An intelligent laryngeal mask and respiration monitoring system

Abstract

The present application relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and a breathing monitoring system, comprising a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded in the laryngeal mask body, containing an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates the coupled original signal, inputs a multi-parameter coupling correction model combined with the physical parameters of the coating, and decouples out independent and accurate multi-parameters; the target thickness of the coating is determined by comprehensive calculation of minimizing the acoustic correction error and meeting the sensitivity of each environmental parameter. The system can generate abnormal ventilation alarm and feedback control ventilator. The present application can realize multi-parameter synchronous monitoring, eliminate coating interference, and improve the safety and practicality of respiratory management. In the actual application process, the abnormal breathing function caused by the abnormal state of the user's ventilator or the abnormal position of the laryngeal mask can be found in time, and the purpose of early discovery and early intervention can be achieved to avoid malignant events.

Description

Technical Field

[0001] This invention relates to the field of multi-parameter physiological signal monitoring technology, and in particular to an intelligent laryngeal mask and respiratory monitoring system. Background Technology

[0002] During the perioperative and emergency periods, the laryngeal mask airway (LMA) is a crucial airway management tool, requiring real-time respiratory monitoring to assess the patient's ventilation status (e.g., hypoventilation, laryngospasm, secretion obstruction, etc.). Existing LMA respiratory monitoring technologies have significant limitations: Limited sensor functionality: Traditional monitoring relies on independent acoustic sensors (such as microphones), temperature sensors, and pressure sensors. The stacking of multiple sensors leads to a complex and bulky LMA structure, easily causing patient discomfort, and making simultaneous analysis of sensor signals difficult. Bottlenecks in fiber optic sensing: Some solutions use fiber optic sensors to detect respiratory sounds, but cannot simultaneously monitor environmental parameters such as temperature and humidity. Adding a sensitive coating to the fiber optic cable to expand multi-parameter detection capabilities can cause interference such as attenuation and reflection of acoustic signals, significantly reducing sound detection accuracy. Furthermore, current technologies lack effective interference correction methods.

[0003] Therefore, there is an urgent need for a technical solution that can simultaneously detect sound, temperature, humidity, and pressure on the same laryngeal mask structure, and can effectively eliminate coating interference and optimize coating thickness, so as to improve the accuracy and clinical applicability of respiratory monitoring. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0005] According to a first aspect of the present invention, a smart laryngeal mask is provided, comprising: a laryngeal mask body and a multi-parameter fiber optic measurement system, the system comprising:

[0006] The fiber optic sensing unit comprises at least one segment of optical fiber embedded within the laryngeal mask body, and is used to sense physiological signals in the laryngeal region; the fiber optic sensing unit includes:

[0007] The acoustic sensing unit is used to generate a first optical response in response to sound wave vibrations.

[0008] An environmental sensing unit has a functional coating on its surface that is sensitive to at least one of the environmental physical parameters, namely temperature, humidity and pressure, and is used to generate a second optical response in response to changes in environmental parameters.

[0009] The signal processing unit is used to receive and demodulate the first optical response and the second optical response to obtain the original sensing signal containing multi-parameter coupling information, and input the original sensing signal and at least one known physical property parameter of the functional coating into a predefined multi-parameter coupling correction model to calculate at least two parameters among the acoustic signal, temperature value, humidity value and pressure value.

[0010] According to a second aspect of the present invention, a respiratory monitoring system is provided for use with the smart laryngeal mask described in the first aspect; the respiratory monitoring system includes:

[0011] An optical demodulation unit is used to transmit optical signals to the fiber optic sensing unit of the smart laryngeal mask, receive and demodulate the returned optical signals, and obtain a raw sensing signal containing coupled information of at least two parameters among sound, temperature, humidity and pressure.

[0012] The signal processing unit, which stores the multi-parameter coupling correction model, is configured to receive the original sensing signal and input the received original sensing signal and at least one known physical property parameter of the functional coating into the multi-parameter coupling correction model to calculate at least two parameters among the acoustic signal, temperature value, humidity value and pressure value.

[0013] The clinical decision unit is used to determine the patient's ventilation status based on the calculated parameters and generate an alarm signal when an abnormality is detected.

[0014] The present invention has at least the following beneficial effects:

[0015] (1) Achieve high-precision synchronous monitoring of multiple parameters: Through the integrated design of acoustic sensing unit and environmental sensing unit with functional coating, the laryngeal sounds (breathing sounds, abnormal airway sounds), temperature, humidity and pressure are detected synchronously on the same optical fiber structure. There is no need to arrange multiple additional sensors, simplifying the laryngeal mask structure and reducing patient discomfort. At the same time, through the multi-parameter coupling correction model (linear / nonlinear correction factor), the interference of the coating on the acoustic signal is effectively decoupled, so that the detection accuracy of each parameter meets the clinical needs (such as minimizing the acoustic signal correction error and meeting the temperature / humidity / pressure sensitivity standards).

[0016] (2) Optimize coating thickness and take into account multiple performance objectives: By using the target thickness calculation method, the target thickness of the functional coating is determined with the dual objectives of minimizing acoustic correction error and meeting the preset threshold for temperature / humidity / pressure sensitivity. This solves the problem that coating thickness is difficult to balance the multi-parameter detection performance in traditional solutions, and ensures that the same coating can efficiently sense environmental parameters while minimizing interference with acoustic signals.

[0017] (3) Improve clinical applicability and adaptability: The system generates ventilation abnormality alarms (such as insufficient ventilation and laryngospasm) through the application output unit and provides feedback control parameters to the ventilator / anesthesia machine. It also supports wireless transmission to the monitoring terminal or cloud, adapting to multiple scenarios such as perioperative period and emergency care. Multi-parameter synchronous analysis can more comprehensively judge the patient's ventilation status, provide early and proactive warnings for ventilation abnormalities and laryngeal mask displacement, and can link with the ventilator for intelligent feedback control, thereby significantly improving the safety of respiratory management in perioperative and critical care nursing, and realizing the leap from passive monitoring to active intervention.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 and Figure 2 This is a schematic diagram of the overall structure of the intelligent laryngeal mask.

[0021] Figure 3 and Figure 4 This is a partial schematic diagram of the fiber optic sensing unit;

[0022] Figure 5 and Figure 6 This is a schematic diagram of a multi-core LC / APC connector. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] It should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. A process can be terminated when its operation is complete, but it may also have additional steps not included in the figures. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0026] Figure 1 and Figure 2 This is a schematic diagram of the overall structure of the intelligent laryngeal mask, showing the positional relationship of the main body of the laryngeal mask, the fiber optic acoustic sensor, the fiber optic wiring path, the sleeve, and the connector. Figure 3 and Figure 4 This is a partial cross-sectional view (AA section) of the fiber optic sensing unit, showing the method of fixing the FBG inside the silicone sleeve wall (epoxy sealant) and the core structure of the acoustic sensing unit. Figure 5 and Figure 6 This is a schematic diagram of a multi-core LC / APC connector, illustrating the convergence of fiber optic signals and the external interface configuration.

[0027] This invention provides an intelligent laryngeal mask, comprising: a laryngeal mask body and a multi-parameter fiber optic measurement system integrated therein.

[0028] The laryngeal mask body is made of medical-grade silicone, such as... Figure 1 As shown, the laryngeal mask body includes an airway tube 6, a cuff 1 for sealing the airway, and a distal cuff 2, with a standard interface 4 at the proximal end that is compatible with the breathing circuit.

[0029] A multi-parameter fiber optic measurement system includes a fiber optic sensing unit and a signal processing unit, such as... Figure 1 and Figure 2 As shown, the fiber optic sensing unit consists of at least one fiber 5, which is embedded along the wall of the airway duct and extends to the cuff area. It utilizes the optical properties of the fiber (such as light reflection, refraction, wavelength modulation, etc.) to detect physiological signals.

[0030] In this embodiment of the invention, an acoustic sensing unit and an environmental sensing unit are integrated on the optical fiber.

[0031] The acoustic sensing unit is used to generate a first optical response in response to sound wave vibrations.

[0032] like Figure 1 and Figure 2 As shown, the acoustic sensing unit may include a first fiber optic acoustic sensing unit 3 and a second fiber optic acoustic sensing unit 7. The first fiber optic acoustic sensing unit is arranged on the outside of the distal cuff 2, and the second fiber optic acoustic sensing unit is arranged on the proximal end of the airway duct 6 to realize the acquisition of sound field in different regions of the larynx.

[0033] In this embodiment of the invention, the acoustic sensing unit is any of the following structures: a structure inscribed with a fiber Bragg grating and attached with an acoustically sensitive thin film on an optical fiber; or a Fabry-Perot interference microcavity structure fabricated on the end face of the optical fiber, i.e., a structure based on a fiber Bragg grating and an acoustically sensitive thin film or a structure based on a Fabry-Perot interference microcavity.

[0034] (1) Structure based on fiber Bragg grating and acoustic thin film

[0035] like Figure 1 ,2 , 3 and Figure 4 As shown, the acoustic sensing unit of this structure includes a first fiber optic acoustic sensing unit 3 and a second fiber optic acoustic sensing unit 7. The first fiber optic acoustic sensing unit is precisely positioned on the outside of the distal cuff 2, while the second fiber optic acoustic sensing unit is located at the proximal end of the airway duct 6. This arrangement can fully cover different areas of the larynx, achieving efficient acquisition of the sound field.

[0036] The specific construction of this structure involves inscribing a fiber Bragg grating 11 on fiber 5 using advanced femtosecond laser direct writing technology or a 193nm excimer laser combined with phase masking. Femtosecond laser direct writing technology utilizes its extremely short pulses and high peak energy to directly excite electrons from the valence band to the conduction band through nonlinear optical effects such as multiphoton absorption and avalanche ionization, inducing a change in the refractive index of a localized region of the fiber, thus forming a grating structure. Fiber Bragg gratings formed in this way exhibit excellent tolerance to extreme environments such as high temperatures and possess exceptional stability. The 193nm excimer laser combined with phase masking method modulates the laser using a phase mask. On a photosensitive fiber core doped with photosensitive elements (such as germanium or phosphorus) and treated with hydrogen, ultraviolet laser exposure induces periodic modulation of the refractive index, thereby forming a fiber Bragg grating. This method is technically mature and allows for precise parameter control.

[0037] After the inscription is completed, the fiber Bragg grating 11 is securely fixed to the inner side of the silicone sleeve wall 10 using epoxy sealant 12. A specially designed acoustic-sensitive film (not shown) is tightly attached to the surface of the fiber Bragg grating 11. The acoustic-sensitive film can be made of a material sensitive to sound response, such as aluminum nitride (AlN) or scandium aluminum nitride (AlScN) films with piezoelectric effect. When acoustic vibrations occur in the throat, such as breath sounds or laryngospasm vibrations, the acoustic-sensitive film, with its excellent acoustic response characteristics, can keenly sense these vibrations and transmit them to the fiber Bragg grating 11 below. The acoustic vibrations cause deformation of the acoustic-sensitive film, which in turn causes a change in the grating pitch of the fiber Bragg grating 11. According to the optical characteristics of the fiber Bragg grating, the change in grating pitch will significantly change its reflection characteristics for light of a specific wavelength, generating the first optical response, i.e., the wavelength shift signal, thereby efficiently realizing the conversion from complex acoustic signals to precise optical signals. This structure offers significant advantages. The fiber Bragg grating exhibits extremely high accuracy and stability in detecting wavelength changes, enabling it to precisely capture even the faintest acoustic vibrations in the throat, providing reliable data for subsequent signal analysis and processing.

[0038] (2) Structure based on Fabry-Perot interferometer microcavity

[0039] At the end face of fiber 5, a Fabry-Perot interference microcavity was meticulously fabricated using cutting-edge microfabrication techniques such as femtosecond laser processing and micro-nano etching. A Fabry-Perot interference microcavity typically consists of two extremely flat and parallel reflecting surfaces, which can be achieved by depositing a highly reflective metal film (such as silver or aluminum) on the fiber end face or by using a multilayer dielectric film. When acoustic waves from the throat propagate to the fiber end face, they directly affect the optical propagation environment within the microcavity, causing subtle changes in the optical path length.

[0040] For example, in the actual manufacturing process, high-purity optical materials are selected to construct the microcavity. By precisely controlling parameters such as the length of the microcavity, the flatness of the reflective surface, and the characteristics of the internal medium, the high sensitivity of the microcavity to acoustic vibrations is ensured. When acoustic vibrations from the throat are transmitted to the microcavity, they will cause changes in the length of the microcavity or the refractive index of the internal medium. According to the Fabry-Perot interference principle, the interference fringes of the light signal reflected from the microcavity will change accordingly, thus generating the first optical response. This structure, relying on an all-fiber design, exhibits excellent adaptability and stability in complex physiological environments and has extremely high sensitivity to weak acoustic signals. It can effectively detect various sound signals from the throat, including breath sounds and laryngospasm vibrations, providing strong support for the acoustic monitoring function of the intelligent laryngeal mask. In addition, the fiber optic FP microcavity also has the advantages of easy coupling, low loss, simple mode distribution, and mature dispersion control, making it more suitable for the acoustic sensing scenarios of this intelligent laryngeal mask compared to other microresonator platforms.

[0041] In this embodiment of the invention, the environmental sensing unit, as the core functional module of the multi-parameter fiber optic measurement system, is integrated into a specific section of fiber optic 5. Its deployment is divided into two methods: one is to reuse the same fiber as the acoustic sensing unit (achieved through the division of different functional areas on the fiber); the other is to set it independently through fiber branches (suitable for scenarios with extremely high sensing accuracy requirements). The core function of the environmental sensing unit is to sense the physical parameters of the throat environment through a functional coating on its surface. Its surface is provided with a functional coating (not shown) sensitive to at least one parameter among temperature, humidity, and pressure, used to generate a second optical response in response to changes in environmental parameters. The material selection, preparation process, and performance control of each sensitive coating are as follows:

[0042] (a) Material selection for functional coatings

[0043] Based on the monitoring requirements of parameters such as temperature, humidity, and pressure in the throat environment, functional coating materials need to be selected by comprehensively considering sensing sensitivity, biocompatibility, and stability, and classified according to different sensing types.

[0044] For temperature parameter monitoring, temperature-sensitive materials with significant optical property responses to temperature changes were selected. Among these, metal oxides included vanadium oxide (VO2) and tin oxide (SnO2). Vanadium oxide has a refractive index temperature coefficient of approximately -2.5 × 10⁻⁶ in the 30-40℃ range. -4 The phase transition temperature is close to human body temperature, making it ideal for monitoring physiological environments. Tin oxide exhibits excellent stability and does not show performance degradation even after long-term use. The phase transition polymer used is polycaprolactone (PCL), with a phase transition temperature between 32-37℃ and a volume change rate exceeding 5% with temperature. This allows it to drive changes in the fiber optic microstructure through its own deformation, thereby achieving temperature sensing. The rare-earth-doped glass uses praseodymium-doped silica glass, with a refractive index temperature coefficient of approximately 1.2 × 10⁻⁶. -5 / ℃, not only has excellent optical stability, but also has strong anti-interference ability.

[0045] Humidity-sensitive materials used for humidity parameter monitoring are primarily those that exhibit significant changes in physical properties due to the adsorption or desorption of water molecules. In the area of ​​hydrophilic polymers, polyimide (PI) has a water absorption rate of 2%-5%, a volume expansion rate exceeding 3% after water absorption, and strong adhesion to optical fibers. Chitosan, as a natural polymer, has good biocompatibility and a humidity response range covering 30%-95%RH, adaptable to monitoring needs under varying humidity conditions. Nanoporous metal oxides include titanium dioxide (TiO2) and alumina (AL2O3). Titanium dioxide has a porosity of 40%-60%, allowing water molecules to easily fill its pores, thus altering its dielectric constant. Alumina exhibits strong corrosion resistance, making it suitable for stable operation in humid environments containing bodily fluids, such as the throat.

[0046] Pressure-sensitive materials must exhibit significant deformation or changes in electrical properties in response to pressure to meet pressure parameter monitoring requirements. Lead zirconate titanate (PZT) is selected as the piezoelectric ceramic, with a piezoelectric coefficient exceeding 300 pC / N. It generates a charge change under pressure, which can be used to indirectly modulate optical signals. Polydimethylsiloxane (PDMS) is used as the elastomer, with an elastic modulus of 1.5 MPa. Its deformation is linearly related to pressure, and its biocompatibility meets medical standards. The carbon nanotube composite material is a system combining carbon nanotubes and PDMS. Its conductivity changes with pressure at a rate exceeding 10% / kPa, which can assist in optical signal calibration by utilizing changes in resistance, thereby improving the accuracy of pressure monitoring.

[0047] For scenarios requiring simultaneous monitoring of multiple parameters, the functional coating employs a "multi-layer composite structure," where each layer's material can be independently selected without interference between them. Taking a typical multi-layer composite structure as an example, the bottom layer is a temperature-sensitive layer, made of vanadium oxide with a thickness controlled at 500-600 nm, prepared using magnetron sputtering. The middle transition layer, made of silicon dioxide (SiO2) with a thickness of 50-100 nm, primarily isolates the different sensitive layers, preventing interpenetration. The middle humidity-sensitive layer is made of polyimide with a thickness of 800-1000 nm, prepared using the sol-gel method. The top layer is a pressure-sensitive layer, made of PDMS with a thickness of 1000-1200 nm, prepared using a drop-coating method. This layered structure enables simultaneous and precise monitoring of multiple parameters, including temperature, humidity, and pressure.

[0048] (II) Preparation process and precision control of functional coatings

[0049] Based on the characteristics of the functional coating materials described above, a suitable coating process needs to be selected to ensure the uniformity, adhesion, and sensing performance of the coating. The specific process and related requirements are as follows:

[0050] Magnetron sputtering is primarily suitable for inorganic material coatings, such as vanadium oxide, tin oxide, titanium dioxide, and piezoelectric ceramics (PZT), which require high density. Precise control of process parameters is crucial: sputtering power should be set between 50-150W, argon flow rate maintained at 20-50 sccm, and substrate temperature controlled between 25-100℃. For vanadium oxide coatings, the substrate temperature must be strictly controlled at 60-80℃ to ensure phase transition characteristics. Sputtering time should be between 10-30 minutes. In terms of quality control, the coating density is detected by observing the coating cross-section using a scanning electron microscope (SEM, model ZeissSigma300). The accelerating voltage is set to 15kV and the magnification is 5000x. The proportion of non-porous areas is counted, and the density is required to exceed 95%. The thickness deviation is monitored in real time using a laser film thickness monitor (model FilmetricsF20). The measurement points are spaced 100μm apart, and the average value of 10 points is taken as the coating thickness. The deviation must be controlled within ±5%.

[0051] The sol-gel method is suitable for organic polymers and some nano-oxides, including polyimide, chitosan, and nanoporous titanium dioxide. The process consists of three steps: First, sol preparation involves dissolving the material powder (e.g., polyimide monomer) at a concentration of 5%-20% in a suitable solvent (e.g., N-methylpyrrolidone) and stirring for 2-4 hours until homogeneous. Second, coating involves uniformly coating the optical fiber surface using a dip-coating method (dip speed 5-20 mm / s) or a spin-coating method (speed 1000-3000 rpm). Third, curing involves staged temperature increases in an oven. Polyimide requires holding at 150℃ for 2 hours, while chitosan requires holding at 80℃ for 1 hour. The heating rate is controlled at 5℃ / min to prevent coating cracking. For quality control, coating adhesion is tested according to GB / T9286-1998 "Cross-cut test for paints and varnishes". A cross-cutting knife is used to make cuts at 1mm intervals down to the fiber optic substrate. Then, 3M 600 tape is applied and vertically peeled off. The coating peeling is observed, and the adhesion is required to exceed 5 N / cm². 2 (Corresponding to grid level 0); Surface roughness is measured by scanning the coating surface with an atomic force microscope (AFM, model Bruker Dimension Edge), with the scanning range set to 5μm×5μm, and the surface roughness Ra is required to be less than 10nm.

[0052] The drop-coating method is suitable for elastomers and composite materials, and is commonly used for flexible materials such as PDMS and carbon nanotube composites. The process also consists of three steps: the first step is material preparation, where PDMS prepolymer and curing agent are mixed at a mass ratio of 10:1, stirred thoroughly, and then vacuum-sealed to remove bubbles; the second step is drop-coating, where the material is drop-coated onto a designated area of ​​the optical fiber using a precision syringe with a range of 1-10 μL, and the coating is kept smooth using a spin coater (1000-3000 rpm); the third step is curing, where the coating is kept at 80℃ for 30-60 minutes to ensure complete curing. For quality control, the coating thickness uniformity must exceed 95%, meaning the thickness difference between different areas of the same coating is less than 3%, and the elastic deformation recovery rate must exceed 98%. This indicator is verified through cyclic pressure testing.

[0053] After coating preparation, post-treatment is required. All coatings must undergo plasma cleaning using argon plasma at a power of 50W for 5 minutes to remove surface impurities and improve biocompatibility. For composite coatings, interlayer treatment is necessary; for example, the silica transition layer requires modification with a silane coupling agent. These treatments ensure interlayer adhesion greater than 3 N / cm. 2 To avoid layering.

[0054] (III) Core physical property parameters of functional coatings

[0055] In this embodiment of the invention, the functional coating used for the multi-parameter coupled correction model has known physical property parameters, which need to be detected and calibrated by specialized equipment after coating preparation. These parameters specifically include the following categories (one or more selected based on sensing requirements): basic physical parameters, thermodynamic parameters, mechanical parameters, and sensing characteristic parameters. Among these, the basic physical parameters include thickness (detected by a laser film thickness monitor (accuracy ±1nm)) and density (detected by water displacement or gas replacement method (accuracy ±0.01g / cm³)). 2 The refractive index (measured by an ellipsometer (measurement range 1.3-2.0, accuracy ±0.001)) and thermodynamic parameters include the coefficient of thermal expansion and glass transition temperature, where the coefficient of thermal expansion is measured by a thermomechanical analyzer (temperature range -20-80℃, accuracy ±1×10⁻⁶). -6 / ℃), the glass transition temperature was determined by differential scanning calorimetry (accuracy ±1℃). Mechanical parameters included Young's modulus (measured by nanoindentation (load range 1-100mN, accuracy ±0.1GPa)) and Poisson's ratio (measured by tensile testing combined with optical strain gauge (accuracy ±0.01)). Sensing characteristics included temperature response coefficient (the rate of change of the refractive index of the tested coating with temperature in a constant temperature chamber (accuracy ±1×10⁻⁶)). -5 ( / ℃)), humidity adsorption rate (curve of coating quality as a function of humidity in a humidity generator (accuracy ±0.1%RH / s)) and pressure deformation coefficient (relationship between coating deformation and pressure in a pressure testing machine (accuracy ±0.1μm / kPa)).

[0056] (iv) Layout and measurement principles of each sensing area

[0057] The environmental sensing section comprises temperature, humidity, and pressure sensing areas. Each area is precisely positioned on the laryngeal mask body according to the characteristics of the monitored parameters. Combined with the performance of the corresponding sensitive material coating, accurate measurement of the parameters is achieved. The specific settings are as follows:

[0058] 1. Temperature sensing area

[0059] Placement: The cuff is evenly distributed along the annular area close to the airway on the inner side of the cuff 1. This area directly contacts the exhaled air from the laryngeal airway and is rigidly connected to the cuff wall. The influence of cuff inflation deformation is less than ±0.5%, which can accurately capture the real-time temperature changes of the laryngeal airway (avoiding temperature measurement errors caused by cuff deformation).

[0060] Coating materials and preparation: Vanadium oxide (VO2) or praseodymium-doped silica glass was selected as the temperature-sensitive coating material and coated on the surface of the optical fiber 5 embedded in the silicone sheath 10 by magnetron sputtering. The initial coating thickness was controlled at 500-800 nm (the final thickness was based on the "temperature sensing target thickness"). During the sputtering process, the substrate temperature was controlled at 60-80℃ to ensure that the vanadium oxide formed a rutile phase structure and improve the temperature response sensitivity.

[0061] Measurement principle: When the temperature of the laryngeal airway changes (such as fluctuations in the patient's body temperature or changes in the temperature of exhaled air), the refractive index of the temperature-sensitive coating changes linearly with temperature (the temperature coefficient of refractive index of vanadium oxide in the 30-40℃ range is approximately -2.5×10). -4 / ℃, praseodymium-doped silica glass is approximately 1.2 × 10⁻⁶. -5 / ℃), causing a change in the phase of the light transmitted in the optical fiber; if a fiber Bragg grating 11 is etched on the optical fiber 5 in this area (which can reuse the same grating as the acoustic sensing unit and distinguish between temperature and acoustic signals through a signal demodulation algorithm, or can be etched with a dedicated grating independently), the change in the refractive index of the coating will further cause the grating reflection wavelength to shift, and the amount of shift is strictly linearly related to the temperature change (the sensitivity is about 10pm / ℃).

[0062] The external optical demodulation unit receives the reflected light signal through the multi-core LC / APC connector 13. After the wavelength offset is demodulated by the spectral analysis module, it is substituted into the pre-calibrated "temperature-wavelength offset" formula such as T=k×△λ+T0, where k is the temperature coefficient, △λ is the wavelength offset, and T0 is the reference temperature. The real-time temperature value of the throat airway can then be calculated with a detection accuracy of ±0.1℃ and a response time of less than 0.5s.

[0063] 2. Humidity sensing area

[0064] Placement: The coating is placed along the axial area of ​​the inner surface of the cuff 1 in contact with the airway. The coating coverage length is consistent with the effective ventilation section of the airway duct 6 (about 3-5 cm) and parallel to the central axis of the airway to ensure full coverage of the main flow path of exhaled gas in the airway and avoid humidity measurement lag caused by dead zones in gas flow.

[0065] Coating Materials and Preparation: Polyimide (PI) or nanoporous titanium dioxide (TiO2) was selected as the humidity-sensitive coating material and coated onto the surface of optical fiber 5 using the sol-gel method. The initial coating thickness was controlled at 800-1200 nm (the final thickness is based on the "target thickness for humidity sensing"). For the polyimide coating, the monomer solution was dissolved in N-methylpyrrolidone at a concentration of 10%, the coating was applied at a spin-coating speed of 10 mm / s, and cured at 150°C for 2 hours. For the nanoporous titanium dioxide coating, the sol concentration was adjusted to 15%, the spin-coating speed was 2000 rpm, and the coating was cured at 120°C for 1 hour, ensuring a coating porosity of approximately 50%.

[0066] Measurement Principle: When the humidity of the laryngeal airway changes (such as changes in the patient's respiratory secretions or adjustments to the humidifier output of the ventilator), the hydrophilic functional coating adsorbs or desorbs water molecules: For the polyimide coating, the volume expansion rate is positively correlated with humidity due to water molecules penetrating between the molecular chains; for the nanoporous titanium dioxide coating, the mass change rate is linearly correlated with humidity due to water molecules filling the nanopores. Both situations will generate axial stress on the optical fiber 5. If a fiber Bragg grating 11 is installed in this area, the stress will change the grating pitch, thereby causing a shift in the reflected wavelength (humidity sensitivity is approximately 20 pm / %RH).

[0067] After the optical demodulation unit detects the wavelength shift, it combines the "humidity-wavelength shift" curve calibrated in advance by the humidity generator (the curve is obtained by taking a calibration point every 5%RH in the range of 30%-95%RH and fitting it with the least squares method) to calculate the relative humidity value of the throat airway. The measurement range is 30%-95%RH, the accuracy can reach ±2%RH, and the response time is less than 1s.

[0068] 3. Pressure sensing area

[0069] Location: Located in the outer region of the cuff 1 corresponding to the pressure optical sensing cavity 8. This region is in direct contact with the laryngeal mucosa. The pressure optical sensing cavity 8 is a closed cavity structure (volume of about 0.5-1mL), which can simultaneously sense the inflation pressure inside the cuff (0-30kPa) and the contact pressure of the laryngeal tissue (0-15kPa), avoiding ischemic damage to the laryngeal tissue due to excessively high cuff pressure (above 25kPa).

[0070] Coating Materials and Preparation: PDMS elastomer or PZT piezoelectric ceramic was selected as the pressure-sensitive coating material and coated onto the surface of optical fiber 5 using a drop-casting method. For the PDMS coating, the prepolymer and curing agent were mixed at a ratio of 10:1, and after drop-casting, the coating was cured at 1500 rpm and 80℃ for 40 min, with the initial thickness controlled at 1000-1500 nm. The PZT coating was prepared by magnetron sputtering with a sputtering power of 120 W, an argon flow rate of 30 sccm, and a substrate temperature of 80℃, with the initial thickness controlled at 600-900 nm (the final thickness was based on the "target thickness for pressure sensing").

[0071] Measurement Principle: When laryngeal pressure changes (e.g., cuff inflation pressure adjustment, tissue pressure fluctuations caused by swallowing or coughing): the PDMS elastomer coating undergoes linear deformation under pressure (deformation is proportional to pressure, elastic modulus 1.5 MPa), causing the optical fiber 5 to undergo micro-bending or axial stretching; the PZT piezoelectric ceramic coating exhibits a piezoelectric effect under pressure, with the rate of change of dielectric constant linearly related to pressure, thus affecting the electric field distribution around the optical fiber. If a fiber Bragg grating 11 is inscribed in this area, the aforementioned deformation or electric field change will cause a shift in the grating reflection wavelength (pressure sensitivity approximately 5 pm / kPa); if a Fabry-Perot interference structure is used (compatible with the acoustic sensing structure, forming a microcavity through fiber end-face coating), the coating deformation will change the microcavity length (length change is positively correlated with pressure), causing the interference fringes to shift (fringe shift approximately 0.5 fringes / kPa).

[0072] The optical demodulation unit calculates the throat pressure value by analyzing the wavelength shift or interference fringe movement and substituting it into the "pressure-signal change" calibration formula, such as P=m×△λ+P0, where m is the pressure coefficient and P0 is the reference pressure. The measurement range is 0-30kPa, the accuracy is ±0.2kPa, and the response time is less than 0.3s.

[0073] (v) Coating scheme for reused optical fibers (when sharing optical fibers with the acoustic sensing unit)

[0074] When the environmental sensing unit and the acoustic sensing unit share the same optical fiber 5, the core principle of coating design is: through "precise division of functional areas + coating material compatibility design," to ensure the acoustic response sensitivity of the acoustic sensor while ensuring the accuracy of environmental parameter sensing, and to avoid physical interference or chemical conflicts between different coatings. The specific solution is as follows:

[0075] 1. Fiber Optic Functional Region Division: First, physical markings (marking width 50-100μm, depth <5μm, not affecting fiber transmission performance) are created on fiber 5 using a laser marking machine, dividing the fiber into two independent functional regions:

[0076] Acoustic sensing dedicated area: approximately 1-2cm in length, corresponding to the fiber Bragg grating 11 writing section or the section where the Fabry-Perot interference microcavity is located in the acoustic sensing unit, used only for acoustic signal acquisition;

[0077] Environmental sensing functional area: The length is set according to the sensing requirements (0.5-1cm for each of the temperature / humidity / pressure sensing areas), corresponding to the functional coating section of the environmental sensing unit, used for monitoring temperature, humidity or pressure parameters;

[0078] The distance between the two areas should be no less than 500μm to avoid signal crosstalk caused by coating edge overflow.

[0079] 2. Regional Coating Design

[0080] (1) Coating for acoustic sensing zone: Prioritize acoustic response. Based on the specific structure of the acoustic sensing unit, a targeted coating protection scheme is adopted. The core is to reduce the coating's obstruction to the transmission of sound wave vibrations.

[0081] If it is a "fiber Bragg grating 11 + acoustic thin film" structure:

[0082] Grating writing section (acoustic film attachment area): No functional coating is applied. Only an ultra-thin PDMS transparent coating (thickness <5μm) is applied to the outside of the acoustic film (thickness 5-10μm, material is polyvinylidene fluoride). It is prepared by drop coating method (prepolymer and curing agent are mixed in a 10:1 ratio, and the spin coater speed is 3000rpm). This coating can achieve waterproof (waterproof rating IPX7) and protection against biological fluid corrosion. Due to its extremely thin thickness and low elastic modulus (1.5MPa), it will not absorb the sound wave vibration energy (vibration attenuation rate <0.5%), ensuring the acoustic film's response sensitivity to throat vibrations (such as respiratory sounds and laryngospasm vibrations).

[0083] The non-written section of the grating (the area fixed to the silicone sheath wall 10 by epoxy sealant 12): coated with a modified epoxy coating (5%-10% silane coupling agent added to ordinary epoxy adhesive), prepared by drop coating method, with the thickness controlled at 10-20μm. This coating has good compatibility with the epoxy sealant used for fixing (peel strength >10N / cm), which can enhance the bonding stability between the optical fiber and the sheath wall, and does not extend to the grating writing section, thus avoiding interference with the acoustic signal.

[0084] If it is a "fiber end-face Fabry-Perot interferometer microcavity" structure:

[0085] Microcavity region at the fiber end face (microcavity length 20-50μm, reflective film is gold film, thickness 50-100nm): No coating is allowed to cover it, to avoid coating changing the cavity length (error must be <1nm) or refractive index (error must be <0.001), which would cause the interference signal to shift (shift amount must be <0.1pm).

[0086] The non-microcavity region on the outer side of the fiber end face (distance from the microcavity edge > 100 μm): coated with a magnesium fluoride (MgF2) antireflection coating (thickness 100-150 nm, matching the 1550 nm communication wavelength), prepared by magnetron sputtering (sputtering power 80 W, argon flow rate 20 sccm), which can reduce the optical reflectivity of the fiber end face from 4% to below 0.5%, reducing stray light interference on the interference signal; at the same time, the fixed section between the fiber and the external structure (length 500 μm) adopts the same modified epoxy coating as above to ensure the reliability of the fixation.

[0087] (2) Coating for environmental sensing functional area: matching parameter sensing requirements

[0088] Based on the monitored environmental parameters (temperature / humidity / pressure), select materials compatible with the coating of the acoustic sensing area, and combine them with corresponding preparation processes to achieve coating, avoiding chemical reactions or physical interference:

[0089] Temperature sensing area: A polyimide coating (thickness 500-800nm) is prepared by sol-gel method (monomer concentration 10%, pulling speed 10mm / s, curing at 150℃ for 2 hours). This coating is resistant to high temperature (long-term operating temperature -40-200℃), has a stable thermal conductivity (0.12W / (m•K)), and has good compatibility with the PDMS / modified epoxy coating of the acoustic sensing area (no peeling or dissolution after 72 hours at 80℃ and 95%RH).

[0090] Humidity sensing area: A nanoporous titanium dioxide coating (thickness 800-1200nm) is prepared by the sol-gel method (sol concentration 15%, spin coating speed 2000rpm, curing at 120℃ for 1 hour). This coating is an inorganic material that does not interact with the organic PDMS / modified epoxy coating, and has a moderate porosity (40%-60%), which can ensure the water molecule adsorption rate without causing excessive stress on the optical fiber due to water absorption and expansion (expansion rate <5%).

[0091] Pressure sensing area: A polyurethane elastic coating (1000-1500 nm thick) is prepared by drop coating (30% solids content, 1500 rpm spin coater, 80°C for 30 minutes). The elastic modulus (5-10 MPa) of this coating is similar to that of PDMS, exhibiting good deformation compatibility and strong adhesion to the modified epoxy coating (bonding strength > 8 N / cm). 2 The coating will not peel off due to pressure deformation.

[0092] 3. Coating compatibility and process control

[0093] Material compatibility verification: Before coating preparation, compatibility is verified by "adjacent coating test" - environmental sensing coating material and acoustic sensing coating material are coated adjacently on blank optical fiber (500μm spacing), and placed in a simulated throat environment (temperature 37℃, humidity 95%RH, pressure 10kPa) for 100 hours to observe whether the coating peels off, discolors, dissolves or other phenomena. Only material combinations without abnormalities are selected.

[0094] Segmented mask coating process: Use PTFE mask tape (temperature resistant to 200℃, chemical corrosion resistant) to cover the uncoated areas. After coating, gently peel off the mask with tweezers (to avoid damaging the optical fiber or the coating). The coating sequence follows the principle of "acoustic sensing area first, then environmental sensing area", and the curing temperature difference between adjacent coatings does not exceed 50℃ (e.g., after the PDMS coating is cured at 80℃, the polyimide coating in the environmental sensing area is cured at 150℃, with a 30-minute interval to cool down to 100℃ to avoid sudden temperature changes that could cause the coating to crack).

[0095] Thickness consistency control: High-precision coating equipment (microdroplet coating accuracy ±0.1μL, magnetron sputtering thickness control accuracy ±1nm) ensures that the coating thickness error is within ±1μm, and the thickness difference between different positions in the same functional area is <3%, avoiding fluctuations in sensing sensitivity due to uneven thickness (e.g., when the coating thickness deviation of temperature sensing exceeds 10%, the sensitivity fluctuation can reach more than 5%).

[0096] (vi) Methods for obtaining the target thickness of functional coatings

[0097] In this embodiment of the invention, the target thickness of the functional coating is determined by comprehensively calculating the target thickness of acoustic correction, the target thickness of temperature sensing, the target thickness of humidity sensing, and the target thickness of pressure sensing.

[0098] 1. Acoustic correction target thickness

[0099] The acoustic correction target thickness refers to the thickness of the functional coating that minimizes the correction error of the multi-parameter coupled correction model on the acoustic signal (the error between the corrected acoustic signal and the standard signal ≤ 3%) when the functional coating and the acoustic sensing unit share the same optical fiber. Its core function is to prevent excessive interference from the functional coating (temperature / humidity / pressure sensitive layer) of the environmental sensing unit due to inappropriate thickness (e.g., coating absorbing vibration energy, altering the acoustic response characteristics of the optical fiber). By optimizing the thickness, the coupled correction model can eliminate this interference to the greatest extent possible. The acoustic correction target thickness can be obtained through the following steps:

[0100] (1) Sample preparation:

[0101] Using optical fibers that can be reused with the acoustic sensing unit (such as single-mode optical fibers etched with fiber Bragg grating 11), and based on the functional coating material selected for the environmental sensing unit (such as polyimide + PDMS composite coating), a series of coating samples with different thicknesses were prepared, covering a thickness range of 500-1500 nm (coinciding with the thickness range of temperature / humidity / pressure coatings to ensure compatibility). A gradient was set at 100 nm intervals (such as 500 nm, 600 nm...1500 nm). Three parallel samples were prepared for each thickness, and the acoustic sensing unit structure of all samples was consistent (such as grating period of 530 nm and acoustic film thickness of 8 μm).

[0102] Control group: A fiber optic acoustic sensor sample without functional coating was prepared as an "interference-free reference" for acoustic signals.

[0103] (2) Performance testing:

[0104] Test environment: Simulates the physiological environment of the larynx (temperature 37℃, humidity 50%RH, pressure 10kPa) to avoid interference from environmental parameters on acoustic signals;

[0105] Signal input: Standard acoustic signals (covering typical clinical scenarios: breath sound signal (frequency 100-1000Hz, amplitude 0.1-1Pa), laryngospasm vibration signal (frequency 200-1500Hz, amplitude 0.5-2Pa)) are input to each sample through an acoustic signal generator.

[0106] Signal acquisition: The external optical demodulation unit is connected through the multi-core LC / APC connector 13 to synchronously acquire the "acoustic signal affected by coating interference" (i.e., the fiber grating wavelength shift signal after being affected by the functional coating) output by each sample, and at the same time acquire the "interference-free reference signal" output by the control group.

[0107] Data calculation: For the output signal of each thickness sample, substitute it into the "acoustic correction module" of the multi-parameter coupled correction model (this module was calibrated through previous experiments and includes the interference coefficient of coating thickness on acoustic signal) to obtain the "corrected acoustic signal";

[0108] Calculate the correction error: use the root mean square error (RMSE) to quantify the deviation between the corrected signal and the standard signal.

[0109] At the same time, the “signal attenuation rate” (the ratio of the amplitude of the interference signal before correction to the amplitude of the standard signal) of each sample is recorded, and samples with an attenuation rate > 20% are excluded (to avoid excessive coating thickness leading to severe loss of acoustic signal, exceeding the compensation capability of the correction model).

[0110] (4) Target thickness determination: Screen out all thickness ranges with “correction error ≤ 3% and signal attenuation rate ≤ 20%”; take the thickness with “minimum correction error” in the range as the acoustic correction target thickness (for example: when the functional coating is a polyimide + PDMS composite layer, the 800-1000nm range meets the requirements, and the correction error is the smallest (1.8%) at 900nm, so the acoustic correction target thickness is 900nm).

[0111] 2. Temperature sensing target thickness

[0112] The target thickness for temperature sensing refers to the minimum coating thickness that allows the temperature sensing sensitivity to meet a preset temperature performance threshold (e.g., sensitivity ≥ 8 pm / ℃, linearity error ≤ 2%), which is obtained through the following steps:

[0113] (1) Sample preparation: Based on the selected temperature-sensitive material (such as vanadium oxide), a series of coating samples with different thicknesses were prepared, covering a thickness range of 500-1200 nm, with a gradient set at 50 nm intervals (such as 500 nm, 550 nm, 600 nm...1200 nm). Three parallel samples were prepared for each thickness to ensure repeatability.

[0114] (2) Performance test: All samples were fixed in a constant temperature chamber, and the temperature change range was set to 30-40℃ (simulating the throat temperature range), the temperature step was 1℃, and the holding time was 5 minutes (to ensure the coating temperature is stable). The grating reflection wavelength offset of each sample was collected in real time through the optical demodulation unit.

[0115] (3) Data calculation: For the test data of each thickness sample, calculate the temperature sensing sensitivity and linearity error, and the temperature sensing sensitivity S T =△λ / △T, where △T is the temperature change. The linearity error is obtained by fitting the "temperature-wavelength" curve using the least squares method and calculating the percentage of the maximum deviation between the actual value and the fitted value relative to the full range.

[0116] (4) Target thickness determination: All thickness ranges with "sensitivity ≥ 8 pm / ℃ and linearity error ≤ 2%" are selected, and the minimum thickness in the range is taken as the target thickness for temperature sensing. For example, vanadium oxide coatings meet the performance threshold in the range of 600-900 nm, and the target thickness is taken as 600 nm, which ensures performance and reduces material usage.

[0117] 3. Humidity sensing target thickness

[0118] The target thickness for humidity sensing refers to the minimum coating thickness that allows the humidity sensing sensitivity to meet a preset humidity performance threshold (e.g., sensitivity ≥ 15 pm / %RH, response time ≤ 1 s). This is obtained through the following steps:

[0119] (1) Sample preparation: Based on the selected humidity-sensitive material (such as polyimide), a series of coating samples with different thicknesses were prepared, covering a thickness range of 600-1500nm, with a gradient set at 100nm intervals (such as 600nm, 700nm...1500nm), and 3 parallel samples were prepared for each thickness.

[0120] (2) Performance test: Place the sample in a humidity generator, set the humidity change range to 30%-95%RH, the humidity step size to 5%RH, and the equilibration time to 3 minutes (to ensure the humidity of the coating is stable). Collect the wavelength shift through the optical demodulation unit and record the response time from humidity change to wavelength stabilization.

[0121] (3) Data calculation: The humidity sensing sensitivity and response time of each thickness sample were calculated. At the same time, the surface morphology of the coating was observed by scanning electron microscopy to exclude samples whose coating cracked due to excessive thickness; humidity sensing sensitivity S H =△λ / △RH, where △RH is the change in humidity;

[0122] (4) Target thickness determination: Select the thickness range of “sensitivity ≥ 15pm / %RH, response time ≤ 1s and no cracking of coating”, and take the minimum thickness in the range as the target thickness of humidity sensing. For example, if the polyimide coating meets the requirements in the range of 800-1200nm, the target thickness is taken as 800nm ​​to balance sensitivity and response speed.

[0123] 4. Pressure sensing target thickness

[0124] The target thickness for pressure sensing refers to the minimum coating thickness that ensures the pressure sensing sensitivity meets a preset pressure performance threshold (e.g., sensitivity ≥ 4 pm / kPa, repeatability error ≤ 1%). Specific steps include:

[0125] (1) Sample preparation: Based on the selected pressure-sensitive material (such as PDMS), a series of coating samples with different thicknesses were prepared, covering a thickness range of 800-2000 nm, with a gradient set at 100 nm intervals (such as 800 nm, 900 nm...2000 nm), and 3 parallel samples were prepared for each thickness.

[0126] (2) Performance test: Fix the sample on the pressure testing machine, set the pressure variation range to 0-30kPa, the pressure step to 2kPa, and the holding time to 2 minutes (to ensure the coating deformation is stable). Collect the wavelength offset through the optical demodulation unit, and repeat the test 3 times for each pressure point.

[0127] (3) Data calculation: Calculate the pressure sensing sensitivity and repeatability error (relative standard deviation of 3 test results) for each thickness sample. At the same time, test the adhesion between the coating and the optical fiber using a tensile testing machine, and exclude samples with adhesion <3N / cm. 2The sample; pressure sensing sensitivity S P =△λ / △P, where △P is the pressure change;

[0128] (4) Target thickness determination: Select targets with "sensitivity ≥ 4 pm / kPa, repeatability error ≤ 1% and adhesion ≥ 3 N / cm". 2 The thickness range is defined as follows: the minimum thickness within this range is taken as the target thickness for pressure sensing (e.g., if the PDMS coating meets the requirements in the 1000-1500nm range, the target thickness is taken as 1000nm, taking into account both sensing performance and structural stability).

[0129] 5. Determining the target thickness

[0130] In this embodiment of the invention, a weighted scoring method is used, combining the importance of each parameter in clinical applications to set weights, and calculating the comprehensive score of each candidate thickness. The one with the highest score is the target thickness. The weights of the acoustically corrected target thickness, pressure-sensing target thickness, temperature-sensing target thickness, and humidity-sensing target thickness decrease sequentially, and in an illustrative embodiment, they are 0.35, 0.3, 0.2, and 0.15, respectively. Specifically, the target thickness is determined through the following steps:

[0131] (1) Determine the candidate thickness set based on acoustically corrected target thickness, pressure sensing target thickness, temperature sensing target thickness and humidity sensing target thickness.

[0132] In this embodiment of the invention, the candidate thickness set is a thickness set formed by each independent target thickness and the thickness of adjacent preset thickness ranges. Specifically, based on the independent target thickness of each of the four independent parameters (acoustics, temperature, humidity, and pressure), the thickness fluctuating by 100nm above or below each independent target thickness is also included, and finally merged into these 5 candidate thickness values: 700nm, 800nm, 900nm, 1000nm, and 1100nm. For example, if the independent target thickness of a certain parameter is 900nm, then 800nm, 900nm, and 1000nm will be included; if the independent target thickness of another parameter is 1000nm, then 900nm, 1000nm, and 1100nm will be included. Finally, duplicates are removed or integrated into these 5 candidate thickness values.

[0133] (2) Determine the score of each candidate thickness in the candidate thickness set based on the preset scoring rules.

[0134] For each candidate thickness, score it according to the following criteria (out of 100 points per parameter):

[0135] If a candidate thickness is equal to the independent target thickness of a certain parameter, it scores 100 points; if a candidate thickness (e.g., 800nm) is equal to the independent target thickness of a certain parameter (e.g., "temperature sensing"), then the candidate thickness (800nm) scores 100 points on the "temperature sensing" parameter.

[0136] If the deviation between the candidate thickness and the independent target thickness is less than or equal to 100nm, 80 points are awarded; if the absolute value (i.e., deviation) of the difference between a candidate thickness and the independent target thickness of a certain parameter is less than or equal to 100nm (and not equal to 0, because if it is equal to 0, 100 points have already been awarded according to the first condition), then 80 points are awarded for that parameter.

[0137] If the deviation between the candidate thickness and the independent target thickness is within a preset range, such as within 100-200nm, 60 points are awarded;

[0138] If the deviation of the candidate thickness from the independent target thickness is greater than the preset value, such as 200nm, it indicates that the compatibility is not met and a score of 0 is obtained.

[0139] (3) The candidate thickness with the highest score in the candidate thickness set is taken as the target thickness of the functional coating.

[0140] In this embodiment of the invention, the candidate thickness of 900nm achieved the highest overall score (92 points), and at this thickness:

[0141] The acoustic correction error is 1.8% (≤3%), the pressure sensitivity is 4.5 pm / kPa (≥4 pm / kPa), the temperature sensitivity is 8.2 pm / ℃ (≥8 pm / ℃), and the humidity sensitivity is 16 pm / %RH (≥15 pm / %RH), all of which meet the performance thresholds for each parameter.

[0142] The coating has optimal compatibility with the fiber optic multiplexing area (no need to adjust the fiber optic cabling or mask range due to thickness differences).

[0143] Therefore, the target thickness of the functional coating was determined to be 900 nm.

[0144] In this embodiment of the invention, the optical fibers of all acoustic sensors converge to the proximal end of the laryngeal mask through embedded channels in the airway duct wall, and are finally connected to the multi-core LC / APC connector 13. This connector is a standard quick-connect structure, which does not affect the conventional connection between the laryngeal mask and the ventilator circuit, and can transmit multiple optical signals simultaneously through the multi-core design. It is also compatible with sterilization processes and meets the requirements for single use.

[0145] In this embodiment of the invention, the signal processing unit is used to receive and demodulate the first optical response and the second optical response to obtain the original sensing signal containing multi-parameter coupling information, and input the original sensing signal and at least one known physical property parameter of the functional coating into a predefined multi-parameter coupling correction model to calculate at least two parameters among the acoustic signal, temperature value, humidity value and pressure value.

[0146] In this embodiment of the invention, the multi-parameter coupling correction model is the core algorithm module for achieving precise decoupling of acoustic, temperature, humidity, and pressure parameters. Because the physical properties of the functional coating (such as refractive index, thickness, and elastic modulus) are simultaneously affected by multiple environmental parameters (for example, temperature changes cause thermal expansion of the coating, altering its thickness and affecting the deformation sensitivity of the pressure sensor; humidity adsorption causes volume expansion of the coating, simultaneously interfering with the vibration transmission of the acoustic signal), the original sensing signal inevitably contains multi-parameter coupling terms (i.e., a single optical response signal simultaneously contains feature information of multiple parameters). Therefore, the model decouples the multi-parameter coupling terms in the original sensing signal by introducing correction factors strongly correlated with the physical properties of the functional coating; these correction factors include linear correction factors and / or nonlinear correction factors.

[0147] Furthermore, the linear correction factor is a coefficient matrix of a linear equation system constructed based on coating thickness, temperature coefficient, and humidity response coefficient, used to eliminate linear coupling interference between parameters. The linear correction factor is suitable for scenarios where parameter values ​​are within the routine clinical monitoring range, and the fluctuation range of parameters within this range meets the requirements for adapting the functional coating's linear response and linear coupling correction model. The clinical routine monitoring range is defined based on the physiological environment of the larynx and clinical safety standards, specifying parameter values ​​(temperature 35-40℃, cuff pressure 5-20kPa, airway humidity 40%-60%RH, acoustic signal frequency 100-1000Hz / amplitude 0.1-1Pa). The functional coating linear response requirement means that within this range, the linearity error of the coating's sensing characteristics (refractive index change, deformation, volume expansion rate, etc.) is ≤5%. The linear coupling correction model adaptation requirement means that cross-coupling interference between parameters can be completely decoupled through the coefficient matrix of the linear equation system, and the calculated parameter accuracy meets the preset threshold (temperature ±0.1℃, pressure ±0.2kPa, humidity ±2%RH, acoustic signal error ≤3%). The temperature parameter value is limited to the clinical routine airway temperature range of 35-40℃. Within this range, when the temperature fluctuates, the temperature coefficient of refractive index (dn / dT) of the temperature-sensitive coating (such as vanadium oxide) fluctuates by ≤5% (dn / dT = -2.5 × 10⁻⁶ at 35℃). -4 / ℃, at 40℃ dn / dT≥-2.4×10 -4The coupling interference between temperature and other parameters (pressure, acoustic signal) is linear and can be eliminated by a linear correction factor. The cuff pressure parameter is limited to a clinically safe sealing range of 5-20 kPa, and the laryngeal tissue contact pressure parameter is limited to a physiologically suitable range of 5-15 kPa. Within this range, when the pressure fluctuates, the deformation of the pressure-sensitive coating (such as PDMS) is linearly related to the pressure change (linearity error ≤2%), and the cross-coupling coefficient (kTP) between pressure and temperature is stable (the wavelength shift of the pressure signal caused by each 1°C temperature change is ≤2 pm), which is suitable for the linear coupling correction model. The humidity parameter is limited to the airway physiologically adapted humidity range of 40%-60%RH. Within this range, when humidity fluctuates, the volume expansion rate of the humidity-sensitive coating (such as polyimide) is linearly related to the humidity change (expansion rate = 0.06% / RH, linearity error ≤ 3%), with no saturation effect. Furthermore, the attenuation effect of humidity on the acoustic signal follows a linear law (the acoustic signal amplitude attenuation rate caused by each 10%RH humidity change is ≤ 1.5%). The acoustic signal parameter is limited to the normal respiratory sound range of 100-1000Hz frequency and 0.1-1Pa amplitude. Within this range, when the acoustic signal fluctuates, the linearity error between the vibration amplitude of the acoustically sensitive film (such as polyvinylidene fluoride) and the input signal amplitude is ≤ 3%. Moreover, the influence of environmental parameters (temperature, humidity) on the wavelength shift of the acoustic signal follows a linear relationship (the acoustic signal wavelength shift caused by each 1℃ temperature change is ≤ 1.2pm), which can be decoupled through the coefficient matrix of the linear equation system.

[0148] In this embodiment of the invention, the coefficient matrix of the linear equation system can be: .

[0149] Where A represents the acoustic correction target thickness, P represents the pressure sensing target thickness, T represents the temperature sensing target thickness, and H represents the humidity sensing target thickness. The left-hand vector represents the change in the optical response signal (e.g., ΔS1 is the grating wavelength shift, ΔS2 is the interference fringe shift, ΔS3 is the light intensity change, and ΔS4 is the phase change); the right-hand matrix is ​​the linear correction factor (coefficient matrix K), where the element kᵢⱼ represents the contribution coefficient of the unit change of the j-th parameter (e.g., T) to the i-th optical response signal (e.g., ΔS1); the right-hand vector represents the change in each parameter to be solved. The values ​​of i and j are 1 to 4.

[0150] Methods for calibrating the coefficient matrix may include:

[0151] Single-parameter calibration: Under the condition of keeping other parameters constant, change the target parameter alone (such as only changing the temperature, while keeping the humidity, pressure and acoustic signal constant), record the change in the optical response signal, and obtain a single coefficient (such as kᵀ1=ΔS1 / ΔT) by fitting using the least squares method.

[0152] Coating parameter correction: The elements of the coefficient matrix need to be dynamically adjusted based on the known physical properties of the functional coating. For example:

[0153] Thickness Correction: When the coating thickness increases from 900nm to 1000nm, the pressure response coefficient k p1 The value will be adjusted from 5 pm / kPa to 4.8 pm / kPa due to changes in coating stiffness (corrected by a pre-stored mapping table of thickness-stiffness-sensitivity).

[0154] Temperature coefficient correction: If the temperature coefficient (rate of change of refractive index with temperature) of the vanadium oxide coating changes from -2.5 × 10⁻⁶, then... -4 / ℃ changes to -2.3×10 -4 / ℃ (due to batch variations), then k T1 It needs to be adjusted to 0.92 times the original value.

[0155] In this embodiment of the invention, matrix inversion (K) can be used. -1 The parameter changes are solved from the optical response signal vector.

[0156] When parameter values ​​exceed the linear adaptation range of routine clinical monitoring, or when the functional coating exhibits nonlinear energy response characteristics within the monitoring range, a nonlinear correction factor needs to be introduced to compensate for the nonlinear coupling effect between parameters; where:

[0157] Parameters exceeding the linear fit range: This refers to the actual value of the parameter exceeding the boundary of the "clinical routine monitoring range and linear correction fit", specifically including:

[0158] Temperature parameters: Values ​​<35℃ or >40℃ (exceeding the normal fluctuation range of airway physiological temperature; the temperature coefficient of refractive index of vanadium oxide coating in the document fluctuates by more than 5% in this range, and the linearity error exceeds the threshold).

[0159] Pressure parameters: Cuff pressure > 20 kPa (exceeds the safe range of "sealing without damaging the mucosa", and the deformation and pressure linearity error of the PDMS coating under this pressure are > 2% in the document);

[0160] Humidity parameters: Values ​​<40%RH or >60%RH (exceeding the suitable humidity range for normal cilia movement of the airway mucosa; the polyimide coating in the document shows a non-linear decay in water absorption rate within this range).

[0161] Acoustic signal: frequency <100Hz or >1000Hz, amplitude >1Pa (exceeding the normal breath sound range, the acoustic diaphragm in the document shows nonlinear distortion in vibration response within this range);

[0162] The coating exhibits nonlinear energy response characteristics: this means that although the parameters are within the range of routine clinical monitoring, the sensing performance of the functional coating shows a nonlinear relationship with parameter changes, specifically including:

[0163] PDMS elastomer coating: Within the bladder pressure range of 5-20 kPa (normal range), if the pressure causes the coating deformation to be greater than 5% (large deformation scenario), its elastic modulus decreases nonlinearly from 1.5 MPa to below 1.0 MPa, causing the linear relationship between deformation and pressure to break down.

[0164] Polyimide coating: In the humidity range of 40%-60%RH (normal range), if the local humidity is instantaneously >60%RH (such as airway secretions dripping), the coating will exhibit a water absorption saturation effect, and the volume expansion rate will change from linear (0.06% / RH) to non-linear growth (the growth rate drops to below 0.02% / RH).

[0165] Vanadium oxide coating: Within the temperature range of 35-40℃ (normal range), if the rate of temperature change is >2℃ / min (such as a sudden rise in body temperature in a patient with high fever), the phase transformation process of the coating is intensified, and the refractive index change exhibits nonlinear fluctuations (dn / dT fluctuation >5%).

[0166] The nonlinear correction factor is a nonlinear mapping relationship obtained through polynomial fitting, exponential function or machine learning model, used to compensate for the nonlinear coupling effect between coating thickness and acoustic signal and environmental parameters.

[0167] In this embodiment of the invention, the nonlinear mapping relationship can be constructed using a data-driven method:

[0168] (1) Polynomial fitting mapping:

[0169] Suitable for weakly nonlinear scenarios (nonlinearity < 10%), it quantifies coupling relationships using high-order polynomials. For example, the cross-nonlinear coupling of temperature and pressure can be expressed as: ΔS 耦合 =a×△T×△P+b×(△T) 2 ×△P+c×(△P) 2 ×△T, where a, b, and c are nonlinear correction coefficients, obtained by collecting a large number of coupled signal samples over a wide parameter range (e.g., temperature 25-50℃, pressure 0-30kPa) and fitting them using the least squares method. The coefficient values ​​are strongly correlated with the coating thickness and material properties (e.g., when the PDMS coating thickness increases, the b value will change from 0.02 to 0.015, which needs to be corrected by the coating cross-sectional dimension parameters).

[0170] (2) Exponential function mapping:

[0171] Suitable for coupling scenarios with saturation characteristics (such as humidity sensing, where the rate of water absorption and swelling of polyimide slows down with increasing humidity when RH > 90%). For example: △S H =k H ×△RH×e -α×△RH α is a nonlinear correction factor (related to the coating porosity; α = 0.02 when the porosity is 60% and α = 0.03 when the porosity is 40%), which is determined by fitting experimental data and is used to compensate for sensitivity attenuation under high humidity.

[0172] (3) Machine learning model:

[0173] It is suitable for scenarios with strong nonlinearity and multi-factor coupling (such as acoustic signal distortion of composite coatings under the combined effects of temperature, humidity, and pressure). A neural network model (such as a 3-layer fully connected network) is used. The input is the original optical response signal and the physical parameters of the coating (thickness, refractive index, elastic modulus). The output is the nonlinear correction value for each parameter. The training process is as follows:

[0174] Training data: In a composite environmental chamber consisting of a temperature control chamber, a humidity generator, and a pressure testing machine, 10,000+ sets of "known parameter combinations (A, T, H, P) - optical response signals - coating parameters" samples were collected.

[0175] Model optimization: Minimize the mean squared error (MSE) between the predicted and actual values ​​using the Adam algorithm, and add L2 regularization to avoid overfitting;

[0176] Online correction: Input the current coating parameters in real time (such as dynamically updating the input through the previously calibrated thickness-temperature drift curve), and the model outputs nonlinear compensation to correct the preliminary results of linear decoupling.

[0177] The multi-parameter coupling correction model adopts a two-level architecture of "linear decoupling + nonlinear compensation". Combined with the known physical property parameters of the functional coating (thickness, cross-sectional dimensions, refractive index, coefficient of thermal expansion, etc.), it achieves full-process decoupling from the original signal to independent parameters. The specific steps are as follows:

[0178] Step 1: Raw signal preprocessing

[0179] The first and second optical responses (such as grating reflection spectra and interference fringe images) output by the optical demodulation unit are denoised (e.g., wavelet threshold denoising is used to remove Rayleigh scattering noise in fiber transmission), and baseline correction is performed (to eliminate baseline shift caused by temperature drift). Feature parameters (such as wavelength shift Δλ, fringe shift ΔN, and light intensity change ΔI) are extracted to form the original sensing signal vector S to be solved.

[0180] Step 2: Linear decoupling (based on linear correction factor)

[0181] Call the pre-stored linear coefficient matrix K (dynamically adjusted based on parameters such as the thickness and temperature coefficient of the current functional coating; for example, K corresponds to a thickness of 900nm). 900 A thickness of 1000nm corresponds to K 1000 );

[0182] The preliminary decoupling results A1, T1, H1, and P1, i.e., the linear estimates of each parameter, are obtained by matrix inversion.

[0183] Output linear decoupling residual (the difference between the actual signal and the linearly fitted signal) to determine whether nonlinear compensation is needed (nonlinear correction is initiated when the residual is >5%).

[0184] Step 3: Nonlinear compensation (based on nonlinear correction factor)

[0185] If the residual is small (≤5%), the linear decoupling result is used directly;

[0186] If the residual is large (>5%), apply the nonlinear correction factor for the corresponding scenario.

[0187] For the temperature-pressure cross-coupling, substitute the values ​​into the polynomial fitting formula to calculate the correction ΔT. n ΔP n We get T2 = T1 + ΔT n P2 = P1 + ΔP n ;

[0188] For humidity signals under high humidity conditions, the exponential function mapping is used to correct H2=H1×e. -α×H1 ;

[0189] For strong nonlinear distortion of acoustic signals, input to a neural network model, output acoustic correction ΔA n We get A2 = A1 + ΔA n ;

[0190] During the compensation process, coating cross-sectional dimension parameters (such as the width / thickness ratio of the AA cross section) are introduced in real time to correct the coefficients of the nonlinear factors (such as when the width / thickness ratio of the cross section is greater than 2, the polynomial coefficient a needs to be multiplied by a correction factor of 0.9).

[0191] Step 4: Parameter Verification and Output

[0192] Perform consistency verification on the decoupled parameters: For example, the trend of temperature and humidity changes should conform to physiological logic (such as the humidity of exhaled air usually increases when body temperature rises). If the deviation exceeds the threshold (such as temperature rises and humidity drops sharply by more than 10%RH), then backtrack to check whether the coating parameters are abnormal (such as whether local sensing deviation is caused by uneven thickness).

[0193] After successful verification, the system outputs parameter values ​​that meet the preset accuracy (acoustic signal accuracy ±3%, temperature ±0.1℃, humidity ±2%RH, pressure ±0.2kPa), which are used for subsequent alarm signal generation (such as triggering a cuff decompression alarm when the pressure is >25kPa), ventilator feedback control (such as adjusting the humidifier output according to the humidity value), and other scenarios.

[0194] To ensure decoupling accuracy, the multi-parameter coupled correction model needs to determine the specific parameters of the correction factor through a rigorous training and calibration process. The steps are as follows:

[0195] Calibration environment setup: Construct a comprehensive calibration platform containing the following modules, with each module achieving collaborative control through a central controller:

[0196] Temperature control module: It adopts a high-precision water bath constant temperature bath with a temperature adjustment range of 34-42℃, an adjustment step of 0.1℃, a heating rate of 5℃ / min, and real-time temperature feedback through a platinum resistance sensor, with a control accuracy of ±0.05℃.

[0197] Humidity control module: It adopts a dynamic humidity generator, with a humidity adjustment range of 30%-100%RH, an adjustment step of 1%RH, a response time of <1s, and real-time monitoring through a capacitive humidity sensor, with a control accuracy of ±1%RH;

[0198] Pressure control module: It adopts a pneumatic proportional pressure valve with a pressure adjustment range of 0-35kPa, an adjustment step of 0.1kPa, and a pressure holding accuracy of ±0.05kPa. The pressure value is collected in real time through a piezoelectric pressure sensor.

[0199] Acoustic signal generation module: It adopts a combination of miniature loudspeaker and power amplifier, with a frequency adjustment range of 100-5000Hz (10Hz step) and an amplitude adjustment range of 0.1-5Pa (0.05Pa step). The signal waveform supports sine wave and square wave (simulating breathing sound and laryngospasm vibration). The output amplitude is calibrated in real time through a sound level meter.

[0200] Sample collection: Three groups of functional coating samples from different batches were selected (coverage thickness deviation ±5%, material parameter tolerance range), and the samples were calibrated on the calibration platform according to L9(3). 4 The orthogonal experimental design was used to collect samples, and the experimental factors and levels are as follows:

[0201] Factor A (Temperature): 34℃, 37℃, 40℃;

[0202] Factor B (Humidity): 40%RH, 60%RH, 80%RH;

[0203] Factor C (Pressure): 5 kPa, 15 kPa, 25 kPa;

[0204] Factor D (acoustic signal frequency): 500Hz, 1500Hz, 3000Hz;

[0205] Each orthogonal experimental setup collected 3 parallel samples, simultaneously covering the boundary values ​​of each factor level (e.g., temperature 34℃ / 42℃, humidity 30%RH / 100%RH, pressure 0kPa / 35kPa), for a total sample size of 5000+ sets. Each sample was collected for 10 seconds at a sampling frequency of 1kHz. Each sample set included:

[0206] Input parameters: precise values ​​for acoustic signal frequency / amplitude, temperature, humidity, and pressure;

[0207] Output signal: The raw response of the optical demodulation unit (wavelength, phase, light intensity, etc.);

[0208] Coating parameters: real-time measured thickness (laser film thickness gauge), cross-sectional dimensions (microscope), and refractive index (ellipsometry).

[0209] Model training and optimization:

[0210] Linear coefficient matrix K: The initial matrix is ​​obtained by fitting with the least squares method through single-parameter calibration experiments, and then corrected by multi-parameter coupled samples (to eliminate the cross-influence between parameters).

[0211] Nonlinear factors: For polynomial / exponential functions, the coefficients are optimized through grid search; for neural network models, the training set and validation set are divided into 8:2 ratios, and iterative training is performed until the validation set MSE < 0.01.

[0212] Coating parameter mapping table: Establish lookup tables for "thickness-linear coefficient" and "section size-nonlinear coefficient" to support real-time dynamic correction;

[0213] Clinical validation: In animal experiments (pig throat model) and clinical trials (100 patients under general anesthesia), the calculation results of this model were compared with the measurements of traditional monitoring equipment (such as independent temperature sensors and pressure sensors) to verify the decoupling accuracy (the deviation must be ≤ the preset threshold). The model parameters were optimized based on clinical feedback (such as adjusting the coefficient of the humidity nonlinear factor for the characteristics of the laryngeal environment of pediatric patients).

[0214] Furthermore, the signal processing unit is integrated into the external monitoring host (connected to the smart laryngeal mask via a multi-core LC / APC connector 13). Its core consists of an FPGA chip (e.g., Xilinx XC7K325T), an ARM processor (e.g., STM32H743), and a data storage module. It features high-speed signal demodulation, multi-parameter decoupling, and data output functions. The specific workflow and details are as follows:

[0215] 1. Signal reception and demodulation

[0216] An external optical demodulation unit (with a built-in broadband light source and spectrometer) transmits a broadband optical signal with a center wavelength of 1550nm to optical fiber 5 via a multi-core LC / APC connector 13. After the optical signal is transmitted to each sensing unit via the optical fiber, the reflected light (first optical response, second optical response) carrying acoustic, temperature, humidity, and pressure information returns along the original path and is transmitted to the spectrometer via the connector. The spectrometer converts the optical signal into an electrical signal (analog signal) and transmits it to the FPGA chip for analog-to-digital conversion (sampling rate of 1MHz, resolution of 16 bits) to obtain the original sensing signal containing multi-parameter coupling information (such as the reflection wavelength data of the fiber Bragg grating and the fringe data of the Fabry-Perot interference).

[0217] 2. Multi-parameter coupling correction and solution

[0218] The ARM processor calls the multi-parameter coupling correction model pre-stored in the data storage module, and inputs the original sensing signal and the known physical property parameters of the functional coating (such as thickness, refractive index, elastic modulus, which are stored in the module through pre-shipment calibration) into the model to perform multi-parameter decoupling.

[0219] 3. Data Output and Application

[0220] The signal processing unit outputs the calculated parameters in the following ways to support clinical applications:

[0221] Local display and alarm: The parameters are transmitted to the display screen of the monitoring host (1024×600 resolution) to display the curves and values ​​of each parameter in real time; when the parameters exceed the preset threshold (such as temperature > 38℃, pressure > 25kPa), the ARM processor triggers the audible and visual alarm module (alarm sound pressure level ≥ 85dB, alarm light flashing red) to remind medical staff to intervene in time.

[0222] External device interaction: Parameters such as pressure and humidity are sent to the ventilator through the ventilator control interface (such as RS485 communication interface) so that the ventilator can automatically adjust ventilation parameters (such as adjusting the humidifier output according to airway humidity); at the same time, the parameters are wirelessly transmitted to an external monitoring terminal (such as a tablet computer) or cloud server through a Wi-Fi module (supporting 802.11b / g / n protocol) to realize remote monitoring and traceability of data.

[0223] Data storage: The calculated parameters are stored in the data storage module in CSV format (capacity ≥32GB), and can be exported via USB interface for clinical data analysis or medical record archiving. The data retention period is no less than 90 days.

[0224] Furthermore, the multi-parameter fiber optic measurement system also includes an application output unit; the application output unit is used to apply the calculated at least two parameters to at least one of the following: generating alarm signals related to the patient's ventilation status; or providing feedback control parameters for a ventilator or anesthesia machine.

[0225] The alarm signal generation is triggered by "parameter threshold abnormality," and multi-level alarm thresholds are preset in accordance with clinical safety standards to avoid false alarms or missed alarms. The specific implementation method is as follows:

[0226] 1. Threshold setting logic

[0227] Based on the "Guidelines for Airway Management in Anesthesiology" and clinical data, three levels of thresholds—"safe value," "warning value," and "danger value"—were set for each parameter. For example:

[0228] Pressure parameters: safe value (5-20 kPa, cuff pressure maintains airway seal and does not damage mucosa), warning value (20-25 kPa, indicating high cuff pressure), danger value (>25 kPa, may cause laryngeal mucosal ischemia).

[0229] Acoustic signals: safe value (breathing sound frequency 100-1000Hz, amplitude 0.1-1Pa, airway unobstructed), warning value (frequency <100Hz or >1500Hz, indicating airway stenosis), danger value (no obvious breath sounds or laryngospasm characteristic frequency 2000-3000Hz, indicating airway obstruction).

[0230] Temperature / Humidity: Safe temperature range (35-38℃, which is in line with the physiological temperature of the human airway) and safe humidity range (40%-60%RH, to avoid airway dryness or excessive humidification). Exceeding these ranges will trigger an alert.

[0231] 2. Presentation format of alarm signals

[0232] The design employs "audio-visual linkage + graded prompts" to adapt to complex clinical environments.

[0233] Sound alarm: The built-in speaker of the monitoring unit outputs alarm sounds of different frequencies (low-frequency beeping for warning value, with an interval of 2 seconds; high-frequency beeping for danger value, with continuous sound), and the volume is adjustable (60-90dB) to ensure that medical staff can recognize it in noisy environments;

[0234] Visual alarm: The corresponding parameter area on the main unit display screen flashes (yellow flashes for warning values, red flashes for danger values), and at the same time, a prompt indicating the reason for the abnormal parameter pops up (such as "Cuff pressure 26kPa, decompression recommended").

[0235] Remote push: Alarm information is synchronized to the nurse station monitoring screen and medical staff's mobile terminals (such as PDAs) via the hospital's local area network, including the patient's bed number, abnormal parameters and suggested treatment plan, with a delay of less than 1 second.

[0236] 3. Alarm suppression and reset mechanism

[0237] To avoid unnecessary interference, set up reasonable alarm control logic:

[0238] Delayed alarm: For parameters such as temperature and humidity that change slowly, a 5-second delay is set to trigger the alarm (e.g., if the temperature briefly fluctuates to 38.2℃ and then quickly drops back down, no alarm will be triggered).

[0239] Manual suppression: After medical staff confirm the alarm, they can temporarily suppress the sound alarm (visual alarm is retained) by pressing the button on the main unit. The suppression time can be set (1-5 minutes). If not handled within the time limit, it will automatically resume.

[0240] Automatic reset: When the parameters return to a safe value, the alarm signal is automatically cleared, and the host records the alarm event (time, parameter change curve, processing result) for easy traceability later.

[0241] Furthermore, providing feedback control parameters for ventilators or anesthesia machines specifically includes:

[0242] 1. Parameter filtering and priority sorting

[0243] Based on equipment type (ventilator / anesthesia machine) and clinical scenario (general anesthesia / ICU ventilation), key control parameters are selected and prioritized:

[0244] Ventilators: Prioritize the transmission of humidity (which directly affects airway mucosal protection), pressure (linked with cuff pressure and airway peak pressure), and acoustic signals (to determine changes in airway resistance); secondary transmission of temperature (to assist in adjusting the humidifier heating power).

[0245] Anesthesia machine: Prioritize the transmission of pressure (cuff pressure must match the anesthesia ventilation pressure) and acoustic signals (to monitor the risk of intraoperative laryngospasm), and secondarily transmit temperature (to avoid hypothermia affecting anesthesia recovery).

[0246] 2. Communication protocol and interface adaptation

[0247] Adopting a standardized communication solution common to medical devices ensures compatibility:

[0248] Hardware interface: Supports two physical interfaces: RS485 and Ethernet (TCP / IP). The ventilator / anesthesia machine is connected to the intelligent laryngeal mask monitoring host through a dedicated medical-grade cable. The interface has a design to prevent mis-insertion (different plug shapes correspond to different devices).

[0249] Communication protocol: Compatible with HL7 (Medical and Health Information Exchange Standard) and DICOM (Medical Digital Imaging and Communication Standard), parameter transmission uses an encrypted format (AES-128 encryption) to prevent data leakage;

[0250] Data update frequency: dynamically adjusted according to the rate of parameter change (pressure and acoustic signals are updated 10 times per second, and temperature and humidity are updated 2 times per second) to ensure the real-time adjustment of the equipment.

[0251] 3. Example of device adaptive adjustment logic

[0252] Taking a ventilator as an example, the following automatic control is achieved based on the parameters transmitted by the smart laryngeal mask:

[0253] Humidity control: When the airway humidity is detected to be <40%RH, the humidifier of the ventilator will automatically increase the heating power (from 37°C to 39°C) and increase the water supply of the humidifier tank (from 5mL / h to 8mL / h) until the humidity rises back to the 45%-55%RH range.

[0254] Pressure coordination: When the cuff pressure is >22kPa (close to the warning value), the ventilator automatically reduces the peak airway pressure (from 20cmH2O to 18cmH2O) and simultaneously sends a message to the main unit suggesting "manual adjustment of cuff pressure" to avoid tissue damage caused by the superposition of cuff and airway pressures.

[0255] Airway obstruction management: When the acoustic signal detects the characteristic frequency of laryngospasm (2000-3000Hz), the ventilator immediately stops positive pressure ventilation and switches to "pressure support ventilation mode" (PSV), reducing the ventilation pressure to 10cmH2O. At the same time, the main unit alarm is triggered, prompting medical staff to administer muscle relaxant medication.

[0256] Another embodiment of the present invention provides a respiratory monitoring system for the aforementioned smart laryngeal mask; the respiratory monitoring system includes:

[0257] An optical demodulation unit is used to transmit optical signals to the fiber optic sensing unit of the smart laryngeal mask, receive and demodulate the returned optical signals, and obtain a raw sensing signal containing coupled information of at least two parameters among sound, temperature, humidity and pressure.

[0258] The signal processing unit stores the multi-parameter coupling correction model and is configured to receive the original sensing signal and input the received original sensing signal and at least one known physical property parameter of the functional coating into the multi-parameter coupling correction model to calculate at least two parameters among the acoustic signal, temperature value, humidity value and pressure value.

[0259] The clinical decision unit is used to determine the patient's ventilation status based on the calculated parameters and generate an alarm signal when an abnormality is detected.

[0260] The optical demodulation unit comprises a broadband light source (center wavelength 1550nm, bandwidth 40nm, output power 10mW, stability ±0.1dB / hour), a spectrometer (resolution 0.01nm, sampling rate 1kHz), an optical isolator (to prevent reflected light from interfering with the stability of the light source), and a fiber optic adapter (compatible with multi-core LC / APC connectors, supporting hot-swapping). The signal processing unit uses a dual-core "FPGA+ARM" architecture (FPGA model Xilinx XC7K325T, responsible for high-speed signal preprocessing; ARM model STM32H743, responsible for model calculation and data interaction), paired with 32GB of industrial-grade flash memory (for storing model parameters, calibration data, and alarm logs). The clinical decision unit uses a two-dimensional approach of "rule base + trend analysis" to assess the patient's ventilation status. The rule base includes over 100 common clinical ventilation abnormalities (such as excessive cuff pressure, airway obstruction, and overhumidification), while trend analysis predicts potential risks (such as impending airway stenosis) through continuous 5-minute parameter change curves (such as continuously rising pressure and gradually decreasing acoustic signal frequency).

[0261] In this embodiment of the invention, the ventilation status is determined by comprehensively considering parameters such as acoustics, temperature, humidity, and pressure to assess the patient's airway patency, respiratory function coordination, and mechanical ventilation suitability. This includes airway patency status, ventilation efficiency status, mechanical ventilation suitability status, cuff sealing status, spontaneous breathing recovery status, and abnormal ventilation status.

[0262] A patent airway is a prerequisite for effective ventilation. Based on a comprehensive assessment of acoustic signals and pressure parameters, airway patency can be classified into three categories: fully patent, partially obstructed, and fully obstructed. In a fully patent state, the respiratory sound frequency is stable between 100-1000 Hz, with uniform amplitude between 0.1-1 Pa, and no abnormal noises such as wheezing or stridor. Airway resistance, calculated by the rate of change of pressure, is less than 10 cmH2O / (L·s), and the cuff pressure is maintained at 5-20 kPa, ensuring a seal without compressing the mucosa. At this time, air can pass smoothly through the airway during spontaneous breathing or mechanical ventilation, without the risk of stenosis or obstruction. Partial obstruction is a warning level. Breathing sounds will shift to below 100Hz or above 1500Hz, and intermittent wheezing will occur. Airway resistance will rise to 10-20 cmH2O / (L·s), and cuff pressure fluctuations will increase to ±5 kPa. This indicates mild airway narrowing, possibly early laryngospasm or partial airway obstruction by secretions. Secretions should be cleared promptly or the laryngeal mask airway position adjusted to prevent progression to complete obstruction. Complete obstruction is a dangerous level. Breathing sounds will disappear or only high-frequency laryngospasm characteristic sounds at 2000-3000Hz will appear. Airway resistance will be greater than 20 cmH2O / (L·s), and cuff pressure will surge to over 25 kPa. At this point, the airway is completely blocked, possibly due to laryngospasm or laryngeal mask airway displacement compressing the airway. Immediate emergency measures are required; otherwise, hypoxia and suffocation may occur.

[0263] Ventilation efficiency is indirectly determined by the "physiological adaptability" of the gas in the airway through temperature and humidity parameters. Under high-efficiency ventilation, the airway temperature is stable at 35-38℃, matching the body's core temperature, which avoids cold or heat stimulation to the airway mucosa. The relative humidity of the airway is maintained at 40%-60%RH, which is within the optimal humidity range for normal mucosal cilia movement, facilitating the discharge of secretions. At this time, the inhaled air, after being "temperature and humidity regulated" by the laryngeal mask and airway, meets the body's physiological needs, reducing the risk of complications such as lung infection and airway dryness. In inefficient ventilation, abnormal temperatures may occur. Temperatures below 35°C may indicate insufficient heating of the humidifier, while temperatures above 38°C may indicate overheating. Humidity may also be abnormal. Temperatures below 40%RH can lead to dry airway mucosa, weakened ciliary movement, and dried secretions. Temperatures above 60%RH can cause condensation in the airway, increasing the risk of infection and affecting gas flow. In such cases, ventilation efficiency decreases. Even if the airway is clear, lung function may be impaired due to uncomfortable air temperature and humidity. It is necessary to adjust the humidifier's temperature, water supply, and other parameters.

[0264] Mechanical ventilation compatibility is assessed for patients using ventilators or anesthesia machines, reflecting the "synergy" between the laryngeal mask airway and the mechanical ventilation equipment. This is determined by pressure and acoustic signals to determine if ventilation parameters are compatible. In the compatible state, the ventilator's delivery pressure matches the cuff pressure. When the peak delivery pressure is less than 20 cmH2O, the cuff pressure is less than 20 kPa, avoiding pressure superposition that could damage the mucosa. There is also no 1500-2000 Hz "airflow impact sound," and breath sounds are synchronized with the ventilator's delivery rhythm. At this point, the mechanical ventilation parameters match the patient's airway resistance and lung compliance, resulting in a smooth ventilation process and reducing patient-ventilator asynchrony. In a mismatch state, pressure conflict will occur. When the peak delivery pressure is greater than 20 cmH2O, the cuff pressure is greater than 25 kPa. The pressure superposition can easily lead to airway mucosal ischemia and acoustic abnormalities, including high-frequency airflow impact sounds. Breathing sounds and the rhythm of ventilator delivery will also be disordered. For example, if the patient's spontaneous breathing rate is greater than the ventilator's set frequency, it will lead to "re-inhalation of gas". At this time, the patient-ventilator asynchrony is obvious, which may lead to insufficient ventilation or hyperventilation. It is necessary to adjust the ventilator parameters or optimize the laryngeal mask fixation method.

[0265] The cuff sealing status applies only to smart laryngeal masks with cuffs. Pressure parameters determine whether the cuff achieves an "effective seal." In an effective seal, the cuff pressure is stable at 8-20 kPa, with pressure fluctuations within ±2 kPa during the respiratory cycle. The ventilator-monitored "leakage" is less than 50 mL / respiratory cycle. The cuff fits tightly against the airway wall, ensuring no gas leakage during mechanical ventilation and guaranteeing accurate tidal volume delivery, preventing environmental pollution or insufficient ventilation due to anesthetic gas leakage. In a seal failure state, the cuff pressure will drop below 8 kPa, possibly due to cuff leakage or insufficient inflation, with leakage exceeding 100 mL / respiratory cycle. A sudden drop in cuff pressure, such as from 15 kPa to 5 kPa, may also occur, indicating cuff rupture. In this case, mechanical ventilation efficiency decreases significantly, requiring immediate cuff inflation or replacement of the laryngeal mask.

[0266] The recovery status of spontaneous breathing is assessed for postoperative and resuscitation patients by using acoustic signals and pressure changes to determine the degree of recovery of their spontaneous breathing function, thus guiding weaning from mechanical ventilation. In a good recovery state, spontaneous breath sounds are clear, with a frequency of 12-20 breaths / minute, a stable amplitude of 0.5-1 Pa, and gradually synchronized with the ventilator-assisted ventilation rhythm. Airway pressure fluctuations during spontaneous breathing are regular, decreasing during inspiration and increasing during expiration, with no abnormal pressure peaks. At this stage, the patient's spontaneous breathing function is gradually recovering, and the ventilator support intensity can be gradually reduced to prepare for weaning. In a poor recovery state, spontaneous breath sounds are weak, with an amplitude less than 0.3 Pa, a frequency less than 10 breaths / minute or greater than 30 breaths / minute, and cannot be synchronized with the ventilator. Airway pressure fluctuations during spontaneous breathing are disordered, exhibiting "negative pressure wheezing," indicating insufficient respiratory muscle strength. In this case, spontaneous breathing function has not reached the target level, and ventilator support needs to be maintained while investigating the cause of poor recovery.

[0267] Abnormal ventilation states encompass rare but dangerous ventilation abnormalities in clinical practice, requiring multi-parameter analysis for accurate diagnosis. In laryngospasm, a characteristic high-frequency vibration sound of 2000-3000Hz will be present in the acoustic signal, accompanied by a sudden increase in airway pressure exceeding 30cmH2O and weakened or absent breath sounds. This is due to strong laryngeal muscle contraction leading to airway narrowing, commonly seen during general anesthesia recovery or when the airway is irritated. Immediate intravenous administration of muscle relaxants is necessary to relieve the spasm. In cases of excessive cuff compression, where cuff pressure consistently exceeds 25kPa, an ischemic murmur of 500-800Hz will be present in the acoustic signal. Airway narrowing is caused by mucosal ischemia and edema. Excessive cuff pressure can lead to prolonged ischemia of the laryngeal mucosa, potentially causing mucosal damage, ulceration, or even perforation. Immediate release of cuff gas and adjustment of the laryngeal mask airway position are required.

[0268] Furthermore, the clinical decision-making unit also transmits the calculated parameters to an external monitoring terminal or cloud server via wireless communication.

[0269] Furthermore, it also includes a ventilator control interface; the ventilator control interface is used to send the alarm signal or the calculated parameters to the ventilator so that the ventilator can automatically adjust the ventilation parameters.

[0270] The respiratory monitoring system provided in this embodiment can monitor key respiratory parameters in real time, synchronously, and accurately, and promptly identify respiratory dysfunction caused by abnormal ventilator status or laryngeal mask position. This provides crucial decision support for clinical practice, enabling early intervention, effectively preventing malignant events such as hypoventilation, ensuring patient safety, and achieving preventative respiratory management. This embodiment also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the methods described in this embodiment.

[0271] This invention also provides a computer-readable storage medium storing computer-executable instructions for performing the methods described in this invention.

[0272] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0273] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A smart laryngeal mask, characterized in that, include: The laryngeal mask body and the multi-parameter fiber optic measurement system, the system comprising: The fiber optic sensing unit comprises at least one segment of optical fiber embedded within the laryngeal mask body, and is used to sense physiological signals in the laryngeal region; the fiber optic sensing unit includes: The acoustic sensing unit is used to generate a first optical response in response to sound wave vibrations; An environmental sensing unit has a functional coating on its surface that is sensitive to at least one of the environmental physical parameters, namely temperature, humidity and pressure, and is used to generate a second optical response in response to changes in environmental parameters. The signal processing unit is used to receive and demodulate the first optical response and the second optical response to obtain the original sensing signal containing multi-parameter coupling information, and input the original sensing signal and at least one known physical property parameter of the functional coating into a predefined multi-parameter coupling correction model to calculate at least two parameters among the acoustic signal, temperature value, humidity value and pressure value. The multi-parameter coupling correction model decouples the multi-parameter coupling terms in the original sensing signal by introducing correction factors related to the physical property parameters of the functional coating; the correction factors include linear correction factors and / or nonlinear correction factors.

2. The intelligent laryngeal mask according to claim 1, characterized in that, The acoustic sensing unit can be any of the following structures: a structure inscribed with a fiber Bragg grating and attached with an acoustically sensitive thin film on an optical fiber; or a Fabry-Perot interference microcavity structure fabricated on the end face of an optical fiber.

3. The intelligent laryngeal mask according to claim 1, characterized in that, The linear correction factor is a coefficient matrix of a linear equation system constructed based on coating thickness, temperature coefficient, and humidity response coefficient, used to eliminate linear coupling interference between parameters.

4. The intelligent laryngeal mask or respiratory monitoring system according to claim 1, characterized in that, The nonlinear correction factor is a nonlinear mapping relationship obtained through a data-driven method, used to compensate for the nonlinear coupling effect between coating thickness and acoustic signals and environmental parameters.

5. The intelligent laryngeal mask according to claim 1, characterized in that, The target thickness of the functional coating is determined based on the acoustically corrected target thickness, the temperature-sensing target thickness, the humidity-sensing target thickness, and the pressure-sensing target thickness; wherein: The acoustic correction target thickness is the coating thickness that minimizes the correction error of the multi-parameter coupled correction model on the acoustic signal. The temperature sensing target thickness is the coating thickness that makes the temperature sensing sensitivity meet the preset temperature performance threshold. The humidity sensing target thickness is the coating thickness that makes the humidity sensing sensitivity meet the preset humidity performance threshold. The pressure sensing target thickness is the coating thickness that makes the pressure sensing sensitivity meet the preset pressure performance threshold.

6. The intelligent laryngeal mask according to claim 1, characterized in that, The multi-parameter fiber optic measurement system also includes an application output unit; the application output unit is used to apply the calculated at least two parameters to at least one of the following: generating alarm signals related to the patient's ventilation status; or providing feedback control parameters for a ventilator or anesthesia machine.

7. A respiratory monitoring system, characterized in that, The intelligent laryngeal mask according to any one of claims 1-6; the respiratory monitoring system includes: An optical demodulation unit is used to transmit optical signals to the fiber optic sensing unit of the smart laryngeal mask, receive and demodulate the returned optical signals, and obtain a raw sensing signal containing coupled information of at least two parameters among sound, temperature, humidity and pressure. The clinical decision unit is used to determine the patient's ventilation status based on the calculated parameters and generate an alarm signal when an abnormality is detected. The signal processing unit of the intelligent laryngeal mask stores the multi-parameter coupling correction model and is configured to receive the original sensing signal and input the received original sensing signal and at least one known physical property parameter of the functional coating into the multi-parameter coupling correction model to calculate at least two parameters among the acoustic signal, temperature value, humidity value and pressure value. The multi-parameter coupling correction model decouples the multi-parameter coupling terms in the original sensing signal by introducing correction factors related to the physical property parameters of the functional coating; the correction factors include linear correction factors and / or nonlinear correction factors.

8. The respiratory monitoring system according to claim 7, characterized in that, The clinical decision unit also sends the calculated parameters to an external monitoring terminal or cloud server via wireless communication.

9. The respiratory monitoring system according to claim 7, characterized in that, It also includes a ventilator control interface; the ventilator control interface is used to send the alarm signal or the calculated parameters to the ventilator so that the ventilator can automatically adjust the ventilation parameters.

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