WO2020118372A1 - Procédé de surveillance d'une région gastro-intestinale chez un sujet - Google Patents

Procédé de surveillance d'une région gastro-intestinale chez un sujet Download PDF

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Publication number
WO2020118372A1
WO2020118372A1 PCT/AU2019/051368 AU2019051368W WO2020118372A1 WO 2020118372 A1 WO2020118372 A1 WO 2020118372A1 AU 2019051368 W AU2019051368 W AU 2019051368W WO 2020118372 A1 WO2020118372 A1 WO 2020118372A1
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Prior art keywords
property
values
subject
parameter values
parameter
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PCT/AU2019/051368
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English (en)
Inventor
Josephine MUIR
Xuhao DU (Peter)
Katherine Mary WEBBERLEY
Gary Andrew Peter ALLWOOD
Linda WANG (Ning)
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The University Of Western Australia
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Priority claimed from AU2018904744A external-priority patent/AU2018904744A0/en
Application filed by The University Of Western Australia filed Critical The University Of Western Australia
Publication of WO2020118372A1 publication Critical patent/WO2020118372A1/fr

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    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/4255—Intestines, colon or appendix
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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Definitions

  • the present invention relates to a method of monitoring a gastrointestinal (Gl) region in a subject, and more particularly, although not exclusively, to a method of monitoring the subject’s Gl region to provide an indication of the existence or non-existence of at least one Gl symptom.
  • Gl gastrointestinal
  • Gl disorders such as irritable bowel syndrome (IBS), and Gl organic diseases such as inflammatory bowel disease (IBD) including Crohn’s disease and ulcerative colitis are debilitating Gl conditions.
  • IBS irritable bowel syndrome
  • IBD inflammatory bowel disease
  • IBS is commonly characterised by a number of Gl symptoms including diarrhea, constipation, cramps, bloating, burps, flatulence, reflux, vomiting and nausea.
  • effective treatments for IBS exist, patients have to undergo a lot of trial and error, with different medications, lifestyle and dietary changes, to understand what the triggers to the IBS symptoms are, and to work out which diet or treatment works best for them.
  • Gl changes experienced by patients having an organic Gl disease such as IBD can be measured and monitored using biomarkers such as blood tests or other collection of bio-samples from the patients.
  • biomarkers such as blood tests or other collection of bio-samples from the patients.
  • Such monitoring is however relatively invasive for the patients and it would be advantageous if a non-invasive approach for monitoring IBD symptoms could provide an indication of, for example, an early warning sign for flare-ups of the disease, or a need to visit a clinician and do further testing.
  • a non-invasive approach for monitoring Gl symptoms could also be useful for some individuals that do not have a clear diagnosis of a Gl condition such as a functional gastrointestinal functional disorder or an organic Gl disease. For example, some people may feel unwell after eating some foods and may want to monitor the occurrence of Gl symptoms. Some others may essentially value the ability to monitor their health. An objective method of monitoring Gl symptoms would possibly also aid in identification of food intolerances and monitoring of overall gut health.
  • a method of monitoring a gastrointestinal (Gl) region in a subject comprising:
  • the method may comprise determining whether the property of the collection of parameter values is characteristic of a change relative to the corresponding reference property, the change being indicative of the existence of at least one Gl symptom.
  • the at least one Gl symptom may comprise one or more of the following: diarrhea;
  • the parameter may be associated with a time domain parameter or a frequency domain parameter.
  • the parameter may be associated with higher order zero crossing.
  • the parameter may be associated with burst.
  • the at least one Gl symptom is associated with a Gl condition.
  • the Gl condition may be a functional Gl disorder and the property of the collection of parameter values may be indicative of the existence or non-existence of at least one symptom associated with irritable bowel syndrome (IBS).
  • IBS irritable bowel syndrome
  • the collection of reference parameter values and corresponding reference property may be associated with the non-existence of a Gl symptom.
  • the Gl region may be monitored over a period of time and the property of the collection of parameter values may be associated with a median of the collection of parameter values generated for the period of time.
  • the collection of reference parameter values may comprise a plurality of sub-collections of reference parameter values determined over respective predefined reference time periods, and the reference property may comprise a set of reference sub-properties associated with respective sub-collections of reference parameter values, each reference sub-property being associated with a respective predefined reference time period.
  • Each reference sub-property may be associated with a median of a respective sub-collection of reference parameter values determined over a respective predefined reference time period.
  • Comparing the property of the collection of parameter values with the corresponding reference property may comprise comparing the property of the collection of parameter values with a property range defined by the set of reference sub-properties.
  • the method may comprise:
  • the property of the collection of parameter values is indicative of the existence or non-existence of at least one Gl symptom.
  • Each of the at least two parameters may be associated with a time domain parameter or a frequency domain parameter.
  • Each of the at least two parameters may be associated with a respective order of differentiation of the higher order zero crossing.
  • the method comprises determining whether the property of the collection of parameter values is characteristic of a change relative to the corresponding reference property, the change being indicative of the existence of a respective Gl symptom.
  • the Gl region may be monitored over a period of time and the method may further comprise determining an index value indicative of a gastrointestinal wellness of the subject over the period of time based on respective properties of the collections of parameter values and respective corresponding reference properties determined over the predefined time period of time.
  • the plurality of bowel sounds originates from at least two abdominal sub-regions of the abdominal region of the subject and identifying individual bowel sounds in the abdominal sound comprises associating each respective identified individual bowel sound with a respective one of the at least two abdominal sub-regions.
  • the method may further comprise:
  • the property of the collection of parameter values associated with a first abdominal sub-region of the subject is indicative of the existence or non-existence of a first symptom
  • the property of the collection of parameter values associated with a second abdominal sub-region of the subject is indicative of the existence or non-existence of a second symptom that is different from the first symptom
  • the Gl region is monitored over a period of time and the method further comprises:
  • each sub-index value being indicative of a respective gastrointestinal wellness of the subject over the period of time and being determined based on respective properties of the collections of parameter and respective corresponding reference properties determined over the predefined time period at the respective sub-abdominal region;
  • the overall index value being indicative of an overall gastrointestinal wellness of the subject over the period of time.
  • the method upon determining that the property of the collection of parameter values is characteristic of a change relative to the reference property, the method further comprises determining, for each Gl symptom of the at least one Gl symptom, a severity index value indicative of a severity of the Gl symptom.
  • the method may further comprise determining a combined symptoms severity index value indicative of an overall severity of a combination all Gl symptoms, the combined symptoms severity index value being determined using the respective severity index values for each of the at least one Gl symptom.
  • Figure 1 is a flow chart of a method according to an embodiment
  • Figure 2 is a top view showing vibration sensors and a recording unit that may be used in a device according to an embodiment
  • Figures 3a and 3b are front views illustrating positions where the vibration sensors shown in Figure 2 may be located in accordance with an embodiment
  • Figure 4 is a flow chart of a method in accordance with a specific embodiment
  • Figure 5 is a representation of a bowel sound signal that may be recorded and processed according to an embodiment
  • Figure 6 shows two plots of respective signals with a particular parameter illustrated in accordance with a specific embodiment
  • Figure 7 shows plots representing data determined as a function of time of a property of a collection of parameter values generated for a parameter characteristic for respective individual bowel sounds in accordance with an embodiment
  • Figures 8(a) - (b) show plots representing data determined as a function of time of respective properties of collections of parameter values generated for two parameters characteristic of respective individual bowel sounds in accordance with a further embodiment
  • Figures 9(a) - (b) show plots representing data determined as a function of time of respective properties of collections of parameter values generated for two parameters characteristic of respective individual bowel sounds in accordance with the embodiment of Figures 7;
  • Figures 10(a) - (c) show plots representing data determined as a function of time of respective properties of collections of parameter values generated for two parameters characteristic of respective individual bowel sounds in accordance with a further embodiment
  • Figure 1 1 shows a flow diagram of a determination of the existence of Gl symptoms in accordance with an embodiment
  • Figures 12(a) - (b) show plots representing values of a property of collections of determined parameter values associated with a parameter and characteristic of respective individual bowel sounds identified from an abdominal signal recorded in a subject without symptoms (baseline - Figure 12(a)) and with a given symptom ( Figure 12(b)) in accordance with a further embodiment;
  • Figures 12(c) - (d) show plots representing values of a property of respective collections of determined parameter values associated with a parameter and characteristic of respective individual bowel sounds identified from an abdominal signal recorded in another subject without symptoms (baseline - Figure 12(c)) and with a given symptom ( Figure 12(d)) in accordance with a further embodiment;
  • Figures 13(a) - (b) show plots representing values of a property of respective collections of determined parameter values associated with a parameter and characteristic of respective individual bowel sounds identified from an abdominal signal recorded in a subject without symptoms (baseline - Figure 13(a)) and with a given symptom ( Figure 13(b)) in accordance with a further embodiment;
  • Figures 13(c) - (d) show plots representing values of a property of collections of determined parameter values associated with a parameter and characteristic of respective individual bowel sounds identified from an abdominal signal recorded in the subject associated with Figure 13(a) - (b), without symptoms (baseline - Figure 13(c)) and with a given symptom ( Figure 13(d)) in accordance to a further embodiment;
  • Figures 14(a)-(d) show plots of sub-index values indicative of a gastrointestinal wellness of a subject calculated for each minute of a 25-minute recording and for each channel or abdominal sub-region (channel 1 ( Figure 14(a)), channel 2 ( Figure 14(b), channel 3 ( Figure 14(c), and channel 4 ( Figure 14(d));
  • Figure 14(e) shows a plot of overall index values indicative of an overall gastrointestinal wellness of the subject as a function of time for each minute of a 25-minute recording, the overall index values being obtained using the sub-index values shown in Figures 14(a)-(d);
  • Figure 15 shows a graph of overall index values versus overall Gl feeling as experienced by a subject
  • Figure 16 shows a graph of predicted symptom severity index values as a function of subjective severity feeling values for the symptom of flatulence and for a given subject or patient;
  • Figure 17 shows a graph of predicted symptom severity index values as a function of subjective severity feeling values for the symptom of pain and for a given subject or patient;
  • Figure 18 shows a graph of predicted symptom severity index values as a function of subjective severity feeling values for the symptom of bloating and for a given subject or patient;
  • Figure 19 shows a graph of predicted overall symptom severity index values as a function of subjective severity overall feeling values and for a given subject or patient.
  • Figure 20 shows a graph of predicted combined symptoms severity index values as a function of subjective overall feeling values of severity of the combined symptoms acquired for a given subject or patient in accordance with an embodiment.
  • Embodiments of the present invention relate to a non-invasive method of monitoring a gastrointestinal (Gl) region in a subject based on the subject’s bowel sounds, the method assisting more specifically the subject in the assessment, tracking and monitoring of at least one Gl symptom.
  • the at least one symptom may be associated with a Gl condition, and the method may further assist the subject or patient in identifying symptoms triggers and in assessing the effectiveness of different management strategies of the Gl condition and its associated symptoms.
  • the at least one Gl symptom in accordance with specific embodiments of the present invention includes one or more of the following: diarrhea, flatulence, burp, constipation, bloating, cramp, and abdominal pain.
  • the at least one Gl symptom is associated in a particular application with a functional Gl disorder such as irritable bowel syndrome (IBS).
  • IBS irritable bowel syndrome
  • the at least one Gl symptom may be associated with a Gl condition other than IBS or a functional Gl disorder and may be associated with a Gl organic disease such as inflammatory bowel disease (IBD), for example.
  • IBD inflammatory bowel disease
  • the at least one symptom in accordance with embodiments of the present invention may also be associated with other functional Gl disorders as well as other Gl organic diseases.
  • the at least one Gl symptom may be associated with a condition other than a Gl condition, and may be associated with, for example, indigestion or a feeling of being unwell after eating some types of foods.
  • the method in accordance with embodiments of the present invention may, for example, be used for further assisting patients in the assessment of the effectiveness of one of the most effective management strategies for IBS, such as a diet low in Fermentable Oligosaccharides, Disaccharides, Monosaccharides And Polyols (the so called‘low FODMAP diet’).
  • a diet low in Fermentable Oligosaccharides, Disaccharides, Monosaccharides And Polyols the so called‘low FODMAP diet’.
  • the monitoring of Gl symptoms in patients having IBS using the method in accordance with embodiments of the present invention would enable the patients to track and monitor the impact on the occurrence of Gl symptoms of the first stage of the low FODMAP diet, which consists in the elimination of all FODMAP foods, and then to track and monitor the impact on the occurrence of Gl symptoms of the second stage which consists in reintroducing different food groups.
  • the method in accordance with embodiments of the present invention would allow acquiring useful information regarding the occurrence of Gl symptoms associated with the IBS Gl condition such that a diet tailored to the patient’s specific needs may ultimately possibly be developed.
  • the non-invasive method in accordance with embodiments of the present invention would assist the subject or patient in acquiring information indicative of, for example, an‘early warning’ sign for flare-ups of the disease, and further of a need for a visit to a clinician and/or further testing of bio-samples.
  • the monitoring of the subject’s Gl region by analysing bowels sounds could allow providing an objective assessment of the effectiveness of new treatments in clinical trials, for example.
  • the method in accordance with embodiments of the present invention would subsequently allow managing a diagnosed Gl condition in a relatively more objective, effective and reliable manner.
  • the method in accordance with embodiments of the present invention could be used for monitoring an overall gastrointestinal wellness, or more generally a gastrointestinal health, of a patient.
  • the method 10 comprises at step 12 obtaining an abdominal signal representative of an abdominal sound including a plurality of bowel sounds originating from an abdominal region of the subject.
  • the method 10 then comprises at step 14 identifying individual bowel sounds in the abdominal sound.
  • the method comprises determining a parameter value for each respective identified individual bowel sound using the abdominal signal to generate a collection of parameter values, each parameter value being associated with a parameter and being characteristic of a respective identified individual bowel sound.
  • the method comprises comparing a property of the collection of parameter values with a corresponding reference property of a collection of reference parameter values associated with the patient.
  • the property of the collection of parameter values is indicative of the existence or non-existence of at least one Gl symptom.
  • the device 20 used for recording the subject’s bowel sounds and obtaining an abdominal signal representative of an abdominal sound including a plurality of bowel sounds originating from an abdominal region of the subject.
  • the device 20 comprises an array of vibration sensors for detecting bowel sounds and generating a corresponding signal representative of the bowel sounds.
  • the device 20 comprises four vibration sensors V1 , V2, V3 and V4 that are to be held against a patient’s or subject’s skin in proximity to respective abdominal sub- regions of the abdominal region of the subject in a spaced apart manner.
  • the sensors V1 , V2, V3, and V4 are placed in positions P1 , P2, P3, and P4, respectively, corresponding to abdominal sub-regions or quadrants on the subject’s abdomen as shown in Figure 3.
  • the four quadrants include the upper right quadrant (P1 ), lower right quadrant (P2), upper left quadrant (P3), and lower left quadrant (P4).
  • the vibration sensors may typically be attached to or held in place by a belt or harness (not shown).
  • the belt or harness may be adjustable in length to accommodate for different subjects.
  • the belt may comprise elastic material or Velcro® hook and loop fasteners.
  • the sensors may be held on the subject’s skin using an adhesive.
  • Each vibration sensor V1 to V4 may comprise, for example, a piezoelectric sensor component and a transducer for converting detected sounds into electrical signals.
  • each vibration sensor V1 to V4 may comprise Microelectromechanical Systems (MEMS). Each vibration sensor may further incorporate double transducers to allow for active noise cancellation if used in a noisy environment. It will further be understood that any other type of vibration sensor may alternatively be used and may be arranged to allow for active noise cancellation.
  • the sensors V1 to V4 are connected to a recording unit 22 of the device 20 for recording the signals.
  • the recording unit 22 is a hand-held recorder however, it will be appreciated that other suitable recording units may be used. Without active noise cancellation, bowel sounds would generally be recorded in a relatively quiet environment.
  • the signal representative of the bowel sounds recorded from the four sensors V1 , V2, V3 and V4 is then processed by a processor and a software associated with the recording unit 22 to identify individual bowel sounds and associate each respective individual bowel sound with a respective one of the abdominal sub-regions P1 , P2, P3 and P4.
  • the processor and software may form part of the recording unit 22 wherein the recording unit 22 comprises an analogue-to-digital converter for the purpose of digital signal processing, or alternatively, the obtained corresponding signal from the vibration sensors V1 , V2, V3 and V4 could be transmitted to a desktop of a server for processing and analysis.
  • the corresponding signals could be transmitted for example via cables or wirelessly by Bluetooth or Wi-Fi, or via the Internet.
  • the subject or patient wears the belt with the four sensors V1 , V2, V3, and V4 for one period per day, each period being a minimum of 20 minutes.
  • a Gl condition such as IBS characterized by the occurrence of Gl symptoms such as diarrhea, burp, flatulence, bloating, constipation, cramp, and abdominal pain (as well as other symptoms such as nausea or vomiting, for example)
  • the subject wears the belt for a period of about 20 minutes once in the day at a time when the patient might typically expect to experience Gl symptoms.
  • This time would vary for different patients and the subject would preferably wear the belt when it is a convenient time.
  • the subject would wear the belt for a period of about or at least 20 minutes first thing in the morning after fasting overnight, at a set time after a meal during the day, such as after a period of 40 minutes following the consumption of a standard meal during the day, or in the evening. It is then proposed that the subject wears the belt for a period of about 20 minutes on another day during a same period of the day and around the time when the subject typically expects to experience Gl symptoms. The subject may then wear the belt at similar times each day for multiple days, for example for approximately seven days, for monitoring the Gl region for the occurrence of Gl symptoms.
  • An abdominal signal obtained for a recording of bowel sounds during which the patient eventually does not experience Gl symptoms constitutes a reference baseline, which is then used for determining a change indicative of the occurrence of at least one Gl symptom based on an abdominal signal obtained for a recording of bowel sounds during which the patient did experience a Gl symptom.
  • the subject may wear the belt for a longer period once a day, such as for 30 minutes, 40 minutes, an hour, or two hours.
  • the subject may wear the belt for a shorter period, such as for 3 minutes, 5 minutes, 10 minutes or any period between a few minutes and 20 minutes, or any other suitable amount of time. It is also envisaged that the subject may wear the belt more than once a day, such as twice in the day, or for multiple periods per day such as after each standard meal, and/or for one early morning and one evening recording for acquisition of the corresponding signal representative of recorded bowel sounds from the four abdominal regions of the subject. In parallel to the recordings of bowel sounds, the subject or patient may further enter information in a diary during the first few days of recordings about specific Gl symptoms that the patient felt he or she experienced during the recordings. This information can be entered via a web application a desktop or mobile device, or via a smart phone application, and stored in a database on a server to be further used for correlations and/or further analyses of the data derived from the recordings of the bowel sounds.
  • the recordings of bowel sounds are typically sampled at a sampling rate of 44.1 kHz, equating to, for example, approximately 53 million samples for a recording of approximately 20 minutes.
  • the processing of the corresponding abdominal signal as described below is then conducted and results in identification of respective frequency spectrum data and respective individual bowel sounds for a given subject or patient.
  • the recorded corresponding abdominal signal 42 is processed by dividing at step 44 the corresponding abdominal signal into signal segments, XBS 1 , XBS 2, , XBS N, where XBS is bowel sound time series data.
  • the segments may for example be 20-40ms in length, such as approximately 30 ms in length.
  • a window function was used for the segmentation with a window size of approximately 30 ms, with 20 ms overlap between adjacent windows.
  • bowel sounds are usually short bursts, where the energy versus time distribution is extremely uneven, a rectangular window function was selected.
  • the frequency response (S N ) of the sound detector or recording unit 22 is also evaluated for the purpose of removing background noise from the spectrum, as follows:
  • Noise reduction is then performed to obtain a series of modified spectrum, SMBS , SMBSJ, , SMBS_N, corresponding to each signal segment, as follows:
  • the series of modified spectrum data SMBSJ , SMBSJ, . . . , SMBS_N is then inputted into a frequency band detector of the processor in order to identify or designate individual bowel sounds.
  • active noise cancellation could be performed prior to identification of bowel sounds.
  • the main frequency component of all of the bowel sounds was between 200 and 2000 Flz with a relatively narrow bandwidth.
  • other contaminating noise factors may be present in the spectrum data, such as friction against the sensors, lung sounds, and the heart beating, etc. Flowever, it was determined that the frequency spectra of these noises did not overlap with the bowel sounds.
  • a plurality of specific frequency band subsets is selected.
  • the following five frequency bands were used: 200 to 800 Flz, 600 to 1000 Flz, 800 to 1200 Flz, 1000 to 1600 Flz, and 1600 to 2000 Hz.
  • Identification of individual bowel sounds is then performed at step 48 using the ratio of energy that a particular abdominal signal or signal portion has within a specific frequency band over the full range of frequencies present in the recording, herein referred to as the band energy ratio (BER).
  • BER band energy ratio
  • a frequency band detector within the processor of the recording unit 22 calculates the BER that a signal segment has within specific frequency bands over the full range of frequencies in accordance with equation 4 as follows:
  • the abdominal signal segment is recognised as a bowel sound section.
  • the frequency band detector recognises no other bowel sound section within a range of 100 ms either side of the recognised bowel sound section, the frequency band detector defines the bowel sound section as an individual bowel sound.
  • the frequency band detector groups the bowel sound sections and defines the grouping as a single bowel sound with multiple components.
  • Each respective individual identified bowel sound is then characterized by identifying at step 50 a plurality of different parameters characteristic of the respective bowel sound signal.
  • the different parameters may include some time domain parameters such as, however not limited to, burst (such as burst amount or burst ratio), duration, contraction interval time, energy, dynamic range, higher order zero crossing, amplitude, and envelope crest factor, and frequency domain features such as, however not limited to, band energy ratio, which is actually used for the bowel sound identification step 48, spectral centroid, frequency centroid, and spectral bandwidth.
  • time domain parameters may include the following:
  • Figure 5 illustrates a section 58 of a bowel sound signal 60 that constitutes a“burst”, as well as a section 62 of the signal 60 that does not constitute a burst.
  • Duration The duration of a bowel sound signal.
  • Figure 5 illustrates duration“D” of an individual bowel sound signal.
  • PI corresponds to a pressure index characteristic of a muscle contraction or burst
  • fi wc is the frequency of an independent wave component or sine wave characteristic of a burst
  • E and b are indices of the envelope outlining the sine wave characteristic of the burst
  • n(t) is the background noise
  • Envelope Crest Factor The ratio of the peak value to the mean value to show how extreme the peaks are in a waveform.
  • Figure 6 shows a maximum hoc n (item 64) and a minimum hoc n (item 66) in two different graphs.
  • Each hoc n can be determined according to Equation 9 as follows:
  • the median FiOC n of all hoc n parameter values identified from the plurality of bowel sound signals for a participant can then be determined by the Equation 10, as follows.
  • the frequency domain parameters may include the following:
  • amplitude are 1 and 2, respectively.
  • Spectral Band Width The wavelength interval in which a radiated spectral quantity is not less than half its maximum value.
  • SBW1 and SBW2 are two different types of the band width according to Equation 13,
  • Modified Spectral Band Width The spectral band width but with a modification to include a power of 2, as shown in Equation 12 below, using Equation 14 to determine the frequency centroid (FC), i.e. a centre of the frequency.
  • each respective identified individual bowel sound was then associated at step 52 with a respective one of the four sensors V1 to V4 positioned at the different abdominal sub-regions or quadrants P1 to P4.
  • the association of each individual bowel sound with a particular abdominal quadrant was done using the amplitude A of the bowel sound according to equation 8, and on the assumption that the sensitivity of each the sensors was identical.
  • bowel sounds that were detected by multiple sensors V1 to V 4 were assigned to the quadrant/sensor that was most strongly associated with the bowel sound.
  • each bowel sound was associated with the sensor V1 to V4 that produced the highest amplitude reading for that bowel sound.
  • each bowel sound would only be associated with one sensor/quadrant. Additionally, a minimum threshold of 60% of maximum energy was applied. Therefore, if for example a bowel sound originated from a relatively central region, it would only be assigned to a quadrant if that quadrant obtained a reading of the bowel sound that exceeded the threshold.
  • the median of higher order zero crossing parameter values hoc n determined for each respective identified individual bowel sound is a property that can provide an indication of the existence or non-existence of Gl symptoms including, however not limited to, diarrhea, flatulence, burp, bloating, and cramp.
  • the hoc n parameter value was thus determined according to Equation 10 above and extracted as at step 54 of flow chart 40.
  • the median FIOC n of all respective hoc n parameter values determined from the plurality of bowel sounds identified for each respective abdominal sub- region of the subject can then be determined using Equation 1 1 above.
  • a recording of the bowel sounds of a subject was first performed for two periods of 40 minutes and 120 minutes, respectively, when the subject experienced no Gl symptoms.
  • Reference hoco, hoci , hoc2, and hoc3 parameter values were determined and extracted in real-time for each of the periods of 40 minutes and 120 minutes and for each respective identified individual bowel sound associated with the respective abdominal sub- regions or quadrants P1 to P4.
  • respective reference median sub property values HOCo, HOCi , HOC2, and HOC3 were calculated, each of the reference median sub-property values HOCo, HOC1 , HOC2, and HOC3 being calculated as a median of the respective reference hoco, hoci , hoc2, and hoc3 parameter values determined over a forward sliding window of 2 minutes.
  • respective reference sub-property values HOCo, HOC1 , HOC2, and HOCs were obtained for each minute over the respective durations of the 40-minute and 120- minute recordings.
  • the reference parameter and property values will be referred to in the following as hoc , and HOCi where / equals to 0, 1 , 2, or 3 to represent different order of differentiation.
  • a recording of the subject’s bowel sounds was then performed for a given period around a time when the subject experiences Gl symptoms. The time duration of this recording varied from subject to subject and was typically between about 10 minutes and about 20 minutes and could also be longer than 20 minutes.
  • Respective hoco, hoci, hoc2, and hoc3 parameter values were determined and extracted in real-time for the period of the recording for each respective individual bowel sound identified and associated with each of the abdominal sub- regions or quadrants P1 to P4.
  • Respective HOC n values are then compared to corresponding reference HOC , values wherein a determined change in any of the respective HOC n values based on the comparison is indicative of the existence of at least one symptom of the IBS condition.
  • a respective HOC n property value of a given same order of differentiation determined in association with different abdominal sub-regions can assist in differentiating between types of Gl symptoms experienced by the subject.
  • the inventors have identified that major indicators of different Gl symptoms including diarrhea, flatulence, burp, bloating, and cramp may include HOCo and HOC 2 property values from the upper right quadrant P1 , HOC 1 , HOC 2 , and HOC 3 property values from the lower right quadrant P2, and HOCo, HOC 2 , and HOC 3 property values from the lower left quadrant P4.
  • data from six subject participants were collected from recordings during which the subject participants experienced Gl symptoms, and from 40-minute and 120- minute recordings when the subject participants did not experience Gl symptoms.
  • the participants typically wore the belt for a total period that varied between about 10 minutes and about 20 minutes. Together the six subject participants experienced about 29 individual symptom events during the combined six recordings.
  • FIG. 7 there are shown multiple plots of respective collections of HOC2 values obtained as a function of time for respective individual bowel sounds identified and associated with the lower right quadrant P2 from three out of the six different participants.
  • the stars represent HOC2 values obtained from the recording during which the participants experienced Gl symptoms, specifically around and at the time when each of the participants experienced the Gl symptom of diarrhea, and the solid and dashed curves represent reference baseline HOC2 values obtained from the respective 40-minute and 120-minute recordings when the participants did not experience Gl symptoms.
  • the bottom x axis represents the time in minutes for the collections of HOC2 values obtained from the 40- minute and 120-minute recordings with no Gl symptoms while the top x axis indicates a time difference to the time of occurrence of the respective Gl symptom, which occurs at time 0 relative to the total period of recording during which the Gl symptom was experienced.
  • the solid curve (Phase 1 ) corresponds to HOC2 property values obtained using a subject’s abdominal signal recorded directly following either an overnight fasting or a two- hour fasting state
  • the dashed curve (Phase 2) corresponds to HOC2 property values obtained using the subject’s abdominal signal recorded for a period of about 40 minutes directly after eating a standard meal.
  • HOC2 values determined around and at a period when the participants experienced the Gl symptom of diarrhea are completely different and do not overlap with the HOC2 reference values determined when the participants did not experience G I or IBS symptoms. It is further observed from the HOC2 values determined around and at the time when the participants experienced the Gl symptom of diarrhea that a substantial change in the HOC2 values occurs when the respective subject or participant actually experiences the Gl symptom of diarrhea.
  • the HOC2 property value at the lower right quadrant P2 is consequently useful in indicating the existence of diarrhea in a patient or subject and may be particularly useful for monitoring the occurrence of the symptom of diarrhea in subjects having, for example, a Gl condition such as IBS.
  • FIG 8 there are shown multiple plots of respective collections of HOC 1 values (Figure 8(a)) and of respective collections of HOC 3 values ( Figure 8(b)) obtained as a function of time for respective individual bowel sounds identified and associated with the lower right quadrant P2 from two out of the six different participants.
  • the stars represent respective HOC 1 values and HOC 3 values obtained from the recording during which the participants experienced Gl symptoms, specifically around and at a time when each of the participants experienced the Gl symptom of flatulence
  • the solid and dashed curves represent respective reference baseline HOCi values and HOC 3 values obtained from the respective 40-minute and 120-minute recordings when the participants did not experience Gl symptoms.
  • the bottom x axis represents the time in minutes for the collections of HOC 1 values and HOC 3 values obtained from the 40-minute and 120-minute recordings with no Gl symptoms while the top x axis indicates a time difference to the time of occurrence of the respective Gl symptom, which occurs at time 0 relative to the total period of recording during which the Gl symptom was experienced.
  • the solid curves (Phase 1 ) correspond to HOC 1 property values and HOC 3 property values obtained using a subject’s abdominal signal recorded directly following either an overnight fasting or a two-hour fasting state
  • the dashed curves (Phase 2) correspond to HOC 1 property values and HOC 3 property values obtained using the subject’s abdominal signal recorded for a period of about 40 minutes directly after eating a standard meal. It is observed that the HOC 1 and HOC 3 values determined around and at a period when the participants experienced flatulence in most cases are completely different and do not overlap with the respective HOC 1 and HOC 3 reference values determined when the participants did not experience Gl symptoms.
  • HOC 1 and HOC 3 properties values determined at the lower right quadrant P2 are consequently useful in indicating the existence or non-existence of flatulence in a patient or subject and may be particularly useful for monitoring the occurrence of the symptom of flatulence in subjects having, for example, a Gl condition such as IBS.
  • FIG. 9 there are shown multiple plots of respective collections of HOC 1 values ( Figure 9(a)) and of respective collections of HOC 3 values ( Figure 9(b)) obtained as a function of time for respective individual bowel sounds identified and associated with the lower right quadrant P2 from four out of the six different participants.
  • the stars represent HOC 1 values and HOC 3 values obtained from the recording during which the participants experienced Gl symptoms, specifically around and at a time when each of the participants experienced the Gl symptom of burp
  • the solid and dashed curves represent respective reference baseline HOC 1 values and HOC 3 values obtained from the respective 40-minute and 120-minute recordings when the participants did not experience Gl symptoms.
  • the bottom x axis represents the time in minutes for the collections of HOC 1 values and HOC 3 values obtained from the 40-minute and 120-minute recordings with no Gl symptoms while the top x axis indicates a time difference to the time of occurrence of the respective Gl symptom, which occurs at time 0 relative to the total period of recording during which the Gl symptom was experienced.
  • the solid curves (Phase 1 ) correspond to HOCi property values and HOC 3 property values obtained using a subject’s abdominal signal recorded directly following either an overnight fasting or a two-hour fasting state
  • the dashed curves (Phase 2) correspond to HOC 1 property values and HOC 3 property values obtained using the subject’s abdominal signal recorded for a period of about 40 minutes directly after eating a standard meal. It is observed that the HOC 1 and HOC 3 values determined when the participants experienced burp in most cases are completely different and do not overlap with the respective reference HOC 1 and HOC 3 values determined when the participants did not experience Gl symptoms.
  • HOC 1 and HOC 3 properties values determined at the lower right quadrant P2 are consequently useful in indicating the existence or non-existence of burp in a patient or subject and may be particularly useful for monitoring the occurrence of the symptom of burp in subjects having, for example, a Gl condition such as IBS.
  • FIGS. 10(a) - 10(c) there are shown multiple plots of respective collections of HOC 2 values ( Figure 10(a)), respective collections of HOCo values ( Figure 10(b)), and respective collections of HOC 3 values ( Figure 10(c)) obtained as a function of time for respective individual bowel sounds identified and associated with the lower left quadrant P4 from two out of the six different participants.
  • the stars represent HOC 2 values, HOCo values and HOC 3 values obtained from the recording during which the participants experienced Gl symptoms, specifically around and at a time when each of the participants experienced the Gl symptom of cramp, and the solid and dashed curves represent respective reference baseline HOC 2 values, HOCo values and HOC 3 values obtained from the respective 40- minute and 120-minute recordings when the participants did not experience Gl symptoms.
  • the bottom x axis represents the time in minutes for the collections of HOC 2 values, HOCo values and HOC 3 values obtained from the 40-minute and 120-minute recordings with no Gl symptoms while the top x axis indicates a time difference to the time of occurrence of the respective Gl symptom, which occurs at time 0 relative to the total period of recording during which the Gl symptom was experienced .
  • the solid curves (Phase 1 ) correspond to HOC 2 property values, HOCo property values, and HOC 3 property values obtained using a subject’s abdominal signal recorded directly following either an overnight fasting or a two-hour fasting state
  • the dashed lines (Phase 2) correspond to HOC 2 property values, HOCo property values, and HOC 3 property values obtained using the subject’s abdominal signal recorded for a period of about 40 minutes directly after eating a standard meal. It is observed that the HOCo, HOC 2 and HOC 3 values determined when the participants experienced cramp are completely different and do not overlap with the respective HOCo, HOC 2 and HOC 3 reference values determined when the participants did not experience Gl symptoms.
  • HOCo, HOC 2 and HOC 3 values determined around and at the time when the participants experience the Gl symptom of cramp that a substantial change in the respective HOCo, HOC 2 and HOC 3 values occurs when the respective subject or participant actually experiences cramp.
  • the HOCo, HOC 2 and HOC 3 property values determined at the lower left quadrant P4 are consequently useful in indicating the existence or non-existence of cramp in a patient or subject and may be particularly useful for monitoring the occurrence of the symptom of cramp in subjects having, for example, a Gl condition such as IBS.
  • the same property for example, HOC2, determined for collections of parameter values obtained at respective different abdominal regions, in the present example the lower right quadrant P2 and lower left quadrant P4, is capable of providing an indication of two different Gl symptoms (such as, for HOC2, diarrhea - lower right quadrant P2, and cramp - lower left quadrant P4).
  • the following further reference property values, including statistical property values, of the reference baseline parameter values are determined:
  • HOC values correspond to a median of the reference hoc, values determined during a period of 2 minutes;
  • HOC /,sfdC orresponding to a standard deviation value of the HOC, values obtained over the time period of recording a subject’s bowel sounds when no Gl symptoms are experienced. Different Gl symptoms are then identified or determined by comparing respective HOC,, values (obtained in real-time from a recording of bowel sounds of the subject experiencing Gl symptoms) to the range of reference baseline HOC, values defined between HOC, ,max and HOC/, mm-
  • Figure 1 1 shows a flow diagram illustrating results as to the determination of the existence of the symptoms of diarrhea, flatulence, burp and cramp, which may be associated with IBS in accordance with the presently described example.
  • HOC 2 _2 corresponds to the parameter HOC 2 determined at the lower right quadrant P2
  • HOCi_2 corresponds to the parameter HOC 1 determined at the lower right quadrant P2
  • HOC 2 _4 corresponds to the parameter HOC 2 determined at the lower left quadrant P4.
  • the property value HOC 2 is considered.
  • the subject is not experiencing diarrhea. However, if the determined property value HOC 2 at the lower right quadrant P2 is not within the defined range, then the subject is experiencing diarrhea.
  • the property values HOC 1 and HOC 3 determined for bowel sound signals identified and assigned to the lower right quadrant P2 are then considered for relevance in providing an indication of the existence of the Gl symptom of flatulence or burp in a subject. If the property value HOC 1 determined in association with the lower right quadrant P2 is within the range defined between corresponding reference property values HOC 7, ,TM,, and HOCi ,max (determined at the lower right quadrant P2), then the subject is neither experiencing flatulence nor burp. However, if the determined property value HOC 1 at the lower right quadrant P2 is not within the defined range, then the property value HOC 3 is considered.
  • HOC 3 is within the range defined between corresponding reference property values HOC 3,m/a and HOC 3,max (determined at the lower right quadrant P2), then the subject is experiencing burp, and if the property value HOC 3 is not within the range defined between HOC 3,m/a and HOC 3,max , then the subject is experiencing flatulence.
  • any of the property values HOCo, HOC 2 and HOC 3 determined for bowel sound signals identified and assigned to the lower left quadrant P4 may be considered for relevance in providing an indication of the existence of the Gl symptom of cramp in a subject. For example, if the property value HOC 2 determined in association with the lower left quadrant P4 is within the range defined between corresponding reference property values HOC 2,m , a and HOC 2,max (deterrnined at the lower left quadrant P4), then the subject is not experiencing cramp. However, if the determined property value HOC2 at the lower left quadrant P4 is not within the defined range, then the subject is experiencing cramp.
  • data were collected from two subject participants wearing a version of the belt with MEMS arranged for noise cancellation, for a period of time when experiencing Gl symptoms, and for two separate periods of around 20 minutes when the subject participants did not experience Gl symptoms.
  • the subject participants wore the belt for periods of between around 7 minutes and around 30 minutes for the recordings during which the subject participants experienced Gl symptoms.
  • FIGS 12(a) and 12(b) there are shown as an example only plots of, respectively, reference HOC2 values ( Figure 12(a)) and HOC2 values ( Figure 12(b)) obtained for a subject participant as a function of time during one recording for each of the situations, i.e. without symptoms (baseline) and with symptoms.
  • the reference HOC2 values and the HOC2 values were calculated in a similar manner as described above, i.e. determined at each minute of the respective recordings from reference hoc2 values and hoc2 values, however for forward sliding windows of 3 minutes.
  • the reference hoc2 values were determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when experiencing no symptom ( Figure 12(a)), and the hoc2 values were determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when
  • Figures 12(c) and 12(d) there are shown as an example only plots of, respectively, reference HOC2 values (Figure 12(c)) and HOC2 values ( Figure 12(d)), obtained for the other subject participant as a function of time during one recording for each of the situations, i.e. without symptoms (baseline) and with symptoms.
  • the reference HOC2 values and HOC2 values were calculated in a similar manner as described above, i.e.
  • the reference hoc2 values were determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when experiencing no symptom ( Figure 12(c)), and the hoc2 values were determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when
  • the stars represent HOC2 values obtained from the recordings during which the participants experienced Gl symptoms, specifically around and at the time when each of the participants experienced the Gl symptom of bloating. It is observed that the HOC2 values determined around and at a period when the participants experienced the Gl symptom of bloating are quite different and do not overlap with the reference HOC2 values determined when the participants did not experience Gl symptoms. It is further observed from the HOC2 values that a substantial change in the HOC2 values occurs when the respective subject or participant actually experiences the Gl symptom of bloating as compared to the reference HOC2 values.
  • the HOC2 property value at the lower right quadrant P2 can consequently be used for indicating the existence of bloating in a patient or subject and may be particularly useful for monitoring the occurrence of the symptom of bloating in subjects having, for example, a Gl condition such as IBS.
  • HOC, and/or HOC n values may alternatively be determined at any regular interval of the recording, such as for example at every two minutes, for respective hoc, or hoc n values determined over a longer forward sliding window, such as a forward sliding window of 5 minutes, 7 minutes or 10 minutes, or for a shorter period or any suitable period.
  • a sum of the burst values is a property that can provide an indication of the existence or non-existence of the Gl symptom of constipation.
  • a burst value corresponds to the number of bursts or bowel sound sections determined to be present within an identified individual bowel sound, and the sum of burst values corresponds to the sum of all burst values determined for respective individual bowel sounds identified over a given period of time of a recording of the bowel sounds in the patient.
  • the sum of burst values will also be referred to below as burst index or burst index property value.
  • the burst value N was determined for each respective bowel sound identified from the recording of the patient’s bowel sounds, and for each abdominal sub-region.
  • the sum of the burst values or burst index property value, determined for the plurality of bowel sounds identified for each respective abdominal sub-region of the subject can then be determined.
  • reference burst values were determined and extracted in real-time for each respective identified individual bowel sound associated with the respective abdominal sub-regions or quadrants P1 to P4.
  • reference burst index property values were then calculated, each of the reference burst index property values being calculated as a sum of the reference burst values determined over a forward sliding window of 3 minutes.
  • reference sub-property values or burst index property values were obtained for each minute over the duration of the 40-minute recording.
  • burst values were determined and extracted in real-time for the given period of the recording for each respective individual bowel sound identified and associated with each of the abdominal sub-regions or quadrants P1 to P4.
  • burst index property values were calculated, each of the burst index property values being calculated, in a specific example, as a sum of the burst values determined over a forward sliding window of 3 minutes.
  • burst index property values were obtained for each minute of the period of time of the recording.
  • Data from one subject participant were collected from a 40-minute recording when the subject participant did not experience Gl symptoms recordings, and from two periods of time during which the subject participant experienced Gl symptoms.
  • the participant wore the belt for two periods of about 35 minutes during which the subject participant experienced Gl symptoms.
  • Figures 13(a) and 13(b) show plots of reference burst index values and burst index values, respectively, obtained for the subject as a function of time, wherein the reference burst index values and burst index values were calculated, respectively, from the reference burst values determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when experiencing no symptom ( Figure 13(a)), and from the burst values determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when experiencing no symptom ( Figure 13(a)), and from the burst values determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when
  • Figures 13(c) and 13(d) illustrate another example for the same subject, wherein Figure 13(c) is the same as Figure 13(a) and is shown for comparison to Figure 13(d).
  • Figures 13(d) shows a plot of burst index values obtained for the same subject as a function of time, wherein the burst index values were calculated from the burst values determined for the individual bowel sounds identified in the abdominal sound collected from the lower right quadrant P2 of the subject when experiencing symptoms ( Figure 13(d)).
  • the stars represent the burst index values obtained from the recordings during which the participants experienced Gl symptoms, specifically around and at the time when each of the participants experienced the Gl symptom of constipation. It is observed that the burst index values determined around and at a period when the participants experienced the Gl symptom of constipation are completely different and do not overlap with the reference burst index values determined when the participants did not experience Gl symptoms. It is further observed from the burst index values that a substantial change in the burst index values occurs when the respective subject or participant experiences the Gl symptom of constipation as compared to the reference burst index values.
  • the burst index property value at the lower right quadrant P2 can consequently be useful in indicating the existence of constipation in a patient or subject and may be particularly useful for monitoring the occurrence of the symptom of constipation in subjects having, for example, a Gl condition such as IBS.
  • some subjects having IBS may always have symptoms, in which case it may be difficult or challenging to obtain a recording of bowel sounds during which the subject does not experience Gl symptoms, for example in a period of seven days during which the Gl region is monitored for the occurrence of Gl symptoms several times each day.
  • the subject may not have proceeded to obtain a recording of bowel sounds during a period when the subject did not experience Gl symptoms.
  • a reference recording also referred to as a baseline recording, would not be available and to account for the possibility of such situations, an embodiment of the present invention provides for the determination of pseudo-reference parameter values and of a pseudo-reference property of the pseudo-reference parameter values.
  • the terms“pseudo-reference” and“pseudo-baseline” may be used interchangeably.
  • the patient enters information in a diary via a desktop, a web application or a smart phone application, about an overall Gl or gut feeling indicative of an overall feeling of the Gl or gut wellness as assessed by the subject at the time of the recordings.
  • This information may then be stored in a database on a server.
  • This information may correspond to a rating between 0 and 10, with a rating of 0 being indicative of the subject feeling pretty well in respect of his or her gut, and a rating of 10 being indicative of the subject feeling relatively poor in respect of his or her gut.
  • This information is then associated to the corresponding recording of the bowel sounds and the recording associated to the “best” overall gut feeling rating is taken to correspond to the pseudo-baseline recording.
  • Pseudo-reference parameter values such as pseudo reference hoc values, and corresponding pseudo-reference property values are then determined for each of the four abdominal sub-regions based on the pseudo-reference baseline recording and used for the determination of the existence or non-existence of Gl symptoms.
  • an overall Gl index value indicative of an overall gastrointestinal wellness of the subject over the period of respective recordings can then be determined based on the property values and reference or pseudo reference property values determined from the recordings of the subject’s bowel sounds. More specifically, sub-index values are calculated for each of the four abdominal sub-regions of the subject based on (i) a statistical property of the parameter values determined in real time from a recording of the subject’s bowel sounds around and at a time when the subject experiences Gl symptoms, and (ii) a reference statistical property of the reference parameter values determined in real-time from a recording of the subject’s bowel sounds around and at a time when the subject does not experience Gl symptoms.
  • sub-index values are calculated for each of the four abdominal sub- regions of the subject based on (i) a statistical property of the hoc parameter values determined in real-time from a recording of the subject’s bowel sounds around and at a time when the subject experiences Gl symptoms, and (ii) a reference statistical property of the reference hoc parameter values determined in real-time from a recording of the subject’s bowel sounds around and at a time when the subject does not experience Gl symptoms.
  • the sub-index values, statistical property, and reference statistical property are in one example determined as follows. For each abdominal sub-region, and for each order of differentiation of the hoc, the following determinations are performed:
  • each HOC k is compared relative to the range of HOC k .
  • baseline or pseudo-baseiine.i defined between HOC k ,baseline or pseudo-baseiinej, max & nd HOC k , baseline or pseudo-baseiinej, min tO determine, for each of the HOC k j, the offset between HOC k j and the range of HOC k , baseline or pseudo-baseiinej.
  • Sub index values indicative of a gastrointestinal wellness of the subject in relation to respective abdominal sub-regions based on hoc values can then be calculated for each minute of a recording according to equation 21 below: (21 )
  • equation 21 is such that if HOC k j is bigger than HOC k , baseline or pseudo-baseiinej, ma then the offset corresponds to the difference between HOCkj value and HOC k , baseline or pseudo-baseiinej, max value, if HOC ,is smaller than HOC baseline or pseudo-baseiinej, min then the offset corresponds to the difference between HOC k j and HOC k , baseline or pseudo-ba eiinej, min, and if if HOC k is between
  • the largest value of all sub-index values determined every minute of a recording across all four channels or abdominal sub-regions corresponds to an overall index value indicative of the overall Gl wellness of the subject during a corresponding recording.
  • outlier reference HOC k .baseune or pseudo-baseiine.i val u es may be excluded from consideration, and the minimum and maximum reference HOCk, baseline or pseudo-baseiine v alues that define the range of reference HOCk, baseline or pseudo-baseiinej v alues based on which the reference median is calculated for comparison to the HOC kj value, may be adjusted to correspond to, respectively, the 2% quantile value and the 98% quantile value.
  • Figures 14(a)-14(d) shows an example of sub-index values Wl calculated for each minute of a 25-minute recording and for each channel or abdominal sub-region (channel 1 ( Figure 14(a)), channel 2 ( Figure 14(b), channel 3 ( Figure 14(c), and channel 4 ( Figure 14(d)).
  • sub-index values Wl were calculated based on the baseline recording and it can be observed that each baseline sub-index value over all baseline recordings across all channels corresponds to 0, which is coherent with equation 21.
  • sub-index values were calculated based on the recording with symptoms. A variation can be observed with elevated sub-index values that typically correspond to the occurrence of a Gl symptom. In the present illustrated example, the existence of the symptoms listed in Table 1 below was determined over the period of the recording:
  • Table 1 List of Gl symptoms determined from the identified individual bowel sounds over a
  • Figure 14(e) shows the overall index values calculated based on the sub-index values illustrated in Figures 14(a)-14(d).
  • the pseudo-baseline may be refined based on diary information regarding overall Gl feelings and overall index values determined for various recordings of the subject’s bowel sounds.
  • Quantiles that produce a maximum correlation coefficient between the overall Gl feeling and the overall index value are selected, and the selected quantiles are used to determine corresponding lower boundaries and upper boundaries of the reference HOCk,i values, for each order of differentiation of the reference HOC property and for each abdominal sub-region, to be used as defining the range of‘pseudo-baseline’ reference HOC pseudo-baseiinej va ues.
  • This range of ‘pseudo-baseline’ reference HOCk, P seudo-basenne, / values may then be used for the analysis of future recordings of the subject’s bowel sounds and determination of the existence or non existence of Gl symptoms.
  • Figure 15 is a graph 1500 of calculated overall index values versus overall Gl feeling wherein data associated with an individual are plotted. Median values obtained by determining the median of overall index values for a given same overall Gl feeling are also plotted. As can be seen from the graph 1500, no data corresponds to an overall Gl feeling of 0, which would correspond to a recording of the patient’s bowel sounds in the absence of symptoms. In the present example, an extrapolation of the median values towards an overall Gl feeling of 0 with a maximum correlation coefficient provides an overall index value of 0.01 , which is very close to 0 as would be expected for an overall index value calculated for data acquired during a baseline recording, i.e. with no symptoms.
  • the overall index value of 0.01 is then used to determine, for each order of differentiation of the reference HOC property and for each abdominal sub-region, corresponding lower boundaries and upper boundaries of the reference median HOCk,i values to be used as defining a refined range of ‘pseudo-baseline’ reference HOCk, P seudo-basenne, / values.
  • the subject or patient also enters data about the severity of the Gl symptoms experienced during the recordings.
  • a subjective feeling associated with the severity of respective symptoms is thus entered in a dataset, in addition to the data associated with the overall Gl feeling and optionally one or more symptoms experienced by the patient at the time of the recordings as entered by the patient in a further dataset.
  • the subjective feeling of symptom severity is entered in the dataset, for example via a desktop, a web application or a smart phone application, as a number on a scale from zero to ten with ten indicating a highly severe negative experience and zero indicating a highly positive experience associated with no symptom.
  • the mathematical models developed by the inventors provide equations that can be used for predicting the severity of the symptoms of flatulence, pain and bloating, respectively, based on statistical properties of parameter values determined from individual bowel sounds identified in the recorded abdominal signal. More specifically, in the present examples, using initial recordings from abdominal signals representative of abdominal sounds in a subject in combination with the subjective data indicative of a feeling of severity of symptoms as experienced by the subject, identified individual bowel sounds associated with the abdominal sub-region corresponding to the lower right quadrant P2 were processed to determine parameter values for each respective identified individual bowel sounds. Then, multi-level statistical analyses of determined parameter values characteristic of respective identified individual bowel sounds were performed. First, parameter values associated with the parameters CIT were determined from the identified individual bowel sounds using equation (6) described above.
  • CIT parameter correspond to a range of CIT parameter values associated with respective bursts in one single identified individual bowel sound
  • multiple CIT parameter values may be determined for each identified individual bowel sound, which comprises at least one burst, each CIT parameter value being associated with a single burst in the identified individual bowel sound.
  • Further corresponding PI parameter values are determined for respective bursts in identified individual bowel sounds.
  • multiple PI parameter values may be determined for each identified individual bowel sound, which comprises at least one burst, each PI parameter value being associated with a single burst in the identified individual bowel sound.
  • a first level of property values was determined for each parameter CIT and PI, the property being one of the mean and median of the range of CIT and PI parameter values, to obtain a range of first level property values for each of the CIT and PI parameters.
  • a second level of property values were determined for each of the CIT and PI parameters and for each minute of the recordings based on first property values determined over a sliding window of 3 minutes ahead of the minute. The second level property values are associated with a respective second level property of the first property values.
  • a second level property may be one of the mean, median, skew or kurtosis of the first property values for the CIT and PI parameters obtained over a sliding window of 3 minutes ahead of a minute within the recorded abdominal signal.
  • parameter values associated with the parameters E, b, N and Fiwc were extracted from each identified individual bowel sound.
  • a respective first level property of the E, b, N, Fiwc parameter values was then determined at each minute of the recordings based on respective E, b, N and Fiwc parameter values determined over a sliding window of 3 minutes ahead of the minute.
  • the first level property may be one of the mean, median, skew, kurtosis or standard deviation of the parameter values.
  • respective third level properties (for CIT and PI) and second level properties (for E, b, N, and Fiwc) associated with one of the mean, median, standard deviation, kurtosis or skew of the respective corresponding second level properties (for CIT and PI) and first level properties (for E, b, N, and Fiwc) obtained over the whole period of the recording were calculated.
  • Equation (23) is an example only of an equation derived from a mathematical model that can be used for obtaining a prediction of a severity index for the symptom of flatulence (predictfiatuience) based on statistical properties of parameter values determined from individual bowel sounds identified in the recorded abdominal signal.
  • Equation (24) is an example only of an equation derived from a mathematical model that can be used for obtaining a prediction of a severity index for the symptom of abdominal pain ( predictp ain ) based on statistical properties of parameter values determined from individual bowel sounds identified in the recorded abdominal signal.
  • Equation (25) is an example only of an equation derived from a mathematical model that can be used for obtaining a prediction of a severity index for the symptom of bloating ( predict bioating ) based on statistical properties of parameter values determined from individual bowel sounds identified in the recorded abdominal signal.
  • a collection of parameter values determined from an analysis of abdominal sounds recorded in a subject suffering with, for example, multiple IBS related symptoms, and multi level derived statistics of the collected parameter values may be used to predict the severity of the symptoms experienced by the subject, such as IBS related symptoms.
  • Equation (26) is an example only of an equation derived from a mathematical model that can be used for obtaining a prediction of a combined symptoms severity index ( predict overaii ) based on statistical properties of parameter values determined from individual bowel sounds identified in the recorded abdominal signal for various symptoms.
  • equations (23) to (25) provide respective predicted symptom severity index values and a predicted symptom severity value greater than zero is indicative of the presence of a symptom. Further, increasing severity index values indicate an increased severity for a given symptom.
  • equation (26) provide a predicted combined symptoms severity index value and a predicted combined symptoms severity value greater than zero is indicative of the presence of at least one symptom. Further, increasing combined symptoms severity index values indicate an overall increased severity for all Gl symptoms combined.
  • Figure 16 shows a graph of predicted symptom severity index values as a function of subjective severity feeling values for the symptom of flatulence and for a given subject or patient.
  • the predicted symptom severity index values were calculated using equation (23) derived from individual bowel sounds identified in recordings of abdominal signals in the patient when experiencing the symptom of flatulence.
  • the coefficients ai, a ⁇ , ae, a4, a 5 , ae, and a 7 were determined to be:
  • Figure 17 shows a graph of predicted symptom severity index values as a function of subjective severity feeling values for the symptom of pain and for a given subject or patient.
  • the predicted symptom severity index values were calculated using equation (24) derived from individual bowel sounds identified in recordings of abdominal signals in the patient when experiencing the symptom of pain.
  • the coefficients a?, a ⁇ , ae, a4, a 5 , ae, and a 7 were determined to be:
  • Figure 18 shows a graph of predicted symptom severity index values as a function of subjective severity feeling values for the symptom of bloating and for a given subject or patient.
  • the predicted symptom severity index values were calculated using equation (25) derived from individual bowel sounds identified in recordings of abdominal signals in the patient when experiencing the symptom of bloating.
  • the coefficients a ? , a 2 , a 3 , a4, a 5 , a 3 , and a 7 were determined to be:
  • Figure 19 shows a graph of predicted combined symptoms severity index values as a function of subjective overall severity feeling values for all Gl symptoms combined (i.e.
  • the predicted combined symptoms severity index values were calculated using equation (26) derived from individual bowel sounds identified in recordings of abdominal signals in the patient when experiencing various symptoms.
  • the coefficients ai, a 2 , a 3 , a 4 , a 5 , a 3 , and a 7 were determined to be:
  • a further machine learning model to may allow obtaining a prediction of a combined symptoms severity index (CSSI) using predicted symptoms severity indexes of given symptoms determined in accordance with the examples described above.
  • a prediction of a combined symptoms severity index (CSSI) can be obtained using equation (27) below.
  • CSSI m ⁇ loating + m 2 Flatulence + n 3 Pain abdominal + m 4 (27) wherein each of‘Bloating’,‘Flatulence’, and‘Pain abdominal corresponds, respectively, to the symptom severity index of the given symptom.
  • Figure 20 shows another example of a graph of predicted combined symptoms severity index values as a function of subjective overall feeling values of severity of the combined symptoms acquired for a given subject or patient.
  • the predicted combined symptoms severity index values were calculated using equation (27) derived from individual bowel sounds identified in recordings of abdominal signals in the patient when experiencing the various symptoms.
  • the coefficients were determined to be 0.176, 0.227, 0.577, and 0.629, respectively.
  • An optimum correlation between the predicted symptom severity index values and the subjective severity feeling values provides a correlation coefficient of 0.88.
  • the determination of a combined symptoms severity index may further be used as an overall indication of the overall Gl wellness or overall Gl health.
  • a composite Gl symptom index based on both the overall Gl index indicative of an overall Gl wellness of the subject and the combined symptom severity index.
  • the overall Gl index value and the combined symptom severity index value would each be weighted by 50% in the composite index.
  • All information obtained in accordance with embodiments of the present invention can then be relayed to the subject and or their care givers, online on a website, via smart phone apps or via email, or via a screen on a device associated with the sound recording equipment.
  • Gl symptoms that are diarrhea, flatulence, burp, bloating, constipation, cramp, and abdominal pain
  • the method described above may be applied to the monitoring of other Gl symptoms, such as for example vomiting, nausea or reflux.
  • the method described be applied to monitoring Gl symptoms associated with Gl conditions other than IBS and Gl functional disorders and may be applied to monitoring Gl symptoms associated with Gl organic diseases such as IBD.
  • the method described may also be applied to monitoring Gl symptoms associated with a condition other than a Gl condition, such as to individuals without a firm diagnosis, but who have food intolerances or wish to gain a general assessment of gut wellness.

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Abstract

L'invention concerne un procédé de surveillance d'une région gastro-intestinale (GI) chez un sujet, le procédé consistant à obtenir un signal abdominal représentatif d'un son abdominal comprenant une pluralité de sons intestinaux provenant d'une région abdominale du sujet, à identifier des sons intestinaux individuels dans le son abdominal, à déterminer une valeur de paramètre pour chaque son intestinal individuel identifié respectif à l'aide du signal abdominal pour générer une collection de valeurs de paramètre, chaque valeur de paramètre étant associée à un paramètre et étant caractéristique d'un son intestinal individuel identifié respectif, et à comparer une propriété de la collection de valeurs de paramètre à une propriété de référence correspondante d'une collection de valeurs de paramètre de référence associées au sujet, la propriété de la collection de valeurs de paramètre étant indicatrice de l'existence ou la non-existence d'au moins un symptôme GI.
PCT/AU2019/051368 2018-12-13 2019-12-12 Procédé de surveillance d'une région gastro-intestinale chez un sujet WO2020118372A1 (fr)

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CN117159019A (zh) * 2023-11-03 2023-12-05 首都医科大学宣武医院 一种基于肠鸣音的监测系统

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WO2010021690A2 (fr) * 2008-08-18 2010-02-25 Board Of Trustees Of Michigan State University Dispositif non invasif pour le diagnostic du reflux gastro-œsophagien
WO2016112127A1 (fr) * 2015-01-06 2016-07-14 The Regents Of The University Of California Système de surveillance physiologique des statistiques abdominales et procédés
CN107714081A (zh) * 2017-10-28 2018-02-23 深圳市前海康启源科技有限公司 用于监测胃肠声音的可穿戴设备及胃肠声音监测方法
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WO2009136930A1 (fr) * 2008-05-08 2009-11-12 Smithkline Beecham Corporation Procédé et système pour surveiller la fonction gastro-intestinale et les caractéristiques physiologiques
WO2010021690A2 (fr) * 2008-08-18 2010-02-25 Board Of Trustees Of Michigan State University Dispositif non invasif pour le diagnostic du reflux gastro-œsophagien
WO2016112127A1 (fr) * 2015-01-06 2016-07-14 The Regents Of The University Of California Système de surveillance physiologique des statistiques abdominales et procédés
CN107714081A (zh) * 2017-10-28 2018-02-23 深圳市前海康启源科技有限公司 用于监测胃肠声音的可穿戴设备及胃肠声音监测方法
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CN114515137A (zh) * 2020-11-19 2022-05-20 纬创资通股份有限公司 生理病征识别方法及生理病征感测系统
CN114515137B (zh) * 2020-11-19 2024-04-19 纬创资通股份有限公司 肠胃病征识别方法及肠胃病征感测系统
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CN117159019B (zh) * 2023-11-03 2024-01-23 首都医科大学宣武医院 一种基于肠鸣音的监测系统

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