Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
  • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0093—Detecting, measuring or recording by applying one single type of energy and measuring its conversion into another type of energy
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0093—Detecting, measuring or recording by applying one single type of energy and measuring its conversion into another type of energy
  • A61B5/0095—Detecting, measuring or recording by applying one single type of energy and measuring its conversion into another type of energy by applying light and detecting acoustic waves, i.e. photoacoustic measurements
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
  • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
  • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
  • G01N21/274—Calibration, base line adjustment, drift correction
• • 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
  • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
  • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
  • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
• • 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
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
  • A61B2560/02—Operational features
  • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
  • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
  • A61B2562/0271—Thermal or temperature sensors

Definitions

• the present invention relates to a measurement method and to a non-invasive sensor making it possible to measure one or more parameters of interest in a target medium.
• the invention relates to a non-invasive sensor based on the detection of a photothermal or photoacoustic effect, in particular configured to measure parameters in a target medium, such as a stratified and/or evolving medium.
• a measured parameter can for example be blood sugar in the skin.
• a zone of interest of a medium to be analyzed is irradiated by means of a laser beam of wavelength and modulation frequency chosen according to the parameter of interest to be measured.
• the laser beam is absorbed by the target over a depth which depends on the structure of the target.
• the absorption of light energy causes local heating of the target.
• a thermal wave with a frequency equal to the modulation frequency of the laser is generated in the target. This wave propagates into the target and in particular to the exterior surface of the target.
• the thermal wave can be directly detected and analyzed. We then speak of photothermal energy. Photoacoustic detection uses the fact that the thermal wave is associated with a pressure wave with a frequency identical to the modulation frequency.
• the pressure wave generated in the fluid external environment is detected when the thermal wave generated in the target arrives, after propagation, at the target - fluid external environment interface.
• Hu et al. have developed a generalized theory of the photoacoustic effect in a stratified material. (Hu, H., Wang, X., & Xu, X. (1999). Generalized theory of the photoacoustic effect in a multilayer material. Journal of Applied Physics, 86, 3953-3958.)
• Photoacoustic detection has many advantages compared to other detection techniques, among which we can cite the orthogonal aspect of transduction: the signal optical input of the medium to be analyzed is converted into an acoustic signal which is very specific to the phenomenon to be observed and which allows the use of inexpensive and miniaturized sensors.
• the difficulty of photoacoustic or photothermal detection comes, among other things:
• a stratified material In a stratified material, to be able to deduce from the photoacoustic or photothermal signal the concentration of a given layer of an analyte of interest, it is necessary to know all the other parameters influencing this signal. In particular, it is necessary to know the structuring of the material, that is to say the thicknesses of the different layers constituting the material, their physicochemical compositions (with the exception of the parameter of interest to be measured), as well as possibly their thermal conductivities or even the thermal resistance associated with each interface between two successive layers.
• Document EP2460470 describes a regular calibration method for a non-invasive blood glucose sensor comprising a near-infrared laser source. Note that this sensor uses a detection method other than photoacoustic or photothermal detection, since it is the fraction of the incident light wave which is transmitted or scattered by the material which is detected. The technical constraints and limitations of such a sensor, particularly in terms of energy consumption and measurement precision, are therefore not the same as those of a sensor based on photoacoustic or photothermal detection.
• an optical spectrum of the tissue to be analyzed is measured by irradiation of the tissue over a predetermined range of wavelengths, spectrum on the basis of which a calibration model is chosen for the measurements at follow.
• the optical spectrum of the tissue to be analyzed is measured again and a difference between the measured spectrum and the reference spectrum is calculated. The quality of the previously chosen calibration model is then evaluated on the basis of this difference. If this assessment is unfavorable, a new calibration model is chosen to determine the measured blood glucose value. The blood sugar value is finally determined by the sensor by substituting the absorbances measured at each wavelength of the spectrum into the calibration model.
• Adapting the calibration model over time following the method of EP2460470 makes it possible to measure blood glucose with increased precision compared to blood glucose sensors whose calibration is done only once when adapting the sensor to the patient.
• the disadvantage of the method of EP2460470 is that for each recalibration it requires the acquisition of an optical spectrum of the tissue to be analyzed over a complete range of wavelengths. This acquisition is associated with significant energy consumption.
• photoacoustic detection requires not only a choice of wavelength but also a choice of laser modulation frequency.
• the depth of penetration of the laser into the target depends on the modulation frequency of the laser. If the structure of the target (e.g. skin) changes over time, the modulation frequency to be used to measure the parameter of interest (e.g. interstitial blood glucose) also changes over time.
• the modulation frequency to be used to measure the parameter of interest e.g. interstitial blood glucose
• EP2460470 poses an a priori skin model, composed of three layers: a surface layer of 0.1 mm thick, an internal layer of 0.9 mm thick and a subcutaneous layer of 2.0 mm d 'thickness. Even if EP2460470 mentions, in paragraph 79, the possibility of using the overall thickness of the skin as a variable in the simulations, it does not demonstrate the feasibility of such an embodiment nor does it indicate whether it is possible to take into account a number of layers other than three or even a variable stratification. The calibration process of EP2460470 therefore does not seem capable of taking into account all the intrinsic variability of the stratified medium to be studied.
• the invention therefore aims to improve the precision of a non-invasive sensor in a target medium, in particular stratified and/or scalable based on photoacoustic or photothermal detection while controlling the energy consumption of this sensor or even to reduce the energy consumption of this sensor while controlling its accuracy.
• the invention relates to a method for measuring a parameter of interest in a target medium by means of a non-invasive sensor based on photoacoustic detection or photothermal detection, comprising: a) a sensor is provided comprising :
• a detection cell configured to detect an acoustic or thermal signal
• a correspondence table comprising model configurations each representative of a given state of the target medium and optimal irradiation cases each comprising a set of irradiation parameters, each model configuration being associated with a case optimal irradiation,
• the adaptation module comprising a processor adapted to implement an inverse modeling algorithm receiving as input an irradiation case comprising a set of irradiation parameters and an acoustic or thermal signal and providing as output a model configuration and a value of the parameter of interest; b) the adaptation module chooses an initial irradiation model configuration; c) the adaptation module determines in the correspondence table the optimal irradiation case for the chosen irradiation model configuration, that is to say the irradiation case which makes it possible to measure the parameter of interest with a predetermined precision and/or the quantity of measurement data allowing the lowest energy consumption; d) the light source irradiates the target medium according to the set of irradiation parameters of said optimal irradiation case; e) the detection cell detects an acoustic or thermal signal generated in response to the irradiation; f) the
• Step g) of evaluating the irradiation model configuration makes it possible to acquire the certainty that the precision of the measurement was indeed the highest possible for the specific case in view of the irradiation cases available in the table correspondence, which is therefore an advantage compared to a process in which step g) is not provided and for which we do not have this certainty;
• step g either two irradiations (step g with execution of sub-step gl, in the case of an unfavorable evaluation) and therefore including a reiteration of c), d), e) and f)).
• the second irradiation is then carried out according to the irradiation case making it possible to obtain the highest possible precision for the specific case in view of the additional information acquired thanks to the first irradiation and the irradiation cases available in the table of correspondence.
• the precision of the process is therefore known and improved compared to a process not having step g).
• the method therefore allows in the first case a validation of the chosen irradiation parameters and to acquire the certainty that the precision of the measurement is the best possible for this sensor configuration and the state of the target on the date of the measurement. taking into account the additional information acquired during the irradiation, and in the second case, an adaptation of the irradiation parameters to obtain in a second step the best possible measurement precision taking into account the additional information acquired during the first irradiation, the everything being carried out with controlled energy consumption and in particular without requiring the acquisition of complete spectra either in irradiation frequency or in modulation frequency. Note that the lookup table is essential for this process.
• gl) includes repeating g) after f).
• the irradiation is repeated as long as the irradiation parameters used are not the optimal irradiation parameters for the state of the target on the date of the measurement, and optionally as long as the number of reiterations is lower at a predetermined threshold value.
• This embodiment allows, in particular if the repetition of the irradiation is carried out at a frequency higher than the characteristic frequency of the modifications of the structure of the target, to converge step by step towards the irradiation parameters making it possible to obtain the better precision.
• the energy consumption for each irradiation being controlled due to the fact that the number of irradiation parameters is limited for each case of irradiation, even if convergence is only obtained after three, four, five or even ten irradiations. successive, the overall energy consumption can be controlled and in particular lower than that which would be necessary for the acquisition of a complete spectrum in irradiation frequency at/or in modulation frequency, and this while controlling, or even improving simultaneously the measurement accuracy.
• a correspondence table is first generated by means of a processor and a database of model configurations comprising multiplets (model configuration, irradiation case, parameter of interest) and a signal acoustic or thermal detected by the detection cell associated with each byte and this correspondence table is stored in the memory of the non-invasive sensor.
• the correspondence table is obtained in an automated manner and can, for example, be updated when the database of model configurations is enriched or must be adapted to a given patient or type of patient.
• the measurement method comprises: a processor learns at least one inverse modeling algorithm from the model configuration database and stores the at least one inverse modeling algorithm in the memory of the sensor not invasive.
• one or more inverse modeling algorithms can be learned in an automated manner, for example each adapted to a model configuration and a given irradiation case or to a group of model configurations and given irradiation cases.
• at least part of the acoustic or thermal signals detected by the detection cell associated with the bytes stored in the model configuration database are simulated, that is to say they are generated by means of a computerized simulation device.
• a correspondence table generated from such a database can therefore cover a much wider range of situations with a much finer grain than in the absence of simulation, which ultimately makes it possible to generate cases of Optimal irradiation all the better adapted in terms of consumption and/or precision to each model configuration.
• the invention further relates to a non-invasive sensor based on photoacoustic or photothermal detection configured to measure a parameter of interest in a target medium comprising:
• a detection cell configured to detect an acoustic or thermal signal
• the non-invasive sensor further comprising an adaptation module adapted to exchange information with the detection cell and the device for controlling the irradiation parameters of the light source, the adaptation module comprising an processor adapted to implement an inverse modeling algorithm receiving as input an irradiation case comprising a set of irradiation parameters and an acoustic or thermal signal and providing as output a model configuration and a value of the parameter of interest, the module d the adaptation being further configured to: i- choose an initial irradiation model configuration; ii- determine in the correspondence table an optimal irradiation case corresponding to a model irradiation configuration; iii- transmit an optimal irradiation case to the device for controlling the irradiation parameters of the light source
• the senor can measure with controlled precision and/or consumption a parameter of interest in a target environment, in particular the tissue of a living being.
• the invention finally relates to a computer program comprising instructions which cause the non-invasive sensor according to the previous embodiment to execute the steps of the method according to any of the embodiments described above.
• Figure 1 represents a typical modeling of a skin-type stratified target medium.
• Figure 2 represents the steps of an optimized measurement method in a particular embodiment of the invention, in the case where you wish to measure the interstitial blood sugar of a patient.
• Figure 3 represents the main elements of a sensor 1 according to the invention, the sensor 1 being in this case positioned in contact with the target medium 2.
• Figure 4 represents the steps implemented by the simulation module 15 to simulate a photoacoustic signal which would be detected by a given photoacoustic cell in response to the irradiation of a target stratified medium modeled using the parameters of the CMk model configuration following the irradiation parameters of the irradiation case Ij.
• Figure 5 shows how an inverse model works.
• Figures 6a, 6b and 6c represent the results of an analysis of the influence of SHAP type variables (“SHapley Additive exPlanations”) carried out on three inverse models trained respectively on:
• a first “thick stratum” group which corresponds to bilayer skin model configurations in which the thickness of the upper layer, modeling the stratum comeum, is greater than 6 p.m.;
• a second “thin stratum” group which corresponds to bilayer skin model configurations in which the thickness of the upper layer, modeling the stratum comeum, is less than 18 pm;
• the abscissa axis represents the SHAP value.
• Each point in a figure corresponds to a Shapley value for a variable and an instance.
• the position on the vertical axis is determined by the variable and on the abscissa axis by the Shapley value.
• the color intensity (in shades of gray) represents the value of the variable.
• Figure 7a represents the blood glucose value predicted by a first inverse model, trained on the first “thick stratum” group from the signals detected following irradiation following six different modulation frequencies.
• Figure 7b represents the blood sugar value predicted by a second inverse model, trained on the first “thick stratum” group from the signals detected following irradiation following the two most influential variables for this group, identified in Figure 6a.
• Figure 8a represents the blood glucose value predicted by a third inverse model, trained on the second “thin stratum” group from the signals detected following irradiation following six different modulation frequencies.
• Figure 8b represents the blood glucose value predicted by a fourth inverse model, trained on the second “thin stratum” group from the signals detected following irradiation following the two most influential variables for this “thin stratum” group, identified on Figure 6b.
• Figure 9a represents the blood glucose value predicted by a fifth inverse model, trained on all of the two groups “thin stratum” and “thick stratum” from the signals detected following irradiation following six different modulation frequencies.
• Figure 9b represents the blood glucose value predicted by a sixth inverse model, trained on all of the two groups “thin stratum” and “thick stratum” from the signals detected following irradiation according to the two most influential variables for this set of the two groups “thin stratum” and “thick stratum” identified in Figure 6c.
• the invention relates to a non-invasive sensor 1 of one or more parameters of a target medium 2, in particular of a stratified target medium 2 whose structuring may possibly evolve over time.
• the stratified target medium 2 can be, for example, a tissue of an organism human or animal, such as skin.
• parameters of interest are hereinafter called “parameters of interest”.
• a parameter of interest may in particular be a physiological parameter in the case where the stratified medium is tissue from a human being or an animal.
• the physiological parameter to measure is blood sugar, particularly interstitial blood sugar. It could also involve measuring the water content in a particular layer of the skin or the lactate concentration of a particular layer. These examples are not limiting.
• the sensor 1 can be portable and it can allow continuous monitoring of the parameter(s) of interest.
• the non-invasive sensor 1 may be based on photoacoustic detection or photothermal detection.
• the measurement method is particularly suitable for improving the precision of a non-invasive sensor based on indirect photoacoustic detection, for which the sensor detects an acoustic wave generated in a fluid medium, in particular gaseous, surrounding the target medium 2 in response to a irradiation, while controlling its energy consumption or to reduce the energy consumption of the sensor while controlling its precision.
• a non-invasive sensor based on indirect photoacoustic detection, for which the sensor detects an acoustic wave generated in a fluid medium, in particular gaseous, surrounding the target medium 2 in response to a irradiation, while controlling its energy consumption or to reduce the energy consumption of the sensor while controlling its precision.
• indirect photoacoustic detection will be described in more detail below, but the generalization to a sensor based on indirect photoacoustics or photothermal energy will be made without difficulty.
• the non-invasive sensor 1 is shown schematically in Figure 3. It includes:
• an irradiation device 11 comprising a light source 1 la, a device for modulating the intensity of this light source 11b, a control device 1 le of at least one modulation frequency at which the modulation device intensity 11b modulates the intensity of light emitted by the light source;
• At least one photoacoustic detection cell 12 detecting a signal generated in response to irradiation of a target medium 2 by the emitted light, for example directly or indirectly detecting a thermal wave which propagates in a target medium 2;
• a signal processing module 13 configured to receive and process data from at least one detection cell 12;
• the light source l ia emits a laser beam modulated in intensity at at least one particular wavelength towards the target medium 2.
• the light source IA may in particular be a light-emitting diode (LED), or a laser chip.
• the light source l ia may, in addition or alternatively, comprise a quantum cascade laser (QCL) emitting in the mid-infrared region (MIR-QCL), an ICL (“interband cascade laser”) laser, a internal or external cavity laser, a GaSb laser. These examples are not limiting.
• QCL quantum cascade laser
• MIR-QCL mid-infrared region
• ICL internal or external cavity laser
• GaSb laser GaSb laser
• the non-invasive sensor 1 may include several light sources l ia.
• the non-invasive sensor 1 also includes the circuits associated with the light source(s) 1a and at least one control device 11c configured to control the irradiation parameters of the light source (lia), in particular:
• a frequency of modulation of the intensity of a light source 1 given at a given wavelength is called fmod(X) in the remainder of the description.
• the light emitted by a light source 1 la at a given wavelength X can be characterized by this wavelength and the corresponding modulation frequency and optionally the corresponding light power and/or other parameters such as time integration or the duty cycle characterizing the laser pulses.
• the light source AI can be modulated in intensity by any known electrical or mechanical means.
• the light can be emitted by a given light source in a continuous or pulsed manner.
• the absorption of light energy causes local heating of the target medium 2. Consequently, a thermal wave of frequency equal to the modulation frequency of the light source propagates in the target medium 2 (phenomenon symbolized by arrows in dotted lines in Figure 3), in particular towards the surface of the target medium 2.
• This thermal wave gives rise in the gaseous medium outside the target to a pressure wave of the same frequency, which propagates in this gaseous medium surrounding the target medium 2 and in particular in the photoacoustic detection cell 12 (phenomenon symbolized by alternating dotted arrows in Figure 3).
• the detection cell 12 comprises, in the case of photoacoustic detection, a chamber filled with a gas (for example air) through which the acoustic wave propagates, and one or more appropriate sensors placed in this chamber , for example facing the target medium 2.
• a gas for example air
• Each electroacoustic sensor is functionally connected to the signal processing module 13.
• the signal processing module 13 may include an analog-to-digital converter configured to convert the analog electrical signal from the electroacoustic sensor into a digital signal.
• the signal processing module 13 may include a synchronous detection device adapted to demodulate and extract the signal of interest from the detected signal.
• the signal processing module 13 optionally includes an operational amplifier operably connected to the analog-to-digital converter and configured to amplify the electronic signal derived from the acoustic response of the target 2 transmitted by an electroacoustic sensor.
• the analog-to-digital converter is operably connected to a digital signal processor for processing the digital signal.
• the non-invasive sensor 1 further comprises an adaptation module 14 of the irradiation parameters and the calibration model, as well as a simulation module 15. These two elements are described in the following sections.
• the adaptation module 14 is a computerized device comprising at least one processor
• the control device(s) 1 le of at least one irradiation parameter of the light source l ia for example a modulation frequency at which the modulation device intensity 11b modulates the intensity of a light emitted by the light source 1 la at a given wavelength, the wave number (or equivalently the wavelength) of a light emitted by the light source 1 la, the light power of the light source 1 la at a given wavelength and optionally a given modulation frequency, etc.
• - target medium 2 is the skin (in this case it is therefore a stratified medium)
• the parameter of interest is interstitial blood glucose.
• the non-invasive sensor 1 further comprises a memory for storing a database of model configurations, a correspondence table and one or more inverse models which are described below.
• This storage memory can be distributed and/or shared in/with the adaptation module 14 and/or the simulation module 15.
• the adaptation module 14 carries out the steps of the method which make it possible to choose, in the database of model configurations, the model configuration of target medium 2 most adapted to the target medium 2 on the date of the measurement, to choose, on the basis of the correspondence table, the optimal irradiation parameters for the measurement and to determine the most suitable inverse model, that is to say the most precise, for the calculation of the measured parameter from the signal detected for this particular measure.
• the optimal irradiation parameters for the measurement and to determine the most suitable inverse model, that is to say the most precise, for the calculation of the measured parameter from the signal detected for this particular measure.
• the model configuration database and the correspondence table are generated from a simulation module 15, embedded in the sensor 1 or remote.
• the simulation module 15 is remote, the non-invasive sensor 1 includes communication means so that the simulation module 15 and the adaptation module 14 exchange data.
• the simulation module 15 is a computerized device configured to generate a set of model configurations corresponding (or even modeling or describing) each to a particular state of the target medium 2, a set of cases of irradiation of the target medium 2 and the signals photoacoustic (or where appropriate photothermal) theoretically detected in response to each case of irradiation for each CMk model configuration of the target medium 2 from analytical models of the target medium 2 and the photoacoustic (or where applicable photothermal) detection cell 12, as shown in Figure 4.
• This simulation module 15 is particularly relevant in the case where the target environment 2 is evolving over time and/or stratified.
• the target medium 2 adopts different real configurations over time (due to the fact that one or more concentrations vary within one or more layers of the target medium 2 and/or that one or more dimensions of the medium target vary, such as, for example, the thickness of one of the layers of the target medium 2 and/or that the number of layers of the target medium 2 varies) which can each be modeled by a particular model configuration.
• the target medium 2 to be analyzed is modeled as shown in Figure 1.
• the target medium 2 separates an external medium A from an internal medium B and is composed of a succession of N layers whose interfaces are for example assumed to be locally planar.
• Each layer i (i G [I, IV]]) is described by the parameter(s) of interest and a certain number of explicit parameters (called level 1 parameters because their values will be provided as input to the simulation 15 for each simulation) appropriate for the target environment 2 considered.
• level 1 parameters because their values will be provided as input to the simulation 15 for each simulation
• the target medium 2 is the skin and we seek to measure a glucose concentration in a layer j of this target medium 2
• the target medium 2 will be described by its number of layers N, the concentration [Glc]j of interest
• each layer i can be described for modeling by its thickness ei, its water concentration
• the list of level 1 parameters can be enriched if one wishes to carry out more precise modeling.
• the concentrations of other components of the skin such as fats, lactate, oxygen, etc., can be included in the list of level 1 parameters describing a layer of the skin.
• Non-explicit parameters of the target environment model 2 can be calculated using analytical models. For example, thermal conductivity, heat capacity, density or even the absorption coefficient at each wavelength of each layer of the target medium 2 can be deduced from the level 1 parameters and known equations.
• the parameter(s) of interest playing a particular role is (they are not) included in the list of level 1 parameters.
• this parameter(s) ( s) of interest will be (will be) known or not: its value is known (their values are known) to carry out the simulations using the simulation module 15, but it is (they are) of course unknown (s). ) in the case of a real measurement with the non-invasive sensor 1.
• the number of layers N of the target medium 2 can also be a variable of the model. Still in the example of the skin, depending on the physiological situations, N can thus be greater than or equal to one or two.
• the skin can be correctly described by two layers, the first corresponding to the stratum comeum, whose glucose concentration can for example be low, and the second to the rest of the skin, the glucose concentration of the second layer being assimilated to the interstitial glucose concentration to be measured.
• the water concentration of a layer may, for example, increase with the depth at which this layer is located.
• the number of layers N of the target medium 2 may therefore not be a constant.
• the external environment A is generally the atmosphere surrounding the patient, which also fills the photoacoustic detection cell.
• the simulation module 15 implements a multiphysics analytical stratified medium 2 model based for example on physical and/or chemical equations such as, by way of non-limiting examples, the Beer-Lambert equations for optical absorption and the equations thermodynamics of heat (Fourier's law and conservation laws).
• a model configuration CMk (k positive integer) of target medium 2 corresponds to (or even models) a particular state of the given target medium 2. This particular state is assumed to be correctly represented by the data of the number of layers N and the values of the level 1 parameters for each layer.
• the multiphysics analytical model allows, if the parameter of interest is also known, to simulate the thermal wave generated at the interface layer 1/external environment A (interface 1/A) in response to irradiation by a light source l ia whose irradiation parameters are known, namely for example the wavelength X, the modulation frequency fmod(Z) at this wavelength and the surface power density at this length wave.
• the multiphysics analytical model makes it possible to simulate the pressure wave generated in the external environment A.
• the signal obtained at the output of a processor implementing the multiphysics analytical model is called “simulated response wave”
• the simulated response wave can be provided as input to a processor implementing the detection cell model.
• the non-invasive sensor 1 based on photoacoustic or photothermal detection comprises a photoacoustic (respectively photothermal) detection cell 12 configured to detect and analyze the pressure wave (respectively the thermal wave) generated in the external environment A when the wave thermal generated in the target medium 2 in response to the irradiation reaches the interface 1/A.
• a photoacoustic (respectively photothermal) detection cell 12 configured to detect and analyze the pressure wave (respectively the thermal wave) generated in the external environment A when the wave thermal generated in the target medium 2 in response to the irradiation reaches the interface 1/A.
• the entire detection cell 12 can be modeled analytically. From a response wave simulated by the multiphysics analytical model, which would theoretically be received at the input of the detection cell 12, the detection cell model 12 makes it possible to predict the output signal of the detection cell 12.
• the parameters of the model of the detection cell 12 can include geometric parameters including in particular its dimensions (for example the size of a vent, the height of the cell, %), thermodynamic state parameters (temperature, atmospheric pressure, relative or absolute humidity, ).
• the photoacoustic detection cell 12 can in particular be modeled using an equivalent RLC circuit.
• the model described in Dehe, Alfons et al. “The Infineon Silicon MEMS Microphone.” (2013) may be suitable.
• this photoacoustic detection cell model a model of the signal processing step carried out by the signal processing module 13 if necessary, so as to generate, from each simulated response wave generated by the processor which implements the analytical multiphysics model, the signal theoretically obtained at the output of the photoacoustic detection cell (and where appropriate after processing of the signal by the signal processing module 13) which corresponds to it.
• the processor of the simulation module 15 can be configured to implement the photoacoustic detection cell model.
• the simulation module 15 therefore receives as input the parameters of the model configuration CMk of target medium 2, that is to say the number of layers N of the target medium 2 and the level 1 parameters for each layer, as well as the parameter of interest and the irradiation parameters of the irradiation case Ij, an irradiation case comprising one or more wavelengths of the lights emitted by one or more lasers, one or more respective modulation frequencies of the intensity of this laser(s), and optionally the respective powers irradiated by this laser(s).
• the simulation module 15 provides the signal theoretically expected at the output of the detection cell 12 or, where appropriate, the processed simulated signal theoretically expected at the output of the signal processing module 13 for the CMk model configuration of target medium 2 chosen, called simulated output signal.
• the simulated output signal can be stored in memory as a Fourier spectrum.
• the multiplets ⁇ CMk model configuration of target medium 2, irradiation case Ij, parameter of interest, amplitudes and phases of the components of the simulated output signal ⁇ can be stored in a database of model configurations.
• CMk model configurations each CMk model configuration corresponding to a number of layers N and a set of level 1 parameters, and optionally to a value or a range of values of the parameter of interest, describing a particular situation of the target medium 2 of interest.
• each CMk model configuration it is possible to generate, possibly in an automated and/or random manner, a large number of irradiation cases Ij, each irradiation case corresponding to a set of irradiation parameters describing the parameters of the (des) light sources 1 used for irradiation.
• a case of irradiation Ij can therefore include one or more frequencies for modulating the intensity of one or more lasers, the respective wavelength of each of these lasers and optionally the respective power irradiated by each laser.
• the simulation module 15 we calculate the amplitude and the phase of each component of the simulated output signal obtained at the output of the processor of the simulation module 15 implementing the global analytical model comprising in cascade the multiphysics analytical model and the model of detection cell for each model configuration CMk for each case of irradiation Ij, a value of each parameter of interest being additionally provided.
• the cases of irradiation Ij can be the same for several different CMk model configurations, and possibly several values of one or more parameters of interest, or different from one CMk model configuration to another and/or a value d 'one parameter of interest to another.
• model configurations CMk, cases of irradiation Ij, parameter values of interest and photoacoustic (or, where appropriate, photothermal) signals can be stored in a database of model configurations. associated simulated in the form of multiplets ⁇ model configuration CMk, irradiation case Ij, parameter of interest, amplitude and phase of the components of the simulated output signal ⁇
• CMk model configurations and/or Ij irradiation cases may not be completely random.
• CMk model configurations can, among other things, be based on physiological considerations to restrict the space of possibilities to physiologically realistic model configurations.
• a blood glucose sensor we can for example limit the possible thicknesses of the first layer of the skin to the range [8 pm, 40 pm] which is actually observed experimentally, and limit the water concentrations of this layer to a restricted range for each thickness, the water concentration of the stratum comeum being correlated with its thickness.
• irradiation cases Ij can in particular take into account the limitations of the light sources l ia available for a non-invasive sensor 1 given in wavelength and/or power, or even of the modulation frequency ranges relevant for the type of target medium 2 to analyze, or wavelengths relevant to the parameter(s) of interest.
• the database of model configurations may only include multiplets ⁇ model configuration CMk, irradiation case Ij, parameter of interest, amplitudes and phases of the signal components actually measured ⁇ obtained by experiments in a real situation or understand both such multiplets obtained in a real situation and multiplets obtained by simulation.
• an artificial intelligence model can be trained in the simulation module 15.
• the artificial intelligence model after learning, is capable of solving the inverse problem, that is to say finding the parameter(s) of interest and the CMk model configuration of target medium 2, therefore the number of layers N and the level 1 parameters, knowing the simulated photoacoustic signal and the irradiation parameters of the irradiation case Ij, as shown in Figure 5.
• the learned model which will be called the inverse model subsequently, can be transmitted to the adaptation module 14 and stored in the memory of this module.
• irradiation Ij can be associated with the same CMk model configuration, it is also possible, using statistical analysis and/or artificial intelligence techniques, to identify the case of irradiation Ij which makes it possible to measure the parameter of interest with the precision closest to the desired precision and/or the amount of measurement data allowing the lowest energy consumption.
• model configuration it is possible to work model configuration by model configuration or to categorize the model configurations and associate an optimal lopt.cat.k irradiation case with each category of model configurations.
• the database data was segmented into two groups:
• the first “thick stratum” group corresponds to model configurations of bilayer skin in which the thickness of the upper layer, modeling the stratum comeum, is greater than 18 pm;
• the second “thin stratum” group corresponds to model configurations of bilayer skin in which the thickness of the upper layer, modeling the stratum comeum, is less than 18 pm.
• the method makes it possible to reduce the energy consumption for each irradiation while controlling the precision of the measurement, thanks to an adaptation of the irradiation case to the detected model configuration.
• the mean square error going from 10.5 mg/dL to 11.2 mg/dL therefore remaining well below the target threshold of 20 mg/dL considered as the maximum acceptable RMSE in this case when selecting the variables of interest for the optimal irradiation case.
• the mean square error going from 4.9 mg/dL to 5.8 mg/dL, therefore remaining well below the target threshold of 20 mg/dL considered as the maximum acceptable RMSE in this case when selecting the variables of interest for the optimal irradiation case. Even if the relative variation of the mean square error is not negligible in this case, the absolute value of this mean square error therefore remains controlled during the selection of the optimal irradiation case.
• the threshold value for the RMSE set for this example at 20 mg/dL, is also exceeded, indicating that other influencing variables must be taken into account to respect this threshold value.
• the correspondence table may possibly include an optimal irradiation case for all groups, particularly with a view to initial irradiation.
• an optimal irradiation case for all groups particularly with a view to initial irradiation.
• the number of irradiation parameters selected results from a compromise between the power available for each irradiation and the acceptable RMSE.
• the couples ⁇ CMk; lopt.k ⁇ or ⁇ category k of model configurations, lopt.cat.k ⁇ are stored in a correspondence table in the memory of the non-invasive sensor 1, for example in a memory of the simulation module 15 and/or of the simulation module. adaptation 14.
• the inverse models corresponding to each model configuration CMk or to each category of model configuration are also stored in the memory of the non-invasive sensor 1, for example in a memory of the simulation module 15 and/or the adaptation module 14.
• the measurement method according to the invention therefore makes it possible to reduce the energy consumption of the sensor 1 compared to the methods of the prior art with identical or even improved precision.
• An initial model configuration of target medium 2 is chosen for the first irradiation on the basis of predetermined criteria.
• the Initial CMirrad model configuration is the CMk model configuration.
• the processor of the adaptation module 14 determines the irradiation case lopt.k associated with the initial model configuration, for example CMk.
• First irradiation A first irradiation of the target medium 2 is carried out with the light source(s) 1 according to the irradiation parameters of the optimal irradiation case lopt.k for the CMk model configuration. d) PA detection:
• the real photoacoustic signal generated in response to the irradiation is detected by means of the photoacoustic detection cell 12.
• the signal processing module 13 receives and processes this real photoacoustic signal and transmits it after processing to the adaptation module 14. e) Resolution of the inverse problem:
• the processor of the adaptation module 14 implementing the learned inverse model(s) receives as input the real photoacoustic signal and the irradiation case lopt.k and determines the current target medium model configuration 2, denoted CMmes as well as the parameter of interest (as shown in Figure 5). f) Validation of the model configuration:
• the adaptation module 14 compares the current target medium model configuration 2 CMmes to the CMirrad model configuration used for irradiation (CMk model configuration resulting from the initialization step for the first measurement, possibly different CMC model configuration for a measurement later). Two scenarios are then possible:
• Case 1) If the model configuration of target medium 2 measured CMmes is identical to the model configuration CMirrad which was used for irradiation, the irradiation parameters chosen were optimal for the current physiological situation (which we recall n is not known a priori and evolves over time), as does the inverse model used to determine the parameter of interest. Consequently, the parameter of interest determined in step f by the adaptation module 14 is the result of the measurement.
• the processor of the adaptation module 14 searches in the correspondence table for the optimal irradiation case lopt.f for the CMf model configuration of the target medium 2 and transmits the corresponding parameters to the irradiation device 11.
• the target medium 2 then undergoes a new irradiation (subsequent irradiation) according to the irradiation parameters of the lopt.f irradiation case.
• steps d) of detection and, where appropriate, processing of the photoacoustic signal and e) of solving the inverse problem are repeated.
• the method may include only one step f) of validation of the model configuration.
• the method may also include a reiteration of step f) of validating the model configuration.
• the current CMmes target medium 2 model configuration may be different from the CMirrad model configuration. Indeed, CMirrad was chosen
• CMirrad is then the most suitable model configuration for the patient knowing the result of the measurement following the previous irradiation.
• CMmes is different from CMirrad
• the adaptation of the irradiation parameters and the model configuration makes it possible to increase the precision of the measurement at the cost of at least one additional irradiation but with energy consumption always controlled, and thanks to the second validation step, to confirm that the precision of the measurement is indeed maximum.
• we authorize the reiteration of the validation step we acquire at the output of the process the additional information that the precision of the measurement is indeed maximum.
• the measurement result of the parameter of interest is obtained after the first irradiation or after two irradiations.
• a limitation of the number of validation steps of the model configuration can be provided.
• the physiological situation corresponds to 3 layers each characterized by 2 concentrations (for example water and blood sugar)
• Knowledge of the amplitude and phase of each of the three components of the photoacoustic signal corresponding to well-chosen modulation frequencies and wavelengths should allow the resolution of the inverse problem with the desired precision.
• the difficulty lies in the optimal choice of these modulation frequencies and optimal wavelengths, a difficulty which is resolved by means of the correspondence table.
• the consumption of sensor 1 according to the invention is therefore limited or controlled.
• each irradiation step requires limited power, less than that necessary to obtain a complete absorption spectrum for each possible modulation frequency.
• the model configuration validation step allows:
• the artificial intelligence model of the simulation device 15 can be pretrained on the database of simulated model configurations then retrained on a database of experimental model configurations, corresponding to real situations. for a given patient or a group of given patients, so as to ensure that the inverse model correctly predicts real situations without this requiring an exhaustive experimental data set.
• the learning of the inverse models may include a transfer learning step.
• light source 11b device for modulating the intensity of the light source 1 la
• 11c device for controlling the finod modulation frequency of the intensity of the light source l ia

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