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
  • A61B5/14532—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
  • A61B5/0093—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
  • A61B5/14507—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 specially adapted for measuring characteristics of body fluids other than blood
  • A61B5/1451—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 specially adapted for measuring characteristics of body fluids other than blood for interstitial fluid
• • 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
  • A61B5/72—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
  • G01N21/1702—Systems in which incident light is modified in accordance with the properties of the material investigated with opto-acoustic detection, e.g. for gases or analysing solids
• • 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
  • G01N21/171—Systems in which incident light is modified in accordance with the properties of the material investigated with calorimetric detection, e.g. with thermal lens detection
• • 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
• • 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
  • G01N21/1702—Systems in which incident light is modified in accordance with the properties of the material investigated with opto-acoustic detection, e.g. for gases or analysing solids
  • G01N2021/1706—Systems in which incident light is modified in accordance with the properties of the material investigated with opto-acoustic detection, e.g. for gases or analysing solids in solids
• • 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
  • G01N21/1717—Systems in which incident light is modified in accordance with the properties of the material investigated with a modulation of one or more physical properties of the sample during the optical investigation, e.g. electro-reflectance
  • G01N2021/1731—Temperature modulation

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 one or more parameters in a stratified target medium, the stratification of which can evolve over time.
• 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 characteristic length which depends on the structuring 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 optical signal at the input of the medium to be analyzed is converted into an acoustic signal which is very specific to the phenomenon. to observe and which allows the use of inexpensive and miniaturized sensors.
• the difficulty of photoacoustic or photothermal detection comes, among other things:
• Photoacoustic or photothermal detection requires not only a choice of wavelength but also a choice of the modulation frequency(ies) of the laser to use. Indeed, the characteristic length of penetration of the incident light wave 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(s) to use to measure the parameter of interest (e.g. interstitial blood glucose) with a precision and controlled consumption also change over time.
• the modulation frequency(s) to use to measure the parameter of interest e.g. interstitial blood glucose
• 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 by taking into account interference phenomena while controlling consumption. energy of this sensor or to reduce the energy consumption of this sensor while controlling its precision.
• the invention relates to a method for measuring a parameter of interest in a target stratified medium by means of a non-invasive sensor based on photoacoustic detection or photothermal detection.
• the measurement method comprises: a) a sensor is provided comprising:
• a device for controlling a plurality of irradiation parameters of the light source the plurality of irradiation parameters comprising at least one frequency of modulation of the intensity of the light source
• a detection cell configured to detect an acoustic or thermal signal (12)
• each irradiation case comprising a set of values of irradiation parameters, a plurality of nodal bytes, each nodal byte comprising characteristics common to all the elements of a given group of model configurations of the target stratified medium and a plurality of associated nodal modulation frequencies, for which the acoustic signal or thermal detected by the detection cell in response to irradiation by the light source has a correlation lower than a predetermined threshold with the parameter of interest;
• an adaptation module comprising a processor and exchanging information with the detection cell and the device for controlling the irradiation parameters of the light source; b) the adaptation module chooses an initial model configuration of the target stratified medium (CMirrad) for irradiation; c) the processor of the adaptation module:
• the light source irradiates the target stratified medium according to the set of irradiation parameters of said particular irradiation case; e) the detection cell detects an acoustic or thermal signal generated in response to the irradiation; f) the processor of the adaptation module determines the parameter of interest on the basis of the detected acoustic or thermal signal.
• the irradiation is carried out according to a case of irradiation comprising a modulation frequency of at least one particular light source, particular in the sense that it was chosen to take into account the nodal frequencies corresponding to the model configuration of the target stratified medium retained for irradiation.
• the particular modulation frequency can be determined on the basis of two nodal frequencies, such that the particular modulation frequency corresponds to a maximum sensitivity of the sensor (all other things being equal) for the interval between these two modulation frequencies.
• the particular modulation frequency is a weighted average of the nodal frequencies, and delimited by them, makes it possible to take into account the fact that the modulation frequency corresponding to the maximum sensitivity with respect to the parameter of interest is found in the interval separating two successive nodal modulation frequencies but that the characteristic depth of penetration of the incident light wave decreases with the modulation frequency.
• the processor of the adaptation module further implements an inverse modeling algorithm receiving as input a case of irradiation and an acoustic or thermal signal and providing as output a model configuration of the target stratified medium and a value of the parameter of interest,
• step f) comprises the following steps: fl) the processor of the adaptation module receives as input the acoustic or thermal signal detected by the detection cell and the particular irradiation case used for the irradiation, and returns at the output of the inverse modeling algorithm a model configuration of the current target stratified medium and an estimated value of the parameter of interest; f2) the processor of the adaptation module evaluates the model configuration of the target stratified medium chosen by comparison with the model configuration of the current target stratified medium, and only if this evaluation is unfavorable: g) the adaptation module receives as input the model configuration of the current target stratified medium and returns as output a new model configuration of the target stratified medium chosen for irradiation, then c), d), e) and fl) are repeated; f3) the value of the parameter of interest measured by the sensor is the last estimated value of the parameter of interest.
• the evaluation of the model configuration of the chosen target stratified medium CMirrad is judged unfavorable when the model configuration of the chosen target stratified medium CMirrad is different from the model configuration of the current target stratified medium CMmes.
• Step f2 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 f2 either two irradiations (step f2) with execution of sub-step g), in the case of an unfavorable evaluation) and therefore including a reiteration of c), d), e) and fl)).
• 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 chosen irradiation case take into account the phenomena of interference for the model configuration chosen for the first irradiation but also the optimal character of the model configuration in relation to the current state of the stratified medium is verified, and if necessary another model configuration and another case of irradiation taking into account interference phenomena are chosen for a second irradiation on the basis of which the parameter of interest will be determined.
• the precision and/or sensitivity and/or energy consumption of the non-invasive sensor can thus be further improved continuously, as the target stratified medium evolves.
• gl) further comprises f2) after fl).
• 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 and that these irradiation parameters take into account interference phenomena, even if the convergence n is obtained that after three, four, five or even ten successive irradiations, 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 and/or frequency modulation, while simultaneously controlling or even improving the precision of the measurement.
• the method first comprises: I- by means of a processor and a database of model configurations comprising multiplets (model configuration of the target stratified medium, case of irradiation, parameter of interest) and an acoustic or thermal signal detected by the detection cell associated with each of said multiplets, a chart of nodal frequencies is generated for:
• the nodal frequency chart comprising a plurality of nodal multiplets, each nodal multiplet comprising characteristics common to all the elements of a group of model configurations of the target stratified medium and a plurality of associated nodal modulation frequencies, and this chart of nodal frequencies is stored in the memory of the non-invasive sensor. Thanks to this arrangement, we can generate a chart of nodal frequencies by simulation, therefore in silico, in a much shorter time and covering a much more complete range of groups of model configurations and irradiation cases than if the chart had been generated manually. This arrangement therefore makes it possible to further improve the precision of the sensor. It also makes it possible to reduce the cost and manufacturing time of the non-invasive sensor.
• the measurement method comprises:
• a processor learns at least one inverse modeling algorithm from the model configuration database and the at least one inverse modeling algorithm is stored in the memory of the non-invasive sensor.
• one or more inverse modeling algorithms can be trained 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, this which makes it possible to gain precision in the step of solving the inverse problem necessary to determine a value of the parameter of interest from the photoacoustic or photothermal signal detected.
• At least part of the acoustic or thermal signals detected by the detection cell associated with the multiplets stored in the model configuration database are simulated, that is to say they are generated using a computerized simulation device.
• the particular irradiation case for the chosen model configuration determined by the adaptation module in step c) further comprises at least one nodal modulation frequency.
• This arrangement makes it possible to collect, in addition to information on the parameter of interest, information on the configuration of the target stratified medium at the time of the measurement independently of the parameter of interest.
• nodal modulation frequencies make it possible to obtain information on characteristics of the target medium other than the parameter of interest with improved precision.
• the invention also relates to a non-invasive sensor based on photoacoustic or photothermal detection configured to measure a parameter of interest in a target stratified medium comprising:
• a source of light - a device for controlling the irradiation parameters of the light source
• a detection cell configured to detect an acoustic or thermal signal
• the non-invasive sensor further comprising an adaptation module comprising a processor and adapted to exchange information with the detection cell and the device for controlling the irradiation parameters of the light source, the adaptation module being further configured to:
• the processor of the adaptation module is more suitable for: - 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 of the target stratified medium and a value of the parameter of interest;
• the device for controlling the irradiation parameters of the light source so that the light source irradiates the target stratified medium according to the set of irradiation parameters of said new irradiation case in particular, that the detection cell detects an acoustic or thermal signal generated in response to this new irradiation and the processor of the adaptation module determines the parameter of interest on the basis of the new acoustic or thermal signal detected;
• the non-invasive sensor at least two of the elements among the light source, the device for controlling the irradiation parameters of the light source, the detection cell configured to detect an acoustic or thermal signal, the memory in which a chart of nodal frequencies is stored and the adaptation module are mechanically independent of each other.
• This embodiment makes it possible to form a distributed sensor, such that the bulk or weight is reduced at the target stratified medium.
• the simulation module can in particular be deported for remote management and possibly shared between several users.
• the invention finally relates to a computer program comprising instructions which cause the non-invasive sensor according to one of the preceding embodiments to execute the steps of the measurement method according to one of the embodiments described above.
• Figure 1 represents the main elements of a non-invasive sensor 1 according to the invention, the non-invasive sensor 1 being in this case positioned in contact with the target stratified medium 2.
• Figure 2 represents in a simplified manner the different thermal waves transmitted and reflected images generated in a bilayer stratified medium in response to laser irradiation.
• Figures 3a and 3b represent the amplitude of the thermal wave generated in response to irradiation as a function of the modulation frequency for varied glucose concentrations, for two different target stratified media.
• Figure 4 represents a typical modeling of a skin-type stratified target medium.
• Figure 5 represents the steps of a measurement method according to the invention in a particular embodiment, in the case where you wish to measure the interstitial blood sugar of a patient.
• Figure 6 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 7 shows how an inverse model works.
• Figure 8a represents the values predicted by the non-invasive sensor as a function of the actual values, in the case where the target stratified medium is the skin of a patient, modeled by a bilayer model (stratum comeum, epidermis).
• the thickness of the upper layer (modeling the stratum comeum) is equal to 14 pm and the thickness of the lower layer (modeling the epidermis) is considered infinite.
• Blood sugar is zero in the upper layer.
• the upper layer comprises 20% water and the lower layer comprises 60% water.
• Figure 8b represents the values predicted by the non-invasive sensor as a function of the actual values, in the case where the target stratified medium is the skin of a patient, modeled by a bilayer model (stratum comeum, epidermis).
• the thickness of the upper layer (modeling the stratum comeum) is equal to 18 pm and the thickness of the lower layer (modeling the epidermis) is considered infinite.
• Blood sugar is zero in the upper layer.
• the upper layer comprises 20% water and the lower layer comprises 60% water.
• Figure 8c represents the values predicted by the non-invasive sensor as a function of the actual values, in the case where the target stratified medium is the skin of a patient, modeled by a bilayer model (stratum comeum, epidermis).
• the thickness of the upper layer (modeling the stratum comeum) is equal to 18 pm and the thickness of the lower layer (modeling the epidermis) is considered infinite.
• Blood sugar is zero in the upper layer.
• the invention relates to a non-invasive sensor 1 of one or more parameters of a target stratified medium 2, the structuring of which can possibly evolve over time.
• the target stratified medium 2 may be, for example, a tissue of a human or animal organism, such as the skin.
• the parameters to be measured 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 non-invasive 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 non-invasive sensor 1 detects an acoustic wave generated in the fluid surrounding the target stratified medium 2 in response to irradiation , while controlling its energy consumption or to reduce the energy consumption of the sensor while controlling its precision.
• this process it is entirely possible to implement this process on a non-invasive sensor based on photothermal energy.
• the example of indirect photoacoustic detection will be described in more detail below, but the generalization to a sensor based on photothermal energy will be made without difficulty.
• the non-invasive sensor 1 is shown schematically in Figure 1. 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 l ia;
• At least one photoacoustic detection cell 12 detecting a signal generated in response to irradiation of a target stratified medium 2 by the emitted light, for example directly or indirectly detecting a thermal wave which propagates in a target stratified medium 2;
• a signal processing module 13 configured to receive and process data from the at least one detection cell 12;
• a simulation module 15 remote or on-board with the other elements of the non-invasive sensor 1.
• the light source l ia emits an intensity-modulated laser beam at at least one particular wavelength towards the target stratified medium 2.
• the at least one wavelength can be chosen according to one of the parameters of interest.
• the wave number 1034 cm 1 corresponding to a glucose absorption peak, could be relevant if the non-invasive sensor 1 is a blood glucose sensor.
• the light source l ia may in particular be a light-emitting diode (LED) or a laser chip.
• the light source l ia may, in addition or as a variant, comprise a quantum cascade laser (QCL) emitting in the mid-infrared region (MIR-QCL), an ICL laser (“Interband Cavity Laser”), a internal or external cavity laser, a GaSb laser.
• QCL quantum cascade laser
• MIR-QCL mid-infrared region
• ICL laser Interband Cavity Laser
• the light source l ia can be chosen according to the target stratified medium 2 and/or the parameters of interest.
• the non-invasive sensor 1 may include several different and/or identical light sources l ia.
• the non-invasive sensor 1 also comprises the circuits associated with the light source(s) 1 and at least one control device 1 configured to control the frequency at which at least one intensity modulation device 11b modulates the intensity of at least one light source 1 la, so that the intensity of the light emitted by the light source l ia is modulated at an adjustable modulation frequency.
• the frequency of modulation of the intensity of a light source 1 data is called finod in the remainder of the description.
• the light source AI can be modulated in intensity by any known electrical or mechanical means.
• the light irradiating the target stratified medium 2 can be emitted continuously or pulsed by a given light source l ia.
• 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.
• the detection cell 12 also includes one or more appropriate sensors placed in this chamber, for example facing the target stratified medium 2. This is for example one or more electroacoustic sensors configured to convert the pressure of the acoustic wave into an electrical signal, such as a microphone or a piezoelectric transducer.
• 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 laminated medium 2 transmitted by an electroacoustic sensor.
• the analog-to-digital converter is operatively connected to a digital signal processor for digital signal processing.
• the non-invasive sensor 1 further comprises a module 14 for adapting the irradiation parameters and possibly the calibration model, as well as a simulation module 15. These two elements will be described later, after the we have clarified the origin of the technical problem to be solved, namely providing a non-invasive sensor 1 based on photoacoustic or photothermal detection of precision and/or consumption controlled by taking into account interference phenomena.
• the target stratified medium 2 comprising at least two superimposed layers having different physicochemical characteristics, it comprises at least one interface at which the incident wave can be reflected.
• the upper layer 2A is in contact with the external medium at the level of the interface 2A/ext and with the lower layer at interface 2A/2B.
• the upper layer 2A is for example the stratum comeum of the skin of a patient and the lower layer 2B is for example the epidermis of the skin of a patient in the case of a non-invasive sensor 1 adapted for measuring blood sugar.
• the interference phenomenon has a significant impact on the amplitude of the detected signal and consequently on the precision of the measurement. This impact depends on the structuring of the target stratified environment 2.
• a so-called “primary” thermal wave P is generated by the irradiation (symbolized by the dotted arrow) at the level of the 2A/2B interface.
• This primary wave P propagates in particular towards the interface 2A/ext, on which it is partially reflected, which gives rise to a first wave transmitted into the external environment Tl and a reflected wave RI.
• the reflected wave RI can itself be reflected on the interface 2A/2B to give rise to a transmitted wave T'2 and a reflected wave R2.
• the reflected wave R2 will itself give rise, after propagation towards the interface 2A/ext, to a second wave transmitted into the external environment T2 and a reflected wave R3, and so on.
• ⁇ £ 2B - £ ext are the respective thermal efficiencies of layer 2A, layer 2B and the external environment.
• the thermal effusivities as well as the thickness of layer 2A can, in the case of our target stratified medium 2, vary over time.
• the target stratified medium 2 is the skin and where the non-invasive sensor 1 is a blood glucose sensor
• a simulation module 15 which will be described in detail later and which makes it possible, among other things, to simulate the photothermal signal associated with irradiation of which all the characteristics (power, wavelength(s) (s), modulation frequency(s), etc.) are known, the target stratified medium 2 also being modeled by a multilayer medium whose characteristics (thicknesses, compositions, etc.) are known, on the basis of a multiphysical analytical model taking into account at least implicitly thermal interference phenomena.
• This simulation module 15 made it possible to carry out extensive in silico experiments, some of the results of which are presented in Figures 3a and 3b.
• the target stratified medium 2 is the skin, modeled by a two-layer model (stratum comeum, epidermis).
• the thickness of the upper layer (modeling the stratum comeum) is equal to 14 pm, that of the lower layer (modeling the epidermis) is considered infinite.
• Blood sugar is zero in the upper layer.
• the upper layer comprises 20% water and the lower layer comprises 60% water.
• the skin model is irradiated (in a simulated manner) with a laser of wave number 1034 cm' 1 modulated in intensity at a modulation frequency which is plotted on the abscissa axis.
• the amplitude of the simulated response thermal wave generated in response to irradiation is represented on the ordinate as a function of the modulation frequency for different blood glucose values of the lower layer (50 mg/L; 500 mg/L and 1000 mg/ dL).
• the nodal modulation frequencies correspond to a response wave generated in the medium whose amplitude is weakly correlated to the parameter of interest, or even independent of the parameter of interest (in this case: interstitial blood glucose), therefore the signal detected at a such modulation frequency cannot be used to determine this parameter of interest.
• the target stratified medium 2 is the skin, modeled by a two-layer model (stratum comeum, epidermis).
• the thickness of the upper layer (modeling the stratum comeum) is this time equal to 18 pm, that of the lower layer (modeling the epidermis) is considered infinite. Blood sugar is zero in the upper layer.
• the upper layer comprises 20% water and the lower layer comprises 60% water.
• the skin model is irradiated (in a simulated manner) with a laser of wave number 1034 cm' 1 modulated in intensity at a modulation frequency which is plotted on the abscissa axis.
• the amplitude of the simulated response thermal wave generated in response to irradiation is represented on the ordinate as a function of the modulation frequency for different blood glucose values of the lower layer (50 mg/L; 500 mg/L and 1000 mg/ L).
• the values of the nodal modulation frequencies depend on the characteristics of the target stratified medium 2, namely in particular the thicknesses and compositions of the different layers which constitute it.
• the inventors have therefore designed, on the basis of these observations, a non-invasive sensor 1 and a method for detecting a parameter of interest improved by the prior art, which we will now describe in more detail.
• the nodal modulation frequencies can be taken advantage of in the context of the non-invasive sensor 1 thanks to the adaptation module 14 which characterizes it and, in a particular embodiment, to the simulation module 15.
• 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 1 la for example a modulation frequency at which the modulation device intensity 11b modulates the intensity of light emitted by the light source 1 la, the wave number of a light emitted by the light source 1 la, the light power of the light source l ia, . . . ).
• a non-invasive sensor 1 comprising an adaptation module 14 for measuring the parameter of interest are represented schematically in Figure 5 in a particular embodiment and in the case where the target stratified medium 2 is the skin and 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 chart of nodal frequencies 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 the irradiation parameters for the measurement on the basis of the nodal frequency chart 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 measurement.
• the model configuration database and the nodal frequency chart can be 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 stratified medium 2, a set of irradiation cases (each comprising at least one parameter irradiation such as a light power, a wavelength and a modulation frequency) of the target stratified medium 2 and the photoacoustic (or where appropriate photothermal) signals theoretically detected in response to each case of irradiation for each configuration CMk model of the target stratified medium 2 from analytical models of the target stratified medium 2 and the photoacoustic (or where appropriate photothermal) detection cell 12, as shown in Figure 6.
• This simulation module 15 is particularly relevant in our case, for which the target stratified medium 2 is evolving over time and/or stratified.
• the target stratified medium 2 in fact adopts different real configurations over time (one or more concentrations vary within one or more layers of the target stratified medium 2, one or more dimensions such as, for example, the thickness of one of the layers of the target stratified medium 2 vary) which can each be modeled by a particular model configuration.
• the target stratified medium 2 to be analyzed is modeled as shown in Figure 4.
• 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 [1, 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 module 15 for each simulation) appropriate for the target stratified environment 2 considered.
• level 1 parameters because their values will be provided as input to the simulation module 15 for each simulation
• the target stratified 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 [Glc]j concentration of interest
• each layer i can be described for modeling by its thickness ei, its water concentration [H2O]i.
• 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 stratified medium model 2 can be calculated using analytical models. For example, the thermal conductivity, the thermal capacity, the density or even the absorption coefficient at each wavelength of each layer of the target stratified medium 2 can be deduced from the level 1 parameters and from known equations.
• the parameter of interest plays a particular role, it is not included in the list of level 1 parameters. Depending on the stage of the process, this parameter of interest will be known or not: its value is known to carry out the simulations by means of the simulation module 15, but it is of course unknown in the case of a real measurement with the non-invasive sensor 1.
• the number of layers N of the target stratified medium 2 can also be a variable of the model.
• N can thus be greater than or equal to 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 which can be 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.
• N 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 example, the Beer-Lambert equations for optical absorption and the equations thermodynamics of heat (Fourier's law and conservation laws).
• a CMk model configuration (k positive integer) of target stratified medium 2 corresponds to (or even models) a particular state of the given target stratified 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 1 la whose irradiation parameters are known, namely for example the finod modulation frequency, the wavelength X and the surface power density.
• the multiphysics analytical model makes it possible to simulate the pressure wave generated in the external environment A.
• 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 thermal wave generated in the target stratified 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 thermal wave generated in the target stratified 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 its dimensions (for example the size of the vent, the height of the cell, . . .), thermodynamic state parameters (temperature, atmospheric pressure, relative or absolute humidity, etc.).
• the photoacoustic detection cell can in particular be modeled using an equivalent RLC circuit.
• a model derived from 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 multiphysics analytical model is chosen to take into account, at least implicitly, thermal wave interference. It can, in a particular embodiment, take these interferences into account explicitly.
• the simulation module 15 therefore receives as input the parameters of the model configuration CMk of target stratified 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.
• 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 stratified medium 2 of interest.
• each irradiation case Ij can be generated, possibly in an automated and/or random manner, 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 wavelength of each of these lasers and optionally the 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 the 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 the parameter of interest, or different from one CMk model configuration to another and/or a value of the parameter of interest to another.
• all of these CMk model configurations, irradiation cases, parameters of interest and associated simulated photoacoustic (or where applicable photothermal) signals can be stored in a database of model configurations in the form of bytes ⁇ model configuration CMk, case of irradiation 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.
• the generation of irradiation cases can in particular take into account the limitations of the light sources 1 available for a non-invasive sensor 1 given in wavelength and/or power, or even of the ranges of modulation frequencies relevant to the type. of target stratified medium 2 to be analyzed, 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 components of the signal actually measured ⁇ obtained by experiments in real situations or understand both such multiplets obtained in a real situation and multiplets obtained by simulation.
• one or more artificial intelligence models 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 or photothermal signal and the irradiation parameters of the irradiation case, as shown in Figure 7.
• 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.
• 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 stratified medium 2 of interest.
• Each nodal byte includes:
• the characteristics common to all the elements of a given GMp group of model configurations of the target stratified medium (number of layers, thicknesses of the layers, concentrations, etc. excluding the parameter of interest which varies from one element of the GMp group to another;
• this chart of nodal frequencies can be stored in a memory of the simulation module, the chart comprising at least a plurality of nodal multiplets each comprising at least the common characteristics (or the set of irradiation parameters common) to a group of GMp model configurations and a plurality of associated nodal modulation frequencies.
• the nodal multiplets can optionally include one or more so-called special frequencies, denoted foptim, for which the sensitivity of the sensor is maximum in the range of modulation frequencies delimited by two nodal modulation frequencies (possibly successive) for the parameter of interest concerned.
• foptim special frequencies
• Such a particular foptim modulation frequency can be observed in zone 1 shown in Figure 3a.
• a particular modulation frequency is determined by means of the processor of the simulation module 15 or the adaptation module 14 based on a weighted average of at least two successive nodal modulation frequencies.
• the nodal frequency chart can be stored in a memory of the adaptation module 14 or of the simulation module 15 or another memory of the non-invasive sensor 1.
• the nodal frequency chart is generated from data obtained via in vivo experiments.
• the simulation module 15 is not necessary.
• the nodal frequency chart is generated both from data obtained in silico using the simulation module and from data obtained in vivo.
• the adaptation module 14 chooses an initial model configuration of target stratified medium 2 for irradiation based on predetermined criteria.
• the initial CMirrad model configuration is the CMk model configuration.
• a set of level 1 and average glycemia parameters for a patient population or for a given patient.
• This set of average parameters corresponds to an initial model configuration CMk of the target stratified medium 2 which has the greatest probability of being the most suitable for the measurement to come in the absence of any other information and in particular in the absence of a measurement history.
• the processor of the adaptation module 14 determines a plurality of nodal modulation frequencies fhod associated with the group GMp of model configurations of the target stratified medium to which the initial model configuration CMirrad belongs;
• the at least one particular modulation frequency foptim may be a modulation frequency for which the sensitivity of the sensor is maximum in the range of modulation frequencies delimited by two nodal modulation frequencies (possibly successive) for the parameter of interest concerned.
• the at least one particular modulation frequency foptim is determined by means of the processor of the simulation module 15 or the adaptation module 14 on the basis of a weighted average of at least two modulation frequencies successive nodals.
• a particular irradiation case for the model configuration of the target stratified medium 2 CMirrad which is an irradiation case comprising at least the particular irradiation frequency foptim for the initial CMirrad model configuration.
• the particular case of irradiation can be noted lopt.k since it is optimized in relation to a criterion for taking into account interference phenomena.
• 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) Determination of the parameter of interest:
• the processor of the adaptation module 14 determines the parameter of interest, in this example blood glucose, on the basis of the detected photoacoustic or photothermal signal.
• step e) Several embodiments of step e) are possible.
• the processor of the adaptation module 14 can implement a unique inverse model.
• the processor of the adaptation module 14 can use an operating law making it possible to deduce a blood sugar value from the detected signal.
• step e) can be broken down as follows: el) Resolution of the inverse problem:
• the processor of the adaptation module 14 implementing the learned inverse model(s) receives as input the photoacoustic or photothermal signal real and the particular irradiation case selected for irradiation, denoted for example lopt.k, and determines the current model configuration of target stratified medium 2, denoted CMmes, as well as the parameter of interest (as shown in the figure 7).
• CMmes current model configuration of target stratified medium 2
• the adaptation module 14 compares the current target stratified 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, CMC model configuration possibly different for a subsequent measurement).
• the processor of the adaptation module searches in the nodal frequency chart for a new plurality of nodal modulation frequencies, this time corresponding to the group of model configurations GMp' to which the new model configuration of target stratified medium CMmes belongs and it determines, on the basis of this plurality of nodal modulation frequencies, a new particular irradiation case which comprises at least one foptim modulation frequency determined based on the plurality of nodal modulation frequencies for the target stratified medium model configuration 2 CMmes.
• the new particular case of irradiation is for example noted lopt. f, insofar as it is optimized to take into account interference phenomena for the physiological situation measured.
• the processor of the adaptation module 14 transmits the corresponding parameters to the irradiation device 11.
• the target stratified medium 2 then undergoes a new irradiation (subsequent irradiation) according to the irradiation parameters of the lopt irradiation case. f. Then steps d) of detection and, where appropriate, processing of the photoacoustic or photothermal signal and el) of solving the inverse problem are repeated.
• the method can include at most one step e2) of validating the model configuration.
• the method may also include a reiteration of step e2) of validation of the model configuration.
• the current CMmes target stratified medium model configuration 2 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 under control, 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 (as shown in Figure 5), 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.
• the particular modulation frequency(ies) of the sources are selected to take into account interference phenomena: a particular modulation frequency foptim is a modulation frequency for which the sensitivity of the measurement is the greatest between two nodal modulation frequencies .
• the choice of an irradiation case comprising at least one particular modulation frequency makes it possible to increase the sensitivity of the sensor without increasing its energy consumption.
• the target stratified medium 2 is the skin of a patient, modeled by a two-layer model (stratum comeum, epidermis).
• the thickness of the upper layer (modeling the stratum comeum) is equal to 14 pm for Figure 8a, and we assume that an event causes this thickness to vary so that it evolves and becomes equal to 18 pm when the measurements for Figure 8b and Figure 8c are produced.
• the thickness of the lower layer (modeling the epidermis) is considered infinite. Blood sugar is zero in the upper layer.
• the upper layer comprises 20% water and the lower layer comprises 60% water.
• the chart of nodal frequencies makes it possible to determine, by means of the processor of the adaptation module 14, a plurality of nodal frequencies for the initial situation (thickness of the stratum comeum equal to 14 pm) and to determine the case of irradiation to be used for this situation based on these nodal frequencies.
• the chart of nodal frequencies makes it possible to determine, by means of the processor of the adaptation module 14, a plurality of nodal modulation frequencies for the final situation (thickness of the stratum comeum equal to 18 pm) and to determine the case irradiation to be used for this situation based on these nodal frequencies.
• the adaptation of the nodal modulation frequencies (on the basis of which are determined the particular modulation frequencies) to the current physiological situation also makes it possible to control the precision of the non-invasive sensor 1, for example so that the mean square deviation or any other quantity making it possible to quantify the precision is less than a predetermined threshold.
• the detection method may comprise the selection for each case of irradiation of a single particular foptim modulation frequency determined on the basis of a plurality of nodal frequencies, for example by means of an arithmetic or weighted average. In particular, in the case of a weighted average, a greater weight can be assigned to the lowest nodal modulation frequency.
• This arrangement makes it possible to take into account the fact that the amplitude of the photothermal signal decreases with the frequency of modulation of the intensity of the incident light wave.
• the amplitude of the photothermal signal is proportional to the inverse of the modulation frequency of the incident light wave. Consequently, particularly in this case, a particular modulation frequency foptim can be the barycenter of two nodal modulation frequencies each assigned a weight equal to its inverse. If we denote fl and f2 as two successive nodal modulation frequencies, the particular modulation frequency can therefore be calculated as follows:
• a particular modulation frequency foptim can be the barycenter of two successive nodal modulation frequencies each assigned a weight equal to its inverse raised to the 3/2 power.
• weights can still be chosen, for example to take into account the frequency responses of one or more other elements of the measurement chain.
• the irradiation case selected in the process may include, in addition to a particular modulation frequency foptim, at least one nodal modulation frequency.
• the detected signal corresponding to the nodal modulation frequency does not carry information concerning the parameter of interest, it does on the other hand carry information concerning the structuring of the stratified medium 2 independently of this parameter of interest. interest.
• the selection of both a particular modulation frequency foptim and a nodal food modulation frequency can therefore lead to the resolution of the inverse problem with increased efficiency and precision compared to any other selection of two modulation frequencies.
• the main optimization criterion is energy consumption, for example if the non-invasive sensor is powered by a battery which reaches a predetermined lower threshold of charge, it is possible to select for irradiation only a single modulation frequency, namely a particular modulation frequency foptim.
• the energy consumption criterion is less important and irradiation following two different modulation frequencies is permitted, a particular modulation frequency foptim and the lowest nodal modulation frequency among those used to determine foptim. Indeed, it is at this modulation frequency the weakest nodal that the amplitude of the signal will be the highest and therefore the precision the best amplitude.
• foptim a particular modulation frequency foptim and two nodal modulation frequencies to determine foptim, and optionally also a second, a third, etc., particular modulation frequency.
• the invention finally relates to a computer program comprising instructions which cause the non-invasive sensor 1 according to one of the preceding embodiments to execute the steps of the method according to any of the embodiments described above.
• 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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