be distributed among the components. A microcontroller may be provided to coordinate the distribution of data and power among the components. The sensors (among them the TCD sensor 131, VOC sensor 132, temperature sensor 14a, humidity sensor 14b, accelerometer 19, and directional coupler 171) take readings under the direction of the microcontroller, which is powered by the power supply 16, and transfer the readings (or the results of processing the readings) to the wireless transmitter 18, which transmits them via the antenna 17 to the receiver 30 for off-board processing or to the processor hardware 151 for on-board processing. For example, the processor hardware 151 and memory hardware 152 may collectively be referred to as a microcontroller.

[0072] The capsule has dimensions of less than 11.2 mm in diameter and less than 27.8 mm in length. The housing of the capsule 10 can be made of a biocompatible, non-digestible polymer. The housing is smooth and non-sticky, allowing for passage as quickly as possible and minimizing the risk of the capsule being retained. Optionally, the ingestible capsule has a length of less than 32.3 mm and a diameter of less than 11.6 mm.

[0073] Antenna 17 may be in series with directional coupler 171. Directional coupler 171 and antenna 17 are configured as a reflectometer. The reflectometer uses a diode detector to measure the amplitude of the reflected signal. The reflectometer reading is a reading that represents the electromagnetic properties of materials near the capsule. The reflectometer reading provides the basis for distinguishing between gas, liquid, and solid materials at the capsule's location in the gastrointestinal tract. The reflectometer reading allows antenna 17 and directional coupler 171 to work together as an environmental dielectric sensor.

[0074] The readings of the ingestible capsule 10 include one or more of readings from the temperature sensor 14a, the heater side 132b of the VOC gas sensor 132, the sensor side 132a of the VOC gas sensor 132, and the TCD gas sensor 131, but may also include a reflectometer reading. Thus, as the position of the capsule within the gastrointestinal tract changes, the reflectometer reading also changes, thus providing an indication that a transition event has occurred between two sections of the gastrointestinal tract.

[0075] Ingestible capsule 10 may further include accelerometer 19. Accelerometer 19 may be a three-axis accelerometer. The rate of change of angular position or orientation of capsule 10 depends somewhat on its position within the gastrointestinal tract, and thus accelerometer readings provide an indication that a transition event has occurred between two sections of the gastrointestinal tract. Accelerometer readings may measure angular acceleration about three axes of rotation, which may be mutually orthogonal.

[0076] Processor hardware, memory hardware The processor hardware and memory hardware may be separate components or may be part of the same single integrated chip. The processor hardware and memory hardware are selected according to the specific implementation requirements of each design or version of capsule 10, noting that constraints such as power consumption, cost, data throughput, and data transfer payload size may vary between designs or versions. The processor hardware may be a single processor or multiple interconnected processors.

[0077] Pairing The wireless transceiver may be a Bluetooth transceiver, a Wi-Fi transceiver, a radio transceiver, or another form of wireless data transceiver. The radio transmitter may be configured to transmit in the 433 MHz band. In either case, the wireless data transmitter may be provided as part of the wireless data transceiver. For example, the wireless data transceiver may be capable of receiving signals when pairing or any other form of coupling with at least the receiving device 30. The coupling capsule 10 may be configured to immediately enter a wireless pairing or coupling mode upon power-up (i.e., first power-on), and the subject or another user may be instructed (via written instructions or an application running on the receiving device 30 itself) to pair or couple the capsule 10 to the receiving device 30 before ingesting the capsule 10. However, the capsule 10 may be configured so that pairing or coupling is not required; for example, the capsule 10 may be configured to broadcast data to the receiving device using a data transmission technique that is independent of the pairing or coupling state, as described in more detail below.

[0078] Data transmission technology There are two primary data transmission techniques, and depending on the implementation details (i.e., use case), the ingestible capsule device 10 can be configured to use either or both. In the post-excretion data transmission technique, signals from the sensors are received by processor hardware 151 (also utilizing the storage capabilities of memory hardware 152) and processed onboard capsule 10 for one or more of: determining the presence or absence of SIBO; identifying and recording motility indicators (and optionally, other characteristics of the sensor output, or sensor readings or groups of sensor readings of interest); and resolving individual gas analytes from the sensed gas composition; and the processed results (determination of SIBO, recorded motility indicators, and optionally other characteristics, metrics, and readings or groups of readings of interest, e.g., peak H2, area under a plot of H2 versus time) are stored as a data transmission payload in memory hardware 152. Other characteristics and readings or groups of readings of interest may include, for example, maximum or minimum readings from a particular sensor, or maximum or minimum readings from a metric calculated from a combination of sensors. For example, metrics representing variations used to determine the presence or absence of SIBO may be stored. The slope of a first-order polynomial trend line fitted to the gas sensor data may be stored. The maximum or minimum reading may be a local maximum or minimum reading, where local is defined, for example, by a predetermined timing or motility event determined to have occurred by the capsule 10 itself. A specific example is maximum or minimum H2 concentration, a metric calculated from the gas sensor readings by appropriately tuned processor hardware. Upon detection of capsule 10 excretion from the gastrointestinal tract (e.g., by temperature sensor 14a and / or accelerometer 19 signals), a data transmission payload is transmitted by the wireless transceiver. Metrics further include peak H2 level or value, timing of peak H2, and total H2 (area under the curve).Such metrics may be calculated by the on-board processor hardware 151 during transit through the subject's gastrointestinal tract and may be transmitted from the capsule 10 to a receiving device as part of a report or otherwise in a post-excretion transmission.

[0079] In the post-excretion data transmission technique, transmission may occur via a Bluetooth transmission mode that is independent of pairing status. That is, for example, if a Bluetooth transceiver is paired with a receiving device, it transmits a data transmission payload to the paired receiving device; if the Bluetooth transceiver is not paired, it broadcasts the data transmission payload to unpaired receiving devices in an inquiry mode (also known as a discovery mode or beacon mode). The Bluetooth protocol has an inquiry mode in which a device broadcasts a unique identifier, name, or other information. The data transmission payload, or portions thereof, may include or be included with the above-mentioned other information. Specifically, the data transmission payload may be prioritized or otherwise filtered by the processor hardware 151, such that information deemed particularly important, such as an indication that excretion has occurred (knowing that the capsule 10 has been excreted is important for clinical reasons), or potential information such as the timing of a determined motility event, is transferred from the capsule 10 in priority to other information. Following the inquiry mode transmission, the transceiver may again attempt to pair, connect, or otherwise couple with the receiving device, and if successful, transmit the remainder of the data transmission payload. Of course, this pairing, connecting, or coupling was first performed prior to ingestion, and after excretion, the Bluetooth transceiver attempts to re-pair, re-connect, or re-couple with the receiving device 30. This description uses Bluetooth as an example transmission protocol, but it is noted that the same techniques can be applied to different transmission protocols.

[0080] After broadcasting its unique identifier, name, and other information during Bluetooth inquiry mode, if there is a data transmission payload pending transmission from capsule 10, capsule 10 may be configured to initiate or re-initiate a data communication connection (i.e., pairing or re-pairing) with receiving device 30. If the communication connection is successfully initiated or re-initiated, the data communication connection remains active and the pending data transmission payload is transmitted from capsule 10.

[0081] The Bluetooth transceiver 18, or any other wireless data transmitter 18, may be configured to automatically reconnect after an initial (i.e., pre-ingestion) connection to the receiving device 30. The receiving device 30 runs an app or web app that guides the subject on how to ingest the capsule 10 and notifies the subject that an excretion event has been determined, and optionally that a data transmission payload has been successfully sent to the receiving device 30 so that the capsule 10 can be flushed. Note that the terms pair, connect, and couple are used interchangeably in this document and each refer to establishing a wireless connection between two devices for wireless data transfer.

[0082] It should be noted that the data transmission payload may be transmitted throughout capsule 10's transit through the gastrointestinal tract, depending on its pairing, coupling, or connection with receiving device 30. However, because the safety of capsule 10 depends on capsule 10 being excreted, confirmation that the capsule has determined that an excretion event has occurred is particularly important information. Therefore, information representing the determination of (i.e., a report of) the occurrence of an excretion event may be prioritized and transmitted in broadcast or query mode, with the remaining data transmission payloads being transmitted once a connection is established between wireless data transmitter 18 and receiving device 30. Similarly, for capsule 10 configured to perform an on-board SIBO determination process, the determination of the presence or absence of SIBO may be transmitted in broadcast or query mode.

[0083] In Bluetooth inquiry mode, data can be transmitted to any Bluetooth receiving device within range of the receiving device 30 or capsule 10 without pairing. The wireless transceiver 18 can operate in Bluetooth inquiry mode or Bluetooth long-range (Coded-PHY) mode. The capsule 10 can store and transmit data transmission payload readings from one or more sensors representing a predetermined period, such as a period during small intestinal transit, and optionally from either side of identified motility indicators. For example, gas sensor signals only, or all sensors. Such readings can be used to determine SIBO, increase the reliability of identified motility indicators in determining whether a motility event has occurred, and / or provide other information useful in a health or clinical context.

[0084] More generally, the data transmitted according to the post-excretion data transmission technique may be any data transmission payload that has not yet been transmitted. For example, the wireless data transmitter 18 may be configured to transmit a data transmission payload to a paired receiving device while in the gastrointestinal tract (this transmission is referred to herein as a pre-excretion data transmission technique). However, due to signal attenuation, noise, power supply issues, temporary pairing failure, or if pairing was never performed in the first place, or other reasons, some or all of the data transmission payload may be pending transmission at the time of excretion. In this case, once excretion is detected, the remaining data transmission payload is transmitted according to the post-excretion data transmission technique. Note that downsampling of the data transmission payload may be performed before transmission according to the post-excretion data transmission technique. Furthermore, note that some elements of the data transmission payload may not be transmitted according to the post-excretion data transmission technique. For example, due to limited bandwidth and transmission time, sensor readings may be excluded from the data transmitted according to the post-excretion data transmission technique, while the motility event indicators and diagnostic indicators themselves are included.

[0085] In the pre-excretion data transmission technique, the sensor signals are continuously transmitted by the wireless transceiver 18. In the pre-excretion data transmission technique, the process hardware 151 coordinates the reception of signals from the sensors and their storage in the memory hardware 152 for transmission by the wireless transceiver 18.

[0086] In the example of the Bluetooth wireless transceiver 18, in a pre-excretion transmission technique, the transceiver may be operated according to a long-range or coded PHY Bluetooth transmission procedure, such as BLE Coded PHY, which can achieve a signal power boost of approximately 10 dB.

[0087] During the data transmission phase of the ingestible capsule 10 (i.e., in a short burst after bowel movements in the case of post-excretion data transmission techniques, or continuously while the ingestible capsule 10 is in use, i.e., in the gastrointestinal tract of the subject mammal 40, taking and transmitting readings in the case of pre-excretion data transmission techniques), the wireless transmitter 18 transmits readings to the receiving device 30, which may be a dedicated device (and optionally with a user interface) for receiving and storing readings or may be a multi-function device such as a mobile phone (e.g., a smartphone). The mobile phone may execute an application that processes some or all of the data transmission payload, determines the presence or absence of SIBO in the patient, and / or generates a motility report or a diagnosis of the medical condition based on the motility and diagnostic indices contained in or derived from the data transmission payload. Alternatively, the application may be configured to transmit the data transmission payload to the server 20 or another processing device to determine the presence or absence of SIBO or generate a motility report or diagnosis based on the data transmission payload. The subject mammal need not remain within a specific range of the remote computer 20 during the live phase. Capsule 10 equipped with Bluetooth transceiver 18 can communicate directly with the user's smartphone, eliminating the need for a dedicated receiving device (smartphone serving as receiving device 30). Receiving device 30 (whether a dedicated device, a mobile phone, or a tablet computer) can process the readings itself or upload them to remote computer 20 for processing (i.e., determining the presence or absence of SIBO, identifying motility indicators, determining the timing of motility events, and decomposing gas analytes). Uploading can occur continuously during the capsule's conduct phase or after the capsule's live phase has ended. Receiving device 30 can also store the readings, so that loss of connection between receiving device 30 and the remote processing device is not critical.

[0088] The onboard processor 151 can apply one or more processing or pre-processing steps, as described in more detail below. Digitization of the readings is performed either by the sensor itself, the processor 151, or the wireless transceiver 18. The digitized readings are transmitted via the antenna 17. Readings of the capsule 10 are taken at a moment in time and are associated with the moment they were taken. For example, a timestamp can be associated with the reading by the microcontroller 15, the wireless transmitter 18, or the receiver device 30 or remote computer 20. For example, if the readings are taken by the wireless transmitter 18 and transmitted near-instantaneously (i.e., within a second or a few seconds), the time of receipt by the receiver device can be associated with the reading as a timestamp. The processing of the readings, as described further below, is somewhat dependent on the relative timing of the readings (i.e., so that simultaneous readings from different sensors can be identified as simultaneous), but accuracy on the order of a second, a few seconds, or even tens of seconds is sufficient.

[0089] In hybrid mode, the capsule 10 can combine two data transmission techniques. For example, the capsule 10 can process onboard sensor readings to identify motility markers (and optionally other readings or groups of readings of interest) and transmit them in Bluetooth inquiry mode immediately after bowel movements. Additionally, the capsule 10 can continuously transmit sensor readings to a paired receiving device. Optionally, the continuous transmission can be gas sensor data only, or gas sensor data and environmental sensor data (environmental temperature sensor data and / or relative humidity sensor data) necessary to calibrate the gas sensor signal or otherwise aid in motility event detection. Gas sensor data can be particularly interesting for providing health and clinical information, especially when combined with motility indicators provided by other sensors such as accelerometers and reflectometers. The gas sensor data can be downsampled or subjected to other compression techniques by an onboard processor before transmission. Optionally, the on-board processor hardware 151 may apply one or more filters, such as a high-pass or low-pass filter applied to the value itself or its derivative with respect to time, so that only gas sensor data meeting certain thresholds is included in the data transmission payload. Metrics representative of the gas sensor data, such as the peak of the derived H value or the area under a plot of the derived H value with respect to time, may be maintained and transmitted from the capsule 10.

[0090] For capsules 10 configured to transmit data while passing through the gastrointestinal tract (i.e., pre-excretion data transmission technology), a commercial band (e.g., 433 MHz) may be used by the antenna 17, as electromagnetic waves in this frequency range can safely penetrate mammalian tissue 40. Bluetooth may also be used in such capsules, and may be long-range Bluetooth, particularly if the subject's (human's) BMI exceeds a threshold or if high levels of attenuation are expected for some other reason. Other commercial bands and protocols, such as LoRa, may be used in various applications. Coding may be applied during digitization to ensure that data transmitted by the capsule 10 is distinguishable from data transmitted by other similar capsules 10. The transmitting antenna 17 may be, for example, a pseudo-patch type for transmitting data external to the in-body data collection system.

[0091] The power supply 16 is a battery or supercapacitor capable of powering the sensors and electronic circuitry, including the processor hardware 151 and memory hardware 152. A minimum lifespan of at least 48 hours can be set as a minimum requirement for the GI capsule. The number of silver oxide batteries in the power supply 16 can be configured depending on the desired lifespan of the capsule and other specifications. For example, long-range Bluetooth may consume more power than standard Bluetooth. The capsule 10 can be configured to switch from long-range Bluetooth transmission to standard Bluetooth transmission when the energy stored in the battery(ies) falls below a predetermined threshold, and the on-board processor or microcontroller can be configured to monitor the stored energy level.

[0092] Data Processing Approach On-board sensors generate large amounts of data. Due to limitations such as the energy capacity of the power source, it may be desirable to process some of the data onboard capsule 10 to extract a (relatively small) data transmission payload from the (relatively large) data generated. In addition to extraction, data processing techniques may summarize or otherwise represent the generated data to reduce the size of the data transmission payload. Processor hardware 151 may be configured to prioritize the contents of the data transmission payload. Specifically, data indicating that an evacuation event has been determined and its timing is given the highest priority (i.e., data indicating that an evacuation event is pending transmission is transmitted simultaneously with other content of the pending data transmission payload).

[0093] Of course, there are all sorts of possibilities between, at one extreme, transmitting all generated data from the ingestible capsule device 10 and processing elsewhere (i.e., from the capsule's perspective, a high data transmission burden and a low data processing burden) and, at the other extreme, performing advanced processing on-board to determine outcomes, including the presence or absence of SIBO, timing of motility events, and diagnosing specific health conditions or diseases, and transmitting only the results of that processing (i.e., from the capsule's perspective, a low data transmission burden and a high data processing burden).

[0094] A particular example would be to process the readings of sensors mounted on capsule 10 to determine the timing of the gastroduodenal transition and the ileocecal transition (i.e., to determine the timing of passage through the small intestine), and then include gas sensor data from readings taken in the period between the two determined timings in the data transmission payload (in addition to data from an ambient temperature sensor if necessary for compensation), but exclude gas sensor data for periods outside of that period from the data transmission payload.

[0095] Embodiments can be configured at the design stage depending on implementation requirements, and can combine data processing and data transmission, such that data processing occurs on-board the capsule 10, at the receiving device 30, or at a remote data processing device 20, to determine the presence or absence of SIBO, motility events, and other gut health indicators such as gas concentration at one or more locations / timings within the gastrointestinal tract, and identify or detect diagnostic indicators.

[0096] The term signal can refer to an output signal generated by a sensor, while the term reading can refer to a particular measurement of a signal taken at a particular instant in time or otherwise associated with a particular time, which instant may be explicitly or implicitly included in or associated with the reading (i.e., if a reading is the 1000th in a series of readings, and the readings are taken at a rate of 1 Hz, and the timing of the first reading in the series is known, then the position of the reading in the series implicitly represents the timing). The term data as applied to a sensor refers to the data embodying those readings or signals, although it is noted that the data may be processed, for example, to compensate for the effects of changes in environmental temperature. A timestamp or other timing indicator may be provided by the processor hardware 151. Data represents the reading as a value or as a vector containing multiple components, such as one for the timing, one for the reading, and optionally additional information such as the sensor temperature at the time of the reading.

[0097] On-board processing may be performed in near real time, taking into account delays incurred by transfers between components and the processing itself. Alternatively, readings may be received by the receiving device 30, processed, and stored thereby and / or for uploading and retroactive processing by the remote processing device 20. Dependencies may exist between indices or markers in the data, constraining the order in which readings are processed.

[0098] 4A-4H by executing processing instructions stored in on-board memory hardware 152. While this increases data processing overhead and increases the performance requirements and cost of on-board processor 151 and memory 152, it reduces data transmission overhead, thereby reducing performance requirements for wireless data transmitter 18. On-board processing also reduces stored energy requirements for power supply 16, assuming that processing data on-board consumes less energy than transmitting the data to receiving device 30 for off-board processing.

[0099] In the case of off-board processing, processing may be performed on a receiving device 30 in direct communication with the ingestible capsule device 10, or on a computing device 20 in data communication with the receiving device 30. For example, the receiving device 30 may be a dedicated device configured to receive signals transmitted by the wireless data transmitter 18, e.g., signals transmitted in the 433 MHz radio band. Alternatively, the receiving device 30 may be a general-purpose computing device, such as a smartphone or tablet computer, configured to receive signals transmitted by the wireless data transmitter 18, e.g., signals transmitted according to a Bluetooth transmission protocol or a LoRa transmission protocol.

[0100] Communication between the capsule 10 and the receiving device 30 may occur via a wireless data transmitter 18 on the capsule 10 configured to transmit signals according to the LoRa data transmission protocol.

[0101] Communication between capsule 10 and receiving device 30 may occur via a wireless data transceiver 18 on capsule 10 configured to transmit signals according to the Bluetooth data transmission protocol.

[0102] Communication between capsule 10 and receiving device 30 may occur via a wireless data transceiver 18 on capsule 10 configured to transmit signals according to the Bluetooth Long Range (Coded PHY) transmission protocol.

[0103] In particular, signals transmitted according to the Bluetooth transmission protocol are transmitted according to a post-pairing transmission mode, a term referring to a transmission mode that is independent of the pairing state, by broadcasting data or by initially attempting to transmit data to a coupled / paired device but broadcasting data as a fallback if coupling / pairing fails. Data broadcasting can be performed in a handshake mode, an inquiry mode, or a discovery mode, in which case the data is broadcast by the data transmitter. For example, a generated report such as that shown in step S50 of FIG. 4D can be included in the broadcast data.

[0104] In the transmit-after-excretion mode, the wireless data transmitter first attempts to pair with the receiver and then broadcasts if the pairing attempt fails. The pairing attempt may be a re-pairing attempt with a receiver that was previously paired with the transmitter. Data is transmitted according to the coded PHY Bluetooth transmission protocol or according to the standard Bluetooth transmission protocol.

[0105] In an example of the post-excretion transmit mode, the excretion of the ingestible capsule device 10 from the subject is detected by the on-board ambient temperature sensor 14, whose measurements, signals, or readings are monitored by the on-board processor 151, and upon detection of capsule excretion, the beacon transmit mode of the wireless data transmitter 18 is triggered to transmit a data transmission payload.

[0106] The post-excretion transmit mode can be triggered by determining that an excretion event has occurred (i.e., the capsule has been excreted) based on an onboard temperature sensor reading, specifically a drop in core temperature. If the capsule device 10 has already paired with a receiver 30, such as a smartphone, during an initialization procedure, the capsule device 10 attempts to re-pair and, if successful, can transmit a data transmission payload to the paired receiver 30. If the re-pairing fails, for example, after a finite number of attempts or after a timeout (e.g., 1 second, 3 seconds, 5 seconds), the wireless data transmitter 18 is configured to transmit a data transmission payload in a discovery, inquiry, or handshake mode, which is typically a precursor to pairing and allows for some data transfer. A dedicated application on the receiver 30 is configured to access and process the transferred data transmission payload.

[0107] An evacuation event may also be detected by monitoring the relative humidity sensor readings and detecting a gradual increase in the relative humidity sensor reading associated with rectal evacuation and submersion in the toilet bowl.

[0108] The data transmission payload may include one or more of a diagnostic result (positive / negative), an indicator indicating that SIBO has been determined to be present in the subject, an indicator indicating that SIBO has been determined not to be present in the subject, an indicator indicating that the presence or absence of SIBO in the subject could not be determined, a measured level of fermentation activity measured in the subject's small intestine, and one or more calculated metrics or parameters leading to a diagnosis, detection, determination, or measurement level. In a further example, the data transmission payload may include a representation of a predetermined characteristic feature in a reading generated by a particular gas sensor, such as a TCD gas sensor, whether the representation is the underlying reading from the particular gas sensor or a parameter derived therefrom, such as an indication of the presence or absence of an increased concentration of a particular component in a gas mixture. An example of a predetermined characteristic feature is a metric representing variation, such as aggregate variation, during transit through the small intestine. A further example is the slope of a first-order polynomial trend line fitted to gas sensor data from readings taken during transit through the small intestine.

[0109] The present method for determining the presence of SIBO was developed in specific testing and other data collection exercises in which subjects (some of whom had tested positive for SIBO based on other tests, such as small bowel aspirate tests, and others had tested negative) ingested an ingestible capsule device 10 (containing all gas sensor hardware, among other things, other sensor devices and electronic components) as disclosed in Australian Patent Application No. 2022900873 and its predecessor versions, and analyzed the data generated by the on-board sensors to identify characteristics or features indicative of SIBO.

[0110] Description of the method in Figures 4A-4H 4A-4H illustrate a method for diagnosing SIBO in a patient based on data generated by a sensor mounted on an ingestible capsule device 10 ingested by the patient. Any of the methods of FIGS. 4A-4H may be computer-implemented. Any of the methods of FIGS. 4A-4H may be executed by processor hardware 151 in conjunction with memory hardware 152 mounted on the ingestible capsule device 10. Any of the methods of FIGS. 4A-4H may be executed by a receiving device 30 configured to receive data from the ingestible capsule device 10 representing a time series of readings from the gas sensor hardware housed within the ingestible capsule device 10, or by a remote computing device 20 in data communication with such receiving device 30. Any of the methods of FIGS. 4A-4H may be executed by a combination of one or more of the processor hardware 151 mounted on the ingestible capsule device 10, the receiving device 30, and / or the remote computing device 20.

[0111] 4A-4H show a method for determining the presence of small intestinal bacterial overgrowth (SIBO).

[0112] Step S10: Data acquisition Step S10 involves acquiring data representing a time series of readings from gas sensor hardware contained within the ingestible capsule device 10 ingested by the subject 40, the time series of readings being acquired while the gas sensor hardware is exposed to the gas mixture in the ingestible capsule device 10 as the ingestible capsule device passes through the gastrointestinal tract of the subject 40, with each reading representing the composition of the gas mixture at the location of the ingestible capsule device 10 within the gastrointestinal tract of the subject 40. It is noted that while the data represent gas sensor readings acquired during passage through the small intestine, the data may represent readings from a longer time frame in which a cutoff is applied retroactively after the timing of passage through the small intestine has been determined. The acquisition of data in S10 can be performed by receiving data from the sensor hardware itself or by receiving data from a sampler configured to periodically sample the output signal from the sensor hardware, for example. The acquisition of data in S10 can be performed by receiving data from the ingestible capsule device 10 itself or by reading data from a predetermined storage location. Readings in a time series may be timestamped values, or the time element may be implicit by the chronological arrangement of the readings. Readings may be taken at predetermined intervals, such as every second, every 5 seconds, every 10 seconds, every 15 seconds, every 20 seconds, every 30 seconds, or every minute. The readings form a time series. Each reading may include an explicit time indication, such as a timestamp, or the time may be implicit by its position in the chronological order. For example, after initiation, the nth reading may occur nxm seconds later, where m is the period between successive readings.

[0113] The gas sensor hardware may include, for example, an H gas sensor that is particularly sensitive to changes in the concentration of H in the gas mixture at the location of the ingestible capsule device 10. The gas sensor hardware may include, for example, a CH gas sensor that is particularly sensitive to changes in the concentration of CH in the gas mixture at the location of the ingestible capsule device 10. The gas sensor hardware may include, for example, a CO gas sensor that is particularly sensitive to changes in the concentration of CO in the gas mixture at the location of the ingestible capsule device 10.

[0114] The gas sensor hardware may include, for example, a TCD gas sensor 131 that is sensitive to changes in the thermal conductivity of the gas mixture at the location of the ingestible capsule device 10 and correlates with changes in the concentrations of the different constituent gases. The TCD gas sensor 131 may operate at a single operating temperature or at multiple operating temperatures, such that TCD gas sensor readings may be taken at different operating temperatures and the concentrations of the different composite gases may be derived based on the correlation. The gas sensor hardware may be, or include, for example, a VOC gas sensor 132 that is sensitive to changes in the concentration of volatile organic compounds in the gas mixture at the location of the ingestible capsule device 10.

[0115] The processor that performs this method may or may not be on-board the ingestible capsule device 10, and if not on-board, may be present in a receiving device 30 that is in direct communication with the ingestible capsule device 10, or may be present in a remote device 20 that is in data communication with the receiving device 30.

[0116] Optionally, the ingestible capsule device further includes processor hardware 151, memory hardware 152, and wireless transmitter 18, where processor hardware 151, in cooperation with memory hardware 152, is configured to perform the method of Figures 4A-4H while ingestible capsule device 10 passes through the gastrointestinal tract of subject 40, and further to diagnose SIBO in the subject and transmit data indicative of the diagnosis to a receiving device via the wireless transmitter. The processor hardware and memory hardware may be integrated into a single chip.

[0117] Step S20, calculation of a metric representing the variation In S20, the gas sensor data is used to calculate a metric representing the variation (e.g., aggregate variation) in the concentration of the gas(es). The variation is the difference between the value of a time series data point and the concurrent value of a trend line fitted to the time series data, e.g., a first-order polynomial, and the aggregate variation is the sum of the magnitude of this difference across all relevant time series data points (i.e., all data points belonging to the relevant time period). The calculation of the metric representing the variation is described in detail below with reference to data from live tests.

[0118] The gas sensor data may be a sample of, or directly proportional to, the output signal produced by the gas sensor hardware.

[0119] The gas sensor data may be obtained by processing sample values ​​of the output signal generated by the gas sensor hardware to extract contributions from specific gases, and the gas sensor data is the extracted contribution from the specific gas.

[0120] The gas sensor data may be obtained by taking readings from an environmental temperature sensor contained within the ingestible capsule device that represents the environmental temperature of the ingestible capsule device, and compensating sample values ​​of the output signal generated by the gas sensor hardware to account for changes in the environmental temperature, wherein the gas sensor data is the compensated value.

[0121] A metric representing variability, such as the aggregate variability in S20, may be calculated from gas sensor data representing readings from an individual gas sensor, such as the TCD gas sensor 131. Alternatively, a metric representing variability (e.g., aggregate variability) may be calculated from gas sensor data representing readings from multiple gas sensors, including one or more of a TCD gas sensor, multiple TCD gas sensors with different sensitivity levels, multiple TCD gas sensors with different sensitivity levels at different operating temperatures, a VOC gas sensor, a dedicated H2 gas sensor, and a dedicated CH4 gas sensor.

[0122] S40 in Figure 4A, Determination of the presence of SIBO in a subject In S40, a metric representing variation, such as aggregate variation, is used to determine the presence or absence of SIBO in a subject. For example, the metric representing variation can be compared to a predetermined threshold, as shown in S30 of Figures 4B-4D.

[0123] Alternatively, SIBO can be determined by combining a metric representing variation with previous values ​​of the same metric calculated for the same patient using the same method (note that capsule 10 is disposable, so multiple capsules will be required). The combination can be a simple sum or average of the current result and one or more previous results. Alternatively, a weighted average can be calculated by including weights that decrease over time depending on age (so that more recent results are weighted relatively higher than less recent results).

[0124] Optional step S50 of the method of FIG. 4A is to generate and output a report including a determination of the presence or absence of SIBO from step S40. The output can be transmitted to the receiving device 30 by the ingestible capsule 10, or presented on a GUI on the display device of the receiving device 30 or the remote computing device 20. The output is performed by the receiving device 30 or the remote computing device 20, and includes generating and transmitting a message, such as an email, SMS, or other message format, to convey the determination result in S40 to the clinician and / or patient.

[0125] Step S30, comparing a measurement criterion representing a variation with a threshold In FIGS. 4B to 4D, at S30, the method includes a first comparison to determine whether a measurement criterion representing the variation calculated or measured at S20 meets a predetermined threshold. The predetermined threshold is calculated in the test using data obtained from a live test of a capsule administered to a patient whose SIBO status is known (based on the best available test process), and a threshold for determining the presence of SIBO if exceeded is established, and optionally, a threshold for determining the absence of SIBO if not exceeded is also established (note that the two thresholds may be the same).

[0126] Determination of the relevant period - timing of passing through the small intestine These methods detect a fermentation indicator or some characteristic, parameter, or value among readings obtained while the ingestible capsule device 10 passes through the small intestine. The timing of passage through the small intestine can be determined by reliably determining that the capsule 10 is within the small intestine (i.e., the sensor readings have a value or display a characteristic consistent with being within the small intestine), or by detecting the timing of the gastroduodenal transition and the timing of the ileocecal transition indicator (i.e., entry into or exit from the small intestine is detected and readings between these two events are attributed to being within the small intestine). The timing of the ileocecal transition indicator provides an upper limit for the timing of readings processed to identify the fermentation indicator; a lower limit can be set by a fixed period preceding the ileocecal transition indicator, or the lower limit can be determined by detecting gastric emptying, i.e., gastroduodenal transition of the ingestible capsule device 10 into the small intestine. The timing of the gastroduodenal transition can be the lower limit. Optionally, a buffer or cushion may be applied so that, for example, the relevant period begins a certain period after the detected gastroduodenal transition timing and ends a certain period before the detected ileocecal transition timing. The ileocecal transition index detected in the gas sensor data may be referred to as a gas sensor data ileocecal transition index.

[0127] For example, gastroduodenal transit may be detected by processing readings from a TCD gas sensor. A gastroduodenal transit indicator detected in the gas sensor data may be referred to as a gas sensor data gastroduodenal transit indicator. Detecting a gastroduodenal transit indicator is described in further detail below. Alternatively, readings from sensors such as an accelerometer 19 or a reflectometer 18, or a combination of both, may be used to detect the presence of the ingestible capsule device in the small intestine and thus determine the time period over which readings are processed in step S20. For example, agitation of the ingestible capsule increases in the small intestine relative to the stomach, which is represented in the output signal of the accelerometer 19. Similarly, because the dielectric constant of the stomach is different from that of the small intestine, readings from the reflectometer 18 may be processed to detect the presence of the capsule 10 in the small intestine. A gastroduodenal transit indicator detected in the accelerometer data may be referred to as an accelerometer data gastroduodenal transit indicator. A gastroduodenal transit indicator detected in the reflectometer data may be referred to as a reflectometer data gastroduodenal transit indicator.

[0128] The exit from the small intestine (ileocecal transition) may be detected, for example, by a change in accelerometer and / or reflectometer readings. The detected indices may be combined to determine the timing of the ileocecal transition indicator. An ileocecal transition indicator detected in accelerometer data may be referred to as an accelerometer data ileocecal transition indicator. An ileocecal transition indicator detected in accelerometer data may be referred to as an accelerometer data ileocecal transition indicator.

[0129] Figures 4B-4D, S40, Determining the Presence of SIBO in a Subject In step S40 of Figures 4B-4D, the method includes determining the presence or absence of SIBO in the subject in response to the first comparison. For example, the first comparison alone may be sufficient to determine the presence of SIBO in the subject. Alternatively, as shown in Figure 4C, additional thresholds may need to be met, such as a threshold applied to the slope of the trend line. In either configuration, preprocessing and / or use of published data in the test is performed to determine a deterministic threshold for SIBO in a metric representing variation, and optionally other SIBO indicators, such as the slope of the trend line. Figure 4C may also be referred to as a two-threshold method.

[0130] Figure 4C illustrates how two criteria can be applied to determine the presence of SIBO in a patient: first, S30 determines whether a metric representing variation exceeds a predetermined threshold; and then, S32 determines whether the slope of the trend line fitted to the gas sensor data at S22 exceeds a further predetermined threshold. For example, if both thresholds are exceeded, SIBO is determined to be present. Optionally, meeting one metric but not the other can indicate the possible presence of SIBO, but further testing is required. If both conditions are not met, SIBO may be determined to be absent. The predetermined thresholds are set to diagnose with a confidence level of 90%, 95%, 99%, etc., based on testing and published data. The line from S22 to S20 indicates that the trend line from S22 can be used as a reference line for calculating a metric representing variation, such as aggregate variation at S20.

[0131] Step S22, Calculation of the slope of the trend line In step S22 (e.g., FIGS. 4D, 4E, 4G), a best-fit line is fitted to the gas sensor data from a relevant period. The relevant period is at least a portion of the time that the capsule 10 resides in the subject's small intestine. Optionally, outliers may be removed before the best-fit line is determined. The best-fit line may be constrained to a first-order polynomial. The y-axis intercept is unconstrained. The best-fit line may also be referred to as a trend line. The best-fit line may be determined using a least-squares method.

[0132] Step S32, trend line slope vs. threshold The threshold slope of the trend line can be a gradient in magnitude, regardless of whether the slope is positive or negative. FIG. 11B shows an example of data from a patient whose small intestinal aspirate test was positive for SIBO, with a negative trend line that is steep enough to exceed the threshold. Alternatively, the threshold slope of the trend line can be a negative value, with trend lines having a more negative slope than the threshold being considered to meet or exceed the threshold. Alternatively, the threshold slope of the trend line can be a positive value, with trend lines having a more positive slope than the threshold being considered to meet or exceed the threshold.

[0133] Figure 4E, S40, Determining the presence of SIBO in a subject FIG. 4E illustrates how two characteristics of the gas sensor data are combined in S40 to determine the presence or absence of SIBO in a subject. Specifically, in S40 of FIG. 4E, a weighted average or weighted sum is calculated by combining metrics representing the variability from S20 and the slope of the trend line from S22 with their respective weights. The weights and thresholds for the weighted sum or weighted average that determine the presence of SIBO are determined using data from the live test and the clinical standard aspirate results that indicate the test subject's actual status, whether SIBO positive or negative. The weighted sum or weighted average may also be referred to as a weighted multifactor metric.

[0134] Weighted averages or weighted sums are examples of methods for processing sensor data from the capsule 10 to determine the presence or absence of SIBO. Weighted averages are based on sensor readings generated by the sensor or pseudo-sensor (reflectometer) mounted on the capsule 10 during transit through the small intestine. Weighted averages can be calculated by combining two elements, each with a weight applied: a metric representing variability, such as aggregate variation, and a trend line. Weighted averages can be calculated solely from these two elements and their respective weights. Weighted averages can take into account additional factors characteristic of the data from the sensor or pseudo-sensor mounted on the capsule 10. The weights can be predefined based on data acquired through testing with known clinical standards for SIBO diagnosis, and weights and threshold weighted averages can be configured to distinguish SIBO-positive patients from other patients. The weights themselves can be completely predefined or predefined as a range, and values ​​within that range can be adaptively selected depending on the characteristics of the sensor data, such as noise. Note that weighted sums and weighted averages are interchangeable in this context.

[0135] Step S50, output of decision report 4A, 4D, 4E, 4G, and 4H illustrate that this method may include one or more additional steps of generating a report of the determination of the presence or absence of SIBO and outputting the report. Outputting the report may include displaying or printing the report if the report is generated by a receiving device 30 or a remote computing device 20 having or connected to a display and / or printing device. If the report is generated on-board the capsule 10 itself, the report is transmitted from the capsule 10 to the receiving device 30 and, optionally, to the remote computing device 20 for output. Note that the report generated in S50 may include only the determination result, or may include additional information such as metrics representing the variability calculated in S20 and the slope of the trend line from S22, and may include additional information such as the timing of capsule 10's passage through the small intestine.

[0136] The report may include one or more of the following additional data: readings forming the ileocecal transit index or a representation thereof; readings forming the gastroduodenal transit index or a representation thereof; and readings forming the excretion index; or the determined timing of excretion; or data indicating that excretion of capsule 10 by the subject has been reliably determined.

[0137] The use of dashed lines for steps S22 and S32 in FIG. 4D indicates that these steps are optional; if included, the method of FIG. 4D is the method of FIG. 4C with the additional reporting step S50, and if excluded, the method of FIG. 4D is the method of FIG. 4B with the additional reporting step S50.

[0138] The generated report is output, and the output can be in one or more of a variety of forms. For example, in methods in which the report is generated by the ingestible capsule device 10, the generated report is output via a wireless data transmitter to the receiving device 30 during transit through the remainder of the subject's gastrointestinal tract or upon detection of an excretion. In methods in which the report is generated by the receiving device 30 or a remote processing device 20 in data communication therewith, the output can be transmitted to the clinician and / or patient via a messaging interface, or the output can be a display of the report on a user interface.

[0139] The level of fermentation activity in the small intestine may be included in the report and indicated by a metric that represents variation. For example, the level of fermentation activity in the small intestine may be measured or calculated by the area between a plot of gas sensor data versus time and a linear trend line, or, for example, the area between the plot and the x-axis. Note that, for example, the gas sensor data may be readings from a TCD gas sensor (corrected to account for environmental changes), or the gas sensor data may be values ​​derived from readings such as H2 concentration. Furthermore, gas concentration data such as H2 concentration and CO2 concentration may be measured directly by a dedicated gas sensor or calculated as a derived metric from readings from a sensor sensitive to multiple gases, such as the TCD gas sensor 131. The level of fermentation activity quantifies the fermentation occurring in a time series of readings from the gas sensor hardware determined to have been taken while the capsule 10 was in the small intestine.

[0140] The level of fermentation activity is a measurable physical effect of SIBO, and it is important to note that factors such as diet can affect the level of fermentation activity. By performing any of the methods shown in Figure 4A-H on different occasions and monitoring how the reported level of fermentation activity changes, patients can be monitored over weeks or months to assess the effectiveness of SIBO treatment.

[0141] The generated report may include additional information such as an indication of the location in the small intestine where the fermentation was detected, a determination of timing of intake, a determination of timing of elimination, and other metrics such as peak hydrogen, total hydrogen, etc.

[0142] Method of Figure 4F FIG. 4F shows a further method for determining the presence or absence of SIBO in a subject.

[0143] Step S10 is described above with reference to Figures 4A-4E.

[0144] In S102, an ileocecal transition indicator is detected from the acquired data and is based on the timing of the ileocecal transition indicator, and in S103, a fermentation indicator is detected from the acquired data representing a reading preceding the timing of the detected ileocecal transition indicator. Techniques for detecting the ileocecal transition indicator are described in further detail below. In addition to or instead of being detected in readings from the gas sensor hardware, and / or the ileocecal transition indicator may be detected in reflectometer data as the reflectometer data ileocecal transition indicator.

[0145] Detecting the fermentation indicator in S103 may include one or both of using the gas sensor data to calculate a metric representative of the variation in concentration of the gas(es) during passage through the small intestine, as described above with reference to step S20, and fitting a trend line to the gas sensor data over passage through the small intestine, as described above with reference to step S30.

[0146] In S104, it is determined whether the fermentation indicator is diagnostic of SIBO, which may include determining whether either or both of the metric representing variation and the slope of the trend line exceed respective thresholds, as described above with reference to steps S30 and S32.

[0147] In S104, comparison with the threshold(s) allows for a positive or negative diagnosis of SIBO, i.e., it can be determined whether the detected fermentation indicator is a diagnostic indicator of SIBO.

[0148] Figure 4G, S40, Determining the presence of SIBO in a subject In the method of Figure 4G, the presence or absence of SIBO is determined from the slope of the trend line. Figure 4G differs from Figures 4A-4E in that it does not include a step of calculating a metric representing variation, although it is noted that such a step is optional in the context of Figure 4G.

[0149] Steps S10 and S22 of FIG. 4G are as described above.

[0150] In S40, the slope of the best-fit line is used to determine the presence or absence of the target SIBO. For example, as shown in S32 of FIGS. 4C and 4D, the slope of the best-fit line can be compared with a predetermined threshold value.

[0151] Alternatively, SIBO can be determined by combining the slope of the best-fit line with the slope of the best-fit line calculated for the same patient using the same method (note that since the capsule 10 is disposable, multiple capsules are required). The above combination can be a simple sum or average of the current result and one or more past results. Alternatively, a weighted average can be calculated by including weights that decrease over time according to age (so that more recent results are relatively more highly weighted than less recent results).

[0152] An optional step S50 of the method of FIG. 4G is to generate and output a report including the determination of the presence or absence of SIBO from step S40. The output can be transmitted to the receiving device 30 by the ingestible capsule 10, or can be presented on a GUI on the display device of the receiving device 30 or the remote computing device 20. The output includes generating and transmitting a message, such as an email, SMS, or other message format, by the receiving device 30 or the remote computing device 20 to convey the determination result in S40 to the clinician and / or patient.

[0153] FIG. 4H In the method of FIG. 4H, the presence or absence is determined based on whether the concentration of a specific gas exceeds a predetermined threshold concentration for that specific gas (singular or plural).

[0154] In FIG. 4H, the gas sensor data acquired in S10 is a specific form of gas sensor data. Specifically, the gas sensor data represents the concentration of a specific gas or a specific combination of gases. "Specific" in this context means known; thus, for example, the specific gas could be hydrogen (H), carbon dioxide (CO), or methane (CH) (or a combination of these two gases). This is in contrast to the case where the gas sensor data represents the physical response of the gas sensor hardware to a gas mixture, which represents a change in the composition of the entire gas mixture but may not resolve the specificity of a single gas or gas combination. The gas sensor hardware may include a gas sensor configured to sense only a specific gas(es) and not other gases that may be present in the small intestine. Alternatively, the gas sensor hardware may include a gas sensor sensitive to more gases than the specific gas(es), and some processing is applied to extract a value representing only the concentration of the specific gas(es) for which a predetermined threshold is applied in S24.

[0155] At S24, a predetermined threshold is applied to the gas sensor data acquired at S10 that specifically represents the concentration of a particular gas(es) at the capsule location during transit through the small intestine. The method of Figure 4H can be combined with processing and data transmission techniques disclosed elsewhere herein, particularly techniques related to identifying the timing of small intestinal transit from among a broader range of readings generated by the ingestible capsule device 10.

[0156] In particular, the specific gas(es) are gas(es) produced by fermentation in the small intestine, such as one or more of carbon dioxide CO2, hydrogen H2, and methane CH4. Optionally, determining the presence of SIBO may require a minimum number of distinct sites or locations, such as two or three, of the specific gas(es) exceeding a predetermined threshold concentration of the specific gas(es). Alternatively, for example, if the measured concentration of the specific gas(es) exceeds a predetermined threshold concentration a minimum number of times or for a minimum period of time, it is determined that SIBO is present in the subject in S40. If the criteria for determining the presence of SIBO in S24 are not met, it is determined that SIBO is not present in S41. If the criteria are met, the presence of SIBO is determined in S40. Optionally, the concentration threshold criteria applied in S24 are not the determining factor in determining whether SIBO is present, and the results of S24 can be combined with the results of one or more other processing techniques, such as comparing the slope of a trend line (e.g., S22, S32 in FIG. 4D) to a slope threshold, and comparing a metric representing variation to a variation metric threshold (e.g., S20, S30, FIGS. 4B, 4C, 4D).

[0157] Optionally, a report is generated and output at S50. For example, the report contains at least the determination of the presence or absence of SIBO. The report may further include data on which the determination is based, such as concentration measurements of a particular gas(es) that exceed a threshold. The report may further include the timing of those measurements. The report may further include an estimate of the location within the small intestine of the site(s) of fermentation activity based on the timing of those measurements relative to the overall timing of transit through the small intestine. For example, such an estimate may be based on the assumption that the displacement per unit time of ingestible capsule device 10 transiting through the small intestine is uniform.

[0158] The threshold is determined through experimentation. The predetermined threshold concentration is a fraction or ratio of the specific gas(es) in the overall gas mixture and is independent of the gas sensing mode, but is dependent on the gas sensor hardware being properly calibrated and / or accurately determining gas sensor data that represents the absolute concentration of the specific gas(es). This is in contrast to other techniques, such as metrics that represent the variability of gas sensor data, which can be based on gas sensor data that represents the absolute concentration of the specific gas(es), or alternatively, can be based on readings from a gas sensor (which may be referred to as raw sensor data) that are sensitive to changes in the composition of the gas mixture but do not necessarily directly represent the concentration of the specific gas(es).

[0159] Machine Learning A machine learning algorithm can be trained to determine whether gas sensor data from the small intestine indicates the presence of SIBO.

[0160] Steps S102-S104, or any combination of steps S10-S40, can be performed by a properly trained AI classification algorithm. The underlying algorithm can be a convolutional neural network. The training data is in the form of gas sensor data from SIBO-positive and SIBO-negative cases in PA tests or other supplemental tests with positive or negative ground truths. The convolutional neural network learns to identify visual differences in the gas sensor data between positive and negative cases and predicts whether the test case is SIBO-positive or SIBO-negative. Using a similar approach, a neural network can be simply trained to provide an input vector containing two elements: a metric representing the variability value and a trend line slope value. The classification algorithm is then trained to use these two-element input vectors to classify SIBO-positive and SIBO-negative cases.

[0161] Ileocecal transition index detection The method includes detecting an ileocecal transit indicator (referring to transit through the ileocecal region by the ingestible capsule device 10), which may be detected according to several techniques. For example, ileocecal transit timing may be used to determine the completion time of transit of the capsule 10 through the small intestine, and may also be useful information for a clinician in assessing the health of a patient's gastrointestinal tract.

[0162] The H2 level reading can be used as a reference for detecting the ileocecal transition indicator in S20. The H2 level can be detected directly by an H2 gas sensor, which is particularly sensitive to changes in H2 concentration. Alternatively, the ileocecal transition indicator can be detected by simultaneously (or temporally adjacently within a predetermined time distance on either side of) identifying an increase in the concentration of volatile organic compounds indicated by the VOC gas sensor output exceeding a predetermined threshold (either the increase exceeds the predetermined threshold or the level itself exceeds the predetermined threshold) and an increase in the H2 level exceeding a predetermined threshold (either the increase exceeds the predetermined threshold or the level itself exceeds the predetermined threshold). Note that the H2 level is determined from the output of the TCD gas sensor and / or the output of the heater-side VOC sensor. The H2 level is determined from the TCD gas sensor output by taking predetermined calibration data correlating TCD readings at different operating temperature setpoints of the TCD gas sensor with changes in thermal conductivity and concentrations of different constituent gases at different operating temperature setpoints.

[0163] Similarly, CH4 concentration readings can be used as the basis for the ileocecal transition index. CH4 concentrations can be detected directly by a CH4 gas sensor, which is particularly sensitive to changes in CH4 concentration. Alternatively, the ileocecal transition index can be detected by simultaneously (or temporally adjacently within a predetermined time distance on either side of) identifying an increase in the concentration of a volatile organic compound indicated by the VOC gas sensor output exceeding a predetermined threshold (either the increase exceeds the predetermined threshold or the level itself exceeds the predetermined threshold) and an increase in CH4 levels exceeding a predetermined threshold (either the increase exceeds the predetermined threshold or the level itself exceeds the predetermined threshold). Note that CH4 levels can be determined from the output of a TCD gas sensor and / or the output of a heater-side VOC sensor.

[0164] The ileocecal transition indicator may be detected as an increase in VOC concentration in the gas mixture in capsule 10, as indicated by a time series of readings from VOC gas sensor 132. Because increases in VOC concentration in the gas mixture in capsule 10 may be caused by fermentation in the small intestine, it may be necessary to distinguish one increase from another. Such a distinction is made by identifying a change in slope, with the reading preceding the change in slope being the detected ileocecal transition indicator.

[0165] FIG. 5 illustrates one technique for detecting ileocecal transition. In particular, FIG. 5 illustrates the change in VOC gas sensor reading with slope and magnitude, and trials establish minimum thresholds for the slope and magnitude of the change in VOC gas sensor reading associated with transition across the ileocecal region.

[0166] Detection of gastroduodenal transit timing Gastric emptying, gastroduodenal transition, or crossing of the stomach-duodenal interface can be detected to establish a lower limit for the timing of capsule 10 transit through the small intestine. Such a process is optional, and the lower limit can be set by a predetermined fixed period relative to the detected ileocecal transition timing, or by detecting the presence of capsule 10 in the small intestine (i.e., not necessarily detecting transition into the small intestine itself). The gastroduodenal indicator can be detected in a first subset of recorded readings, the first subset being temporally defined by beginning after an ingestion event. The ingestion event can be determined by readings from temperature sensor 14a (and / or relative humidity sensor 14b) or by user interaction with an interface on receiving device 30 or remote computer 20. Additionally, the first subset can be constrained by sensors, including readings from TCD gas sensor 131. The first subset can further include readings from a reflectometer (i.e., antenna 17 and directional coupler 171) and / or accelerometer 19.

[0167] The gastroduodenal transition indicator in the TCD gas sensor reading may be a spike, a step change, or an inflection point in the TCD gas sensor reading. Based on the reading recorded from the environmental temperature sensor 14a, a correction may be applied to the TCD gas sensor reading to account for changes in environmental temperature. The gas sensor data may be the value of the output signal from the TCD gas sensor, or a temperature corrected version thereof.

[0168] The primary physical mechanism sensed in the TCD gas sensor reading when detecting the gastroduodenal transition index is as follows: Hydrochloric acid in gastric juices leaving the stomach mixes with bicarbonate in bile acids released by the pancreas. These bile acids act to neutralize the pH of the fluid, and a by-product of this reaction is CO2. In this region of the gastrointestinal tract, the ambient gases are primarily N2 and O2, with trace amounts of CO2. The amount of CO2 produced in this reaction is significantly greater than the trace amount present in the ambient air due to swallowing of exhaled breath. Therefore, it is appropriate to simply use the TCD sensor output without calculating CO2. In other words, the TCD gas sensor reading, when corrected for changes in environmental temperature, provides the gastroduodenal transition index due to changes in thermal conductivity caused by changes in CO2 concentration on both sides of the gastroduodenal junction. For exercise purposes (i.e., to determine the position of the ingestible capsule 10), there is no particular need to calculate the actual CO2 concentration.

[0169] It should be noted that the gas sensor data used to calculate a metric representing variation, such as aggregate variation, in S20 and the gas sensor data used to detect the gastroduodenal transit index may be the same or different.

[0170] Because the TCD sensor 131 is affected by the temperature of the gas mixture at the location of the capsule, a temperature correction process is necessary to account for temperature changes in the external environment (i.e., drinking cold water, exercising, eating, etc.). Starting from the timing of a determined ingestion event, a bump, step change, or large inflection in the TCD gas sensor 131 reading plotted versus time that is not related to a change in environmental temperature may be an indicator of gastroduodenal transition.

[0171] Figure 6 shows the recorded readings of the environmental temperature sensor 14a (top row readings in the top graph) and the corrected TCD gas sensor readings over time during the instance of capsule ingestion and gastrointestinal progression. The gastroduodenal transition index, also known as gastric emptying, is indicated by a spike above a threshold height in the corrected TCD gas sensor reading. The height of the spike can be measured, for example, by its distance (e.g., as a percentage, absolute value, or number of standard deviations) from a trend line fitted to the readings up to that point, or its distance from the average value up to that point (the processor maintains the average value). The VOC sensor trace, marked as motility (heat), shows a drop at approximately 5 hours, which is the ileocecal index.

[0172] Figure 6 shows the gastroduodenal transit index displayed in gas sensor data (TCD gas sensor readings corrected for changes in environmental temperature). The thermal conductivity of gas mixtures in the gastrointestinal tract changes as the relative concentrations of gases such as CO2 and H2 change. CO2 is produced when hydrochloric acid in gastric juice leaves the stomach and mixes with bicarbonate in bile acids released from the pancreas. This reaction also neutralizes the pH of the fluid. The method is able to detect this event using temperature-compensated TCD gas sensor data rather than the resolved CO2 contribution because the TCD gas sensor data has less noise. The raw TCD gas sensor readings are adjusted to compensate for the temperature change measured by the environmental temperature sensor 14a.

[0173] As shown in Figure 2B, the circuit includes a directional coupler 171 connected in series with the antenna 17, which acts as a reflectometer. A diode detector measures the amplitude of the reflected signal from the antenna. The diode detector's measurement is the reflectometer reading, which measures the reflected energy from the antenna, i.e., the energy not radiated from the antenna 17 due to impedance mismatches. The reflectometer reading measures the antenna's radiation efficiency, which is affected by the dielectric of the material surrounding the capsule.

[0174] Readings may become noisy and / or experience a baseline shift in timing of gastroduodenal transition events, e.g., increased noise and / or baseline shifts may be detectable as transition indicators.

[0175] Optionally, an absolute value range may be established for the reflectometer readings, with readings within that range indicating the presence of capsule 10 in the small intestine, noting that due to noise, a series of readings are averaged (optionally after outliers have been removed) and compared to the range of values ​​on a rolling basis to obtain an indication that capsule 10 is present in the small intestine.

[0176] Figure 7 shows reflectometer readings (labeled "Ant" for antenna) versus time (top graph of the lower set of two axes), with gastric emptying events (GET) marked. Antenna 17 and directional coupler 171 function as a reflectometer, measuring reflected energy from the antenna, i.e., energy not radiated from the antenna. This signal changes as the dielectric properties of the surroundings change, most notably as the capsule moves from the spongy, fluid-filled stomach into the tubular tissue of the small intestine. The shift in reflectometer readings was observed to coincide with the GET indicator in the corrected TCD readings (localized spikes), making it a reliable secondary measure.

[0177] FIG. 8 is a plot of recorded readings (or processed versions thereof) of multiple sensors and pseudo-sensors (reflectometers) within capsule 10 versus time. Gastric emptying (gastroduodenal transition) events are labeled. The top plot of the graph in FIG. 8 shows reflectometer readings versus time (labeled "Ant" for antenna). Baseline shifts have been found to coincide with spikes in the corrected TCD gas sensor readings (the spikes are gastroduodenal transition indicators). For example, baseline shifts can be detected on a progressive / rolling basis by comparing the average of the most recent consecutive readings (e.g., 5, 10, or 20) with the average of the readings preceding the most recent consecutive readings (or the previous reading in the case of reverse time series processing). Baseline shifts can be indicated by a difference exceeding a threshold, which can be an absolute value, a percentage, or a value determined relative to the standard deviation of the readings. Detecting a coincident gastroduodenal transition indicator in the reflectometer output may be sufficient to confirm that the first gastroduodenal transition indicator was caused by gastroduodenal transition of capsule 10 and determine the timing of gastroduodenal transition. Alternatively, the combination of the two indicators may be evaluated via a probabilistic model to modify a confidence score and compare the modified confidence score to a threshold, where meeting the threshold determines that the first gastroduodenal transition indicator was caused by gastroduodenal transition of capsule 10, and thus determines the timing of gastroduodenal transition.

[0178] As shown in FIG. 2B, the capsule 10 may include an accelerometer 19. The accelerometer 19 may provide one or more of a gastroduodenal transition index and an ileocecal transition index. The index provided by the accelerometer 19 may be used alone to determine the timing of an event or may be combined with an index from another source for increased reliability. An exemplary accelerometer 19 measures roll around three mutually orthogonal axes. Readings from the accelerometer 19 may be a vector with a component per axis, with each component indicating the instantaneous angular acceleration around the corresponding axis or the average acceleration around the corresponding axis over a period of time since the previous live reading. Alternatively, the readings may provide a three-dimensional orientation of the capsule. Onboard the capsule, a receiving device 30 or a remote computer 20 may process the accelerometer readings to generate a representation (e.g., a plot versus time) of aggregated (i.e., all three axes) accelerometer readings in which a marker (i.e., a gastroduodenal transition index) can be identified. Such a plot or representation may also be used to identify markers of other events, including elimination events. The processor hardware 151 of the capsule 10, or the receiving device 30 or other remote computing device that processes the sensor data, is configured to process the acceleration sensor data to obtain a time series of metrics (representative metrics) representative of the accelerometer data, from which one or more of the gastroduodenal transit index, ileocecal index, and emptying index can be identified and the timing of corresponding motility events can therefore be determined, or the time series can lend confidence to timing determined from, for example, gas sensor data or antenna reflectivity data.

[0179] An exemplary algorithm for processing accelerometer data to obtain a representative metric measures the angle of tilt between a capsule reference axis or line (e.g., the capsule's long axis) that has a fixed relationship to the capsule and a reference axis, line, or plane (e.g., the horizontal) that has a fixed relationship to the Earth. The metric is cumulative and increases by an amount that the tilt angle exceeds a hysteresis range. When the hysteresis range is exceeded, the metric increases and the hysteresis range decreases by the same amount. This metric tracks the cumulative angle traveled by the capsule with reference to the two-dimensional representation. Advantageously, this algorithm excludes roll about the capsule's reference axis or line; for example, if the capsule's reference axis or line is the capsule's long axis, the algorithm excludes roll about the long axis (which may thus be referred to as capsule tipping). This algorithm is fast and computationally efficient, and the metric tracks a clear signal.

[0180] Example: Obtaining time series data Figures 9 and 10 each show time series of readings from the gas sensor hardware during a live test of an ingestible capsule device ingested by a different subject. Reference or trend lines are shown as dashed lines. Specifically, the time series show readings from the TCD gas sensor corrected to account for changes in environmental temperature, as measured by the environmental temperature sensor 14a mounted on the capsule 10. The subject in Figure 9 is a true-negative subject (no SIBO present) confirmed by the current clinical standard aspirate test, while the subject in Figure 10 is a true-positive subject (SIBO present). Gastroduodenal and ileocecal transit events are shown in Figures 9 and 10. Data points are TCD gas sensor readings corrected to compensate for changes in environmental temperature. Readings from the first 30 minutes after gastroduodenal transit timing are truncated before processing to calculate trend lines and metrics representing variability. The 30-minute value is configurable; values ​​such as 5 minutes or less, 5 to 10 minutes, 10 to 15 minutes, 15 to 30 minutes, and 30 minutes to 1 hour can also be used. Optionally, such truncation may be performed prior to the determined ileocecal indicator timing, such that the relevant period begins at or a period after the determined gastroduodenal transition timing and ends at or a period before the determined ileocecal transition timing. Note that while capsule 10 is within the small intestine for the entire relevant period, the relevant period is not necessarily the entire time that capsule 10 is within the small intestine.

[0181] Paradigm: Determining metrics to represent variation In step S20, a metric representing the variation of a first characteristic of the gas sensor data, i.e., the concentration of the gas(es), over a relevant time period is calculated. In an exemplary embodiment, the metric representing the variation is a scalar sum or aggregation of deviations from a reference line. Above a predetermined threshold, the metric representing the variation is a variability index. This may be considered as the cumulative magnitude distance between a data point and a fixed line, such as a trend line or X-axis. Note that because the characteristic quantifies the variability of the gas sensor data, magnitude, not direction, is considered. An on-board processor may be configured to determine the relevant time period and calculate the first characteristic, or the gas sensor data may be transmitted by the capsule 10 to the receiving device 30 and processed there, or processed at the remote computing device 20, to determine the relevant time period and calculate the first characteristic.

[0182] Figure 11A shows deviation from the baseline, but the level is low and not indicative of SIBO. The larger the area between the data points and the reference line in Figure 11B, the greater the fermentation and bacterial load, indicating SIBO.

[0183] Figures 11A and 11B each show a time series of readings from the gas sensor hardware during a live test of an ingestible capsule device ingested by a different subject. The reference or trend line is shown as a dashed line. In particular, the time series are readings from the TCD gas sensor corrected to account for changes in environmental temperature as measured by the environmental temperature sensor 14a mounted on the capsule 10. The subject in Figure 11A is a true-negative subject (no SIBO present) confirmed by the current clinical standard aspirate test, while the subject in Figure 11B is a true-positive subject (SIBO present). The data points are TCD gas sensor readings corrected to compensate for changes in environmental temperature. The first 30 minutes of readings after gastroduodenal transit timing are truncated prior to processing to calculate the aggregate variance (first characteristic) and the slope of the trend line (second characteristic).

[0184] The aggregate variability is the sum of the magnitude of the difference between each data point and a reference line, which may be the x-axis or a trend line fitted to the data points. Visually, the aggregate variability is identifiable as the area below the continuous line connecting the data points to the reference line (or specifically, the area between the continuous line and the reference line).

[0185] In each of Figures 11A and 11B, the reference lines are trend lines fitted to the data points for the respective relevant time periods.

[0186] In the example of FIG. 11A, the subject tested negative for SIBO in the aspirate, and the gas sensor data showed little variation from the reference line over the relevant time period. The concentration of gas sensed by the TCD gas sensor was constant. For completeness, it is noted that the data in FIG. 11A may be the result of different gas concentrations, which may by chance cancel each other out in terms of their effect on the thermal conductivity of the gas mixture.

[0187] In the example shown in Figure 11B, a subject tested positive for SIBO in the aspirate and the gas sensor data was found to exhibit significant fluctuations from the trend line during the relevant time period. Such fluctuations are caused by changes in the concentrations of the constituent gases to which the gas sensor hardware is exposed during its passage through the small intestine. The association with SIBO is based on the hypothesis that the fluctuations are the result of fermentation associated with bacterial overgrowth.

[0188] Paradigm: Comparison of a metric representing variation with threshold(s) In S30, a metric representing variation, such as the aggregate variation calculated in S20, is compared to a predetermined threshold. The threshold is set based on test data such as those shown in FIGS. 11A and 11B, and the intent behind setting the threshold is to distinguish positive aspirate cases from other cases. Note that normalization may be applied depending on the length of the time period. Furthermore, note that there may be two predetermined thresholds that do not necessarily coincide: a positive indicator threshold (above which the metric representing variation indicates SIBO) and a negative indicator threshold (below which the metric representing variation indicates SIBO negative). (As shown below, the positive indicator threshold itself may be divided into an independent positive threshold and a dependent positive threshold.) There may be a gap between the positive indicator threshold and the negative indicator threshold (the negative indicator threshold is lower than the positive indicator threshold), and a metric representing variation values ​​that fall within that gap does not indicate positive or negative SIBO. The positive indicator threshold and the negative indicator threshold may coincide.

[0189] Additionally, SIBO may be diagnosed based on the metric representing the variation at S30 alone, alone, or in combination with the slope of the trend line at S32. The method may apply two positive indicator thresholds to the metric representing the variation at S30: an independent positive indicator threshold above which SIBO is diagnosed even without the results of the comparison of the slope of the trend line at S32, and a lower dependent positive indicator threshold above which SIBO is diagnosed, also dependent on the results of the comparison of the slope of the trend line at S32.

[0190] Example: Comparing the slope of a trend line to threshold(s) In S22 of Figures 4C, 4D, 4E, and 4G, a trend line is fitted to the gas sensor data. Note that a trend line can also be fitted to the data to serve as a reference line as part of the metric representing the variance calculation in S20. The trend line can be the same in each instance.

[0191] In S32, the slope of the trend line calculated or fitted in S22 is compared to a predetermined threshold. The slope of the trend line may be referred to as a second characteristic of the gas sensor data. The trend line is a first-order polynomial. FIGS. 12A and 12B each show a time series of readings from the gas sensor hardware during live testing of an ingestible capsule device ingested by a different subject. In particular, the time series are readings from the TCD gas sensor corrected to account for changes in environmental temperature as measured by the environmental temperature sensor 14a mounted on the capsule 10. The threshold is set based on the test data, as shown in FIGS. 12A and 12B, with the intent of setting the threshold to distinguish positive aspirate cases from other cases. Further, it is noted that there may be two predetermined thresholds, which do not necessarily coincide: a positive indicator threshold (above which the slope of the trend line indicates SIBO) and a negative indicator threshold (below which the slope of the trend line indicates negative SIBO). (As shown below, the positive indicator threshold itself may be divided into an independent positive indicator threshold and a lower dependent positive indicator threshold.) There may be a gap between the positive indicator threshold and the negative indicator threshold (the negative threshold is lower than the positive threshold), and trend line slope values ​​that fall within the gap do not indicate positive or negative SIBO. The positive indicator threshold and negative indicator threshold may coincide.

[0192] Further, it is noted that SIBO can be diagnosed based on the comparison at S30 alone, the comparison at S32 alone, or a combination of the comparisons at S30 and S32. The method can apply two positivity thresholds to the slope of the trend line at S32: an independent positivity threshold (above which SIBO is diagnosed even in the absence of the result of the metric representing the variability comparison at S32) and a lower dependent positivity threshold (above which SIBO is diagnosed also depending on the result of the comparison of the slope of the trend line at S32).

[0193] The ingestible capsule device 10 used in the testing and data collection exercises was designed and manufactured by Atmo Biosciences Pty Ltd and may be referred to as the Atmo Gas Capsule.

[0194] Test Data: ROC and Threshold Figures 13A and 13B show the sensitivity to the slope of the trend line (Figure 13A) and the specificity of the aggregate variation or area under the curve, AUC (Figure 13B). The receiver operating characteristic (ROC) curves in Figures 13A and 13B use the clinical aspirate test as the sole source of truth. The thresholds for the trend line slope and aggregate variation can be selected based on the ROC curve depending on the required selectivity and specificity. The thresholds can be configured to combine to determine a positive judgment for the presence of SIBO (referred to elsewhere as dependent positive thresholds). For more information on combining measures, see below. The thresholds can also be selected as standalone thresholds (independent positive thresholds) so that the presence of SIBO is determined solely based on the aggregate variation or the slope of the trend line.

[0195] Test data: A combination of aggregate variation and trend line slope Independent positive thresholds for the trend line slope and aggregate variation can be applied to determine whether a subject has SIBO based on either feature alone. Alternatively, the two thresholds can be combined, such that both the trend line slope and the aggregate variation AUC must satisfy their respective slopes to determine that a subject has SIBO. Figures 4C and 4D illustrate such an approach. Combining the two features in this way increases confidence in the determination.

[0196] 14A-14C each show a time series of readings from the gas sensor hardware during a live test of the ingestible capsule device ingested by a different subject. In particular, the time series are readings from the TCD gas sensor corrected to account for changes in environmental temperature as measured by the environmental temperature sensor 14a mounted on the capsule 10.

[0197] FIG. 14A shows gas sensor data (temperature corrected TCD) from a study in which SIBO was determined to be absent because the slope of the trend line did not exceed the threshold set for the slope of the trend line and the aggregate variability did not meet the threshold set for the aggregate variability.

[0198] FIG. 14B shows gas sensor data (temperature corrected TCD) from a study in which SIBO is determined to be absent because the slope of the trend line exceeds the threshold set for the slope of the trend line, but the aggregate variability does not meet the threshold set for the aggregate variability.

[0199] FIG. 14C shows gas sensor data (temperature corrected TCD) from a study in which SIBO was determined to be present because the trend line slope exceeded the threshold set for the trend line slope and the aggregate variability met the threshold set for the aggregate variability.

[0200] 15A and 15B show gas sensor data (temperature corrected TCD) from a further study in which SIBO was determined to be present because the trend line slope exceeded the threshold established for the trend line slope and the aggregate variability met the threshold established for the aggregate variability, consistent with the results from the small intestinal aspirate of the same subject.

[0201] 16A and 16B show gas sensor data (temperature corrected TCD) from a further study in which SIBO was determined to be absent based on a combination of threshold tests, as the aggregate fluctuations did not meet the thresholds established for aggregate fluctuations, consistent with the results from the small intestinal aspirate of the same subject.

[0202] Figure 17 shows the results of a comparison of this two-threshold technique (called the Atmo Dx method) with small intestine aspirate testing, obtained from 23 trials (i.e., 23 capsules administered to 23 different subjects, each with a positive or negative result for the small intestine aspirate). This method has an 83% accuracy rate for determining the true small intestine aspirate. Note that due to the inaccuracy of small intestine aspirate testing, the accuracy of the agreement with the small intestine aspirate testing does not necessarily represent the true accuracy, and may be higher or lower.

[0203] FIG. 18 illustrates a hardware arrangement of an apparatus configured to perform the method(s) described herein. FIG. 18 is a schematic diagram of a hardware arrangement of a computing device. The methods described herein may be performed by an apparatus having an arrangement such as that shown in FIG. 18. An apparatus having processor hardware and memory hardware described herein may include one or more devices having an arrangement such as that shown in FIG. 18. Multiple such devices may be interconnected via a network, such as a local area network or the Internet. A cloud service that includes performing one or more of the methods described herein may be performed by one or more devices having an arrangement such as that shown in FIG. 18.

[0204] A computing device includes multiple components interconnected by a bus connection. The bus connection is an exemplary form of data and / or power connection. In addition to or instead of a bus connection, direct connections between components for the transfer of power and / or data may be provided.

[0205] The computing device includes memory hardware 991 and processing hardware 993; these components are required regardless of implementation. Additional components are context dependent, including a network interface 995, input devices 997, and a display unit 999. The display unit 999 and processing hardware 993 can work together to implement a graphical user interface.

[0206] The memory hardware 991 stores processing instructions to be executed by the processing hardware 993. The memory hardware 991 may include volatile memory and / or non-volatile memory. The memory hardware 991 may store data pending processing by the processing hardware 993, and may also store data resulting from processing by the processing hardware 993.

[0207] The processing hardware 993 includes one or more interconnected and cooperating CPUs that process data according to processing instructions stored by the memory hardware 991 .

[0208] The computing device may include one computing device according to the hardware arrangement of Figure 18, or multiple such devices operating in conjunction with each other, for example in a client:server arrangement.

[0209] The network interface 995 provides an interface for transmitting and receiving data over a network. Connection to one or more networks is provided, for example, a local area network and / or the Internet. The connections can be wired and / or wireless.

[0210] The input device(s) 997 provide a mechanism for receiving input from a user. For example, such devices may include one or more of a mouse, a touchpad, a keyboard, a gaze system, and a touchscreen touch interface. The input may be received over a network connection. For example, in the case of a server computer, a user may connect to the server through a connection to another computing device and provide input to the server using an input device on the other computing device.

[0211] The display unit 999 provides a mechanism for visually displaying data to a user. The display unit 999 can display a user interface that allows certain locations on the display unit to function as buttons or other means for interacting with the data via an input mechanism such as a mouse. A server can be connected to the display unit 999 via a network.

[0212] Any discussion of documents, acts, materials, devices, articles or the like contained in this specification should not be construed as an admission that any or all of such matters form part of the prior art or were general general knowledge in the art relevant to this disclosure as existing prior to the priority date of each appended claim.

[0213] Throughout this specification the word "comprise" or variations such as "comprises" or "comprising" will be understood to mean the inclusion of a stated element, integer, step, or group of elements, integers, or steps, but not the exclusion of other elements, integers, steps, or groups of elements, integers, or steps.

[0214] Any discussion of documents, acts, materials, devices, articles or the like contained in this specification should not be construed as an admission that any or all of such matters form part of the prior art or were general general knowledge in the art relevant to this disclosure as existing prior to the priority date of each appended claim.

Claims

1. 1. A method for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, comprising: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in the composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate a metric representative of the variation in the gas sensor data during passage of the ingestible capsule device through the small intestine of the gastrointestinal tract; determining the presence or absence of SIBO in the subject based at least in part on the metric representing variation.

2. 2. The method of claim 1, wherein the determining includes, as a first comparison, comparing the metric representative of variation to a predetermined threshold and using a result of the first comparison to determine whether SIBO is present or absent, and wherein the metric representative of variation is an aggregate variation such as a cumulative aggregate variation, or the metric representative of variation is a standard deviation or variance from a trend line.

3. 3. The method of claim 1, wherein the gas sensor data is obtained by taking readings representative of an environmental temperature of the ingestible capsule device from an environmental temperature sensor housed within the ingestible capsule device and compensating sample values ​​of an output signal generated by the gas sensor hardware to account for changes in environmental temperature, the gas sensor data being the compensated values.

4. 10. The method of any one of the preceding claims, wherein the gas sensor data represents the concentration of a particular gas or gases.

5. 5. The method of claim 4, wherein the gas sensor data is obtained by processing sample values ​​of an output signal generated by the gas sensor hardware to extract the concentration of the particular gas(es).

6. 6. The method of claim 5, wherein the specific gas(es) is one or more of carbon dioxide CO2, hydrogen H2, methane, and one or more VOCs.

7. 7. The method of any one of claims 4 to 6, wherein determining the presence or absence of SIBO in the subject depends at least in part on the concentration of the particular gas(es) exceeding a predetermined threshold concentration at one location or a predetermined threshold number of locations during passage of the ingestible capsule device through the small intestine.

8. The method of any one of claims 1 to 3, wherein the gas sensor data is a sample of, or is directly proportional to, an output signal generated by the gas sensor hardware.

9. The method comprises: further comprising fitting a trend line to the gas sensor data; 9. The method of claim 1, wherein determining the presence or absence of SIBO in the subject depends at least in part on the metric representing variation and at least in part on the slope of the trend line or the average slope of the trend line.

10. The determining step includes comparing the slope of the trend line to a second predetermined threshold as a second comparison; and combining the results of the first comparison with the results of the second comparison to detect the presence or absence of small intestinal bacterial dysgrowth in the subject.

11. 11. The method of claim 10, wherein the determining comprises calculating a weighted average or weighted sum of characteristics including at least the metric representing the variation and the slope of the trend line, comparing the weighted average to a predetermined threshold, and determining the presence or absence of SIBO depending on the result of the comparison.

12. 12. The method of claim 11, wherein the gas sensor data represents a concentration of a particular gas(es), and the characteristics further include the number of times or duration that the concentration of the particular gas(es) exceeds a predetermined threshold concentration during passage of the capsule through the small intestine.

13. 10. The method of any one of the preceding claims, wherein the gas sensor hardware includes a TCD gas sensor, and the gas sensor data represents a time series of readings from the TCD gas sensor.

14. The method comprises:

10. The method of any one of the preceding claims, further comprising detecting a gas sensor data gastroduodenal transition index from the gas sensor data and / or detecting a gas sensor data ileocecal transition index from the gas sensor data, and determining a timing of transit of the ingestible capsule device through the small intestine of the subject based on a timing of the detected gas sensor data gastroduodenal transition index and / or the detected gas sensor data ileocecal transition index.

15. The method comprises: acquiring accelerometer data representing a time series of readings from an accelerometer housed within the ingestible capsule device, the time series of readings being acquired while the ingestible capsule device passes through the gastrointestinal tract of the subject; 10. The method of any one of the preceding claims, further comprising detecting an accelerometer data gastroduodenal index and / or an accelerometer data ileocecal index within the accelerometer data, and determining a timing of passage of the ingestible capsule device through the small intestine of the gastrointestinal tract based on the accelerometer data gastroduodenal index and / or the accelerometer data ileocecal index.

16. The method comprises: acquiring reflectometer data representing a time series of readings from a reflectometer housed within the ingestible capsule device, the reflectometer including a transmitting antenna connected in series with a directional coupler configured to measure a reflected signal from the transmitting antenna, the time series of readings being acquired while the ingestible capsule device passes through the gastrointestinal tract of the subject; 10. The method of any one of the preceding claims, further comprising detecting a reflectometer data gastroduodenal index and / or a reflectometer data ileocecal index within the reflectometer data, and determining a timing of passage of the ingestible capsule device through the small intestine of the gastrointestinal tract based on the reflectometer data gastroduodenal index and / or the reflectometer data ileocecal index.

17. Determining the timing of passage of the ingestible capsule device through the small intestine of the subject includes:

17. The method of any one of claims 14-16, comprising determining when the ingestible capsule device passes through the gastroduodenal junction based on one or more of the gas sensor data gastroduodenal index, the accelerometer data gastroduodenal index, and the reflectometer data gastroduodenal index.

18. Determining the timing of passage of the ingestible capsule device through the small intestine of the subject includes:

18. The method of any one of claims 14 to 17, comprising determining when the ingestible capsule device passes through the ileocecal region based on one or more of the gas sensor data ileocecal region index, the accelerometer data ileocecal region index, and the reflectometer data ileocecal region index.

19. quantitating the amount of small intestinal bacterial overgrowth in the subject according to the value of the metric representing variation; quantitating the amount of small intestinal bacterial overgrowth in the subject according to the slope of the trend line; or 10. The method of any one of the preceding claims, further comprising quantifying the amount of small intestinal bacterial overgrowth in the subject according to the number of times or duration that the concentration of the particular gas(es) exceeds a predetermined threshold concentration during passage of the capsule through the small intestine.

20. 10. The method of any one of the preceding claims, further comprising generating a report comprising the detection of the presence or absence of small intestinal bacterial overgrowth in the subject.

21. Detected fermentation indicators, the metric representing the variation; the number of events, or duration, during which the concentration of the particular gas(es) represented by the gas sensor data exceeds a predetermined threshold concentration while the capsule is passing through the small intestine; and based on one or more of the slopes of trend lines fitted to the gas sensor data; 21. The method of claim 20, further comprising measuring a level of fermentation activity detected in the small intestine of the subject and including the measured level in the generated report.

22. based on the timing of deviations from a trend line contributing to a metric representing variation and / or the timing of events in which the concentration of a particular gas(es) represented by said gas sensor data exceeds a predetermined threshold concentration; 22. The method of claim 21, further comprising determining the likely location(s) of fermentation activity within the small intestine.

23. 23. The method of claim 21 or 22, wherein the report further comprises the measured level of fermentation activity and / or the estimated location(s) of fermentation activity within the small intestine.

24. 24. The method of any one of claims 20-23, wherein the method is performed by processor and memory hardware within the ingestible capsule device, and the method further comprises wirelessly transmitting the report to a receiving device outside the subject's body.

25. 25. The method of claim 24, wherein the wireless transmission occurs via a Bluetooth transceiver housed in the ingestible capsule device.

26. 24. The method of any one of claims 1 to 23, wherein the method is performed by a computing device that includes processor hardware and memory hardware and that receives data directly or indirectly from the ingestible capsule device.

27. 24. The method of any one of claims 1 to 23, wherein portions of the method are performed by processor hardware and memory hardware within the ingestible capsule device, and the method further comprises wirelessly transmitting data representing the performed portions of the method from a wireless transceiver housed in the ingestible capsule device to a receiving device outside the subject's body, and receiving and using the transmitted data representing the performed portions of the method at the receiving device or a computing device in data communication therewith to complete the method.

28. an ingestible capsule device comprising: an ingestible, non-digestible, biocompatible housing; and within the housing: a power source; sensor hardware including gas sensor hardware; processor hardware; memory hardware; and a wireless data transmitter; the memory hardware storing processing instructions that, when executed by the processor hardware, cause the processor hardware to perform a process for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject; The process comprises: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in the composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate a metric representative of the variation in the gas sensor data during passage of the ingestible capsule device through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the metric representative of variation.

29. 1. A computer program that, when executed by a processor, causes the processor to perform a process for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the process comprising: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in the composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate a metric representative of the variation in the gas sensor data during passage of the ingestible capsule device through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the metric representing variation.

30. 30. A non-transitory computer readable medium storing the computer program of claim 29.

31. 1. A method for detecting small intestinal bacterial overgrowth (SIBO), comprising: acquiring data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by a subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, each reading representing a composition of the gas mixture at a location of the ingestible capsule device within the gastrointestinal tract of the subject; Detecting an ileocecal transition index from the acquired data, and based on the timing of the detected ileocecal transition index, detecting a fermentation index from the acquired data representing a reading value prior to the timing of the detected ileocecal transition index among the time series reading values; In response to detecting the fermentation indicator, determining the presence of SIBO in the subject and generating a report indicating that the presence of SIBO in the subject has been determined.

32. 32. The method of claim 31 , wherein the fermentation indicator is detected by processing a time series of readings from the gas sensor hardware to obtain a time series of values ​​representing the concentration of a particular gas(es) in the gas mixture at the location of the ingestible capsule device, and detecting the concentration of the particular gas(es) above a predetermined threshold as the fermentation indicator.

33. 1. A method for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the method comprising: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; using the gas sensor data to calculate a slope of a best-fit line of a first-order polynomial fitted to the gas sensor data as the ingestible capsule device passes through the small intestine of the gastrointestinal tract; and determining the presence or absence of SIBO in the subject based at least in part on the slope of the best-fit line.

34. 1. A method for detecting the presence or absence of small intestinal bacterial overgrowth (SIBO) in a subject, the method comprising: acquiring gas sensor data representing a time series of readings from gas sensor hardware contained within an ingestible capsule device ingested by the subject, the time series of readings being acquired while the gas sensor hardware is exposed to a gas mixture in the ingestible capsule device as the ingestible capsule device passes through the gastrointestinal tract of the subject, the gas sensor hardware being sensitive to changes in composition of the gas mixture at the location of the ingestible capsule device within the gastrointestinal tract of the subject; the gas sensor data represents a concentration of a particular gas or gases; The method further includes determining the presence or absence of SIBO in the subject based at least in part on a concentration of a particular gas or gases exceeding a predetermined threshold concentration at one location or a minimum number of locations during passage of the ingestible capsule device through the small intestine.

35. 35. The method of claim 34, wherein the particular gas(es) is one or more of hydrogen, carbon dioxide, and methane.

36. The method of any one of claims 31 to 35, further comprising the method of any one of claims 1 to 27.

37. A computer program which, when executed by a processor, causes the processor to carry out a method according to any one of claims 1 to 27 or any one of claims 31 to 36.

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