Bite block capable of monitoring breathing state in real time

Abstract

The invention provides a bite block capable of monitoring the breathing state in real time, relates to the technical field of medical monitoring equipment, and solves the problem that an existing bite block cannot accurately monitor abnormal breathing in real time and cannot give an early warning in time. According to the bite block, a bite block body extending forwards from the lower end face of an occlusal surface is integrated with a dual-mode combined sensor, namely a pressure sensor and a temperature sensor, pressure and temperature changes of respiratory airflow are detected, signals are converted into electric signals through a signal conversion module, data fusion and analysis are carried out through a signal processing module, and respiratory mode waveforms are generated; the feedback system judges the breathing state according to the waveform characteristics and triggers the alarm device when no breathing exists or breathing is abnormal. By means of the multi-mode sensing and intelligent analysis technology, dynamic capture of respiration parameters and instant response of abnormal events are achieved, the system is suitable for the scenes of digestive endoscopy, sleep apnea syndrome monitoring, postoperative respiration monitoring and the like, and the real-time performance and reliability of medical monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical monitoring equipment, and more specifically, to a dental pad for real-time monitoring of respiratory status. Background Art

[0002] In the field of medical monitoring, bite block, as a common oral device, plays an important role in digestive endoscopy. Traditionally, bite block is used to prevent patients from grinding their teeth and maintain airway patency. It also prevents the endoscope from damaging the oral cavity and assists in fixing the endoscope during digestive endoscopy. However, with the increasing demand for real-time monitoring of patients' respiratory status during digestive endoscopy, existing bite block for endoscopy has exposed many limitations in functional design.

[0003] During digestive endoscopy, patients need to continuously monitor their respiratory rate, airflow pattern, and abnormal events (such as apnea and hypopnea) during the examination. However, existing dental pads for endoscopic examinations lack integrated, high-precision respiratory monitoring capabilities. At present, this type of monitoring usually relies on external sensors, such as chest-belt respiratory sensors, nasal airflow catheters, etc. In actual use, these external devices will cause discomfort to patients undergoing endoscopic examinations, and due to the slight movement of the patient during the examination, the signal is easily disturbed by motion, and it is difficult to achieve long-term continuous and stable monitoring. Digestive endoscopy often takes a certain amount of time, and stable respiratory monitoring data during this period is crucial. In addition, the oral environment is complex, and traditional monitoring methods face technical bottlenecks in miniaturization and multimodal sensor integration, making it impossible to directly obtain the dynamic characteristics of respiratory airflow through dental pads, which is extremely unfavorable for timely grasping the patient's respiratory status during endoscopic examinations.

[0004] Specifically, in the prior art, attempts to integrate respiratory sensors into dental pads for endoscopic examinations mostly use a single sensing mode. For example, some designs use pressure sensors to detect changes in airflow pressure on the occlusal surface, but the sensor material and structural design limit its sensitivity, and its response to weak airflow (such as shallow breathing) is poor, and it is unable to distinguish the temperature difference between the respiratory phases (inhalation and exhalation). Other solutions use temperature sensors to capture temperature fluctuations in respiratory airflow, but the temperature in the oral cavity is significantly affected by environmental factors, such as saliva evaporation and changes in ambient temperature. A single temperature signal is prone to drift, which can lead to misjudgment. The limitations of this single-modal data make the analysis of respiratory patterns lack multi-dimensional information support, making it difficult to accurately identify abnormal conditions such as apnea and respiratory rhythm disorders during digestive endoscopy, affecting the protection of patient safety.

[0005] Furthermore, the signal processing and feedback mechanisms of existing dental pads for endoscopic examinations also have the problem of insufficient efficiency. During the sensor signal conversion process, the analog signal is easily interfered by circuit noise, and the dynamic characteristics of the respiratory signal (such as periodicity and nonlinearity) are not fully considered, resulting in reduced signal fidelity. Some schemes use fixed thresholds to judge respiratory abnormalities, but do not combine individualized baseline data (such as patients' daily respiratory parameters). Due to the large physiological differences between different patients, this method cannot adapt to the actual situation of different patients in digestive endoscopy, and is prone to missed reports or false alarms. In addition, traditional algorithms have limited ability to fuse multi-source data, and it is difficult to extract key features of the respiratory pattern (such as waveform decline slope, phase synchronization) from the temporal correlation of pressure and temperature signals, which seriously limits the real-time and accuracy of abnormal event detection in digestive endoscopy, and is not conducive to timely detection and treatment of possible abnormal respiratory conditions in patients.

[0006] From the perspective of technical implementation, there is a contradiction between the miniaturization, multimodal sensor integration and low-power design of existing dental pads for endoscopic examination. The space in the oral cavity is small. When performing endoscopic examination, the sensor, signal processing circuit and power module need to be accommodated in a limited volume at the same time, which puts extremely high requirements on the miniaturization packaging of the device. If a rigid circuit board is used, it will greatly affect the wearing comfort of the dental pad, which is difficult for patients who need to hold the dental pad for a long time to cooperate with endoscopic examination; while the flexible circuit can better adapt to the oral morphology, its conductive stability and anti-fatigue performance still need to be further optimized. In addition, medical-grade materials need to meet biocompatibility requirements (such as non-cytotoxicity and resistance to saliva corrosion), which greatly limits the selection range of sensor active materials. For example, some high-sensitivity piezoresistive materials (such as metal strain gauges) are difficult to be compatible with the flexible dental pad body due to their high hardness, while flexible conductive materials (such as conductive polymers) have problems such as poor long-term stability and sensitivity attenuation. These technical difficulties make it difficult for existing dental pads for endoscopic examination to take into account both high-precision monitoring and patient comfort requirements, which seriously limits its popularization and application in clinical digestive endoscopy monitoring. Summary of the invention

[0007] One purpose of the present invention is to provide a dental pad for real-time monitoring of respiratory status, which can solve the problem that existing dental pads lack the function of real-time monitoring of respiratory status and cannot accurately capture respiratory abnormalities (such as apnea, rhythm disorders), resulting in medical monitoring relying on external equipment, and there are problems such as wearing discomfort and signal interference.

[0008] Traditional sensor materials have low sensitivity and are difficult to detect weak respiratory airflow pressure (such as shallow breathing) and temperature changes, and cannot distinguish temperature differences in respiratory phases, resulting in insufficient data reliability. The occlusal surface design of the dental pad is single and cannot adapt to individual differences in the degree of mandibular protrusion of patients, affecting wearing comfort and monitoring accuracy. The connection method between the sensor and the circuit is easily affected by oral humidity. The traditional rigid circuit board causes discomfort in wearing, and the waterproof performance is insufficient, affecting the stability of long-term use. Respiratory signal processing does not combine individual baseline data, and the degree of standardization is low, resulting in large signal analysis errors between different patients and inability to adapt to physiological differences. The timing of pressure and temperature signals is not aligned, and multi-source data fusion is difficult. It is difficult to extract key features of the respiratory pattern (such as peak interval and waveform decline slope), which affects the accuracy of abnormal event detection. The feature fusion weight is fixed, and the actual data distribution is not adapted through the optimization algorithm. In addition, the determination of abnormal exhalation events lacks auxiliary verification parameters, which is prone to false alarms. Respiratory waveform generation relies on single signal interpolation, and does not combine deep learning to extract multidimensional parameters, resulting in poor waveform continuity and inability to accurately reflect respiratory rhythm and phase. Too strong rigidity or insufficient friction coefficient of the dental pad body can easily cause displacement or slippage during wearing, affecting the stability of the sensor signal and poor long-term wearing comfort. The density of the traditional porous silicone layer is evenly distributed, which cannot balance the buffering and support performance, resulting in uneven distribution of bite force and affecting the durability of the dental pad structure.

[0009] To this end, the present invention provides a mouth pad for real-time monitoring of respiratory status, which comprises:

[0010] The tooth pad body has an occlusal surface designed as a structure in which the lower end surface protrudes forward relative to the upper end surface;

[0011] A dual-mode combined sensor, which is integrated in the dental pad body, includes a pressure sensor and a temperature sensor, the pressure sensor is used to detect the respiratory airflow pressure signal, and the temperature sensor is used to detect the respiratory airflow temperature signal;

[0012] A signal conversion module, which is electrically connected to the dual-mode combination sensor, and is used to convert the pressure signal and the temperature signal into an electrical signal;

[0013] A signal processing module, which is used to perform data fusion and analysis on the electrical signal and generate a breathing pattern waveform;

[0014] A feedback system is connected to the signal processing module. The feedback system is used to determine the breathing state according to the breathing pattern waveform and trigger an alarm device when no breathing or abnormal breathing is detected.

[0015] Preferably, the dual-mode sensor for real-time monitoring of respiratory status uses three-dimensional buckled carbon nanofiber material as an active material, wherein:

[0016] The pressure sensor is composed of a three-dimensional porous network structure of carbon nanofiber material. The piezoresistive response sensitivity of the pressure sensor is 0.1 to 1.5 kPa-1, and it detects a respiratory airflow pressure signal of 0.1 to 5 kPa.

[0017] The temperature sensor is composed of a carbon nanofiber composite layer doped with graphene quantum dots. The temperature sensor has a thermal resistance temperature coefficient TCR of -2.5 to -4.5% / °C, a response time of 50 to 200ms, and detects a respiratory airflow temperature signal of 0.1 to 2°C.

[0018] Preferably, the lower end surface of the tooth pad for real-time monitoring of respiratory status extends forward by 2 mm, 5 mm or 8 mm relative to the upper end surface.

[0019] Preferably, in the dental pad for real-time monitoring of respiratory status, the dual-mode combination sensor and the signal conversion module are connected through a flexible printed circuit board, the flexible printed circuit board adopts a polyimide substrate and integrates a silver nanowire conductive layer with a thickness of ≤0.1mm; the surface of the flexible circuit is covered with two layers of 0.05mm thick medical-grade silicone waterproof film.

[0020] Preferably, in the bite block for real-time monitoring of respiratory status, the step of the signal processing module fusing and analyzing the electrical signal comprises:

[0021] The pressure signal and the temperature signal are subjected to low-pass filtering with a cut-off frequency of 10 Hz and digital band-pass filtering with a passband range of 0.1 to 2 Hz;

[0022] Based on the mean and standard deviation of the patient's baseline data, the filtered raw signal is normalized (i.e., a distribution with a mean of 0 and a standard deviation of 1):

[0023]

[0024] The baseline data refers to the data of the pressure signal and temperature signal of the respiratory airflow output by the signal conversion module, which are continuously sampled at a frequency of 100 Hz within the first 10 seconds after the dual-mode combination sensor is activated and filtered. raw is the original signal after filtering, corresponding to the original data of the pressure sensor (unit: kPa) and the temperature sensor (unit: °C) after filtering; μ baseline is the mean of the baseline data, σ baseline is the standard deviation of the baseline data, S norm is the normalized signal.

[0025] Preferably, in the dental pad for real-time monitoring of respiratory status, the step of data fusion and analysis of the electrical signal by the signal processing module further includes: aligning the timing of the normalized pressure signal and the temperature signal through a dynamic time warping algorithm, compensating for the inherent delay of the temperature sensor, and outputting the aligned pressure signal P′(t) and the temperature signal T′(t), with the timing error not exceeding 5ms (milliseconds); extracting the peak interval Δt from the aligned pressure signal P′(t) peak (unit: times / min), amplitude A1 (unit: kPa), waveform falling slope S down (unit: kPa / s), and extract the standard deviation of fluctuation σ from the aligned temperature signal T′(t) T (Unit: °C), and rising edge time t rise , (unit: ms); construct the fusion feature value F fused , (used to comprehensively evaluate the stability of respiratory status) formula is:

[0026]

[0027] Among them, A baseline 、T cycle , σ baseline are the amplitude mean of the pressure signal, the respiratory cycle mean, and the standard deviation of the temperature signal from the baseline data, respectively.

[0028] Preferably, in the dental pad for real-time monitoring of respiratory status, the step of the signal processing module fusing and analyzing the electrical signal further comprises: determining in the training data set by a particle swarm optimization algorithm: w1=0.6, w2=0.3, and w3=0.1;

[0029] The fusion eigenvalue F fused With the preset threshold F threshold Compare:

[0030] If F fused >F threshold , determined as abnormal respiratory intensity;

[0031] If F fused <0.5·F threshold , determined as abnormal respiratory rate;

[0032] At the same time, the waveform slope S down As an auxiliary verification parameter, if S down <0.5 kPa / s, it is marked as an abnormal exhalation event;

[0033] When abnormal breathing intensity, abnormal breathing frequency or abnormal exhalation events are detected, the alarm device is triggered.

[0034] Preferably, in the bite block for real-time monitoring of breathing status, the specific steps of the signal processing module generating the breathing pattern waveform include:

[0035] Based on the aligned pressure signal P′(t) and temperature signal T′(t), the convolutional neural network model is used to extract features from the time series data and output multidimensional waveform parameters including respiratory rhythm, amplitude and phase.

[0036] The multi-dimensional waveform parameters are input into the waveform synthesizer, and the discrete parameters are processed into a continuous state using the piecewise cubic Hermite interpolation algorithm to generate a real-time breathing pattern waveform;

[0037] The horizontal axis of the breathing pattern waveform is time t, and the vertical axis is the weighted fusion value W(t) of the normalized pressure and temperature signals (the fusion value W(t) is a real-time waveform generation parameter). The calculation formula is:

[0038] W(t)=α·P′(t)+β·T′(t)

[0039] Among them, α=0.7, β=0.3, and α+β=1, and the weight value is determined by optimizing the clinical data set validation.

[0040] Preferably, the dental pad for real-time monitoring of respiratory status comprises:

[0041] The surface anti-slip texture structure is composed of a honeycomb micro-protrusion array distributed on the occlusal surface. The height of a single protrusion is 0.3-0.8mm, and the distance between adjacent protrusions is 1-2mm. The material is medical grade silicone, with a Shore hardness of 20-30A, and the occlusal surface friction coefficient ≥0.6;

[0042] The internal flexible support layer is a gradient elastic structure, comprising:

[0043] The surface buffer layer is made of a superelastic polyurethane material with an elastic modulus of 0.5 to 1.5 MPa and a thickness of 1 to 2 mm;

[0044] The middle transition layer is made of porous silica gel with a porosity of 30-50% and a thickness of 3-5 mm;

[0045] The bottom rigid frame is made of biocompatible polycarbonate with an elastic modulus of 2 to 3 GPa and a thickness of 0.5 to 1 mm.

[0046] Preferably, in the dental pad for real-time monitoring of respiratory status, the porous silica gel in the middle transition layer has a gradient density distribution along the thickness direction, the porosity decreases linearly from 50% to 30% from the surface layer to the bottom layer, and the average pore diameter gradually decreases from 200μm to 50μm; the gradient density distribution is achieved by a 3D printing process.

[0047] Beneficial Effects

[0048] The present invention integrates dual-mode sensors and feedback systems to achieve real-time dynamic monitoring of respiratory status, accurately capture abnormal events such as apnea and hypopnea, reduce dependence on external devices, and improve patient wearing comfort and monitoring continuity.

[0049] The present invention uses a three-dimensional buckled carbon nanofiber material and a graphene quantum dot composite layer to significantly improve the sensitivity of the sensor (the piezoresistive response reaches 0.1-1.5 kPa). -1 ) and temperature resolution (0.1 degrees), effectively distinguishing the difference in inhalation / exhalation temperature and enhancing data reliability.

[0050] The present invention adopts a multi-model protrusive design to adapt to the mandibular anatomical characteristics of different patients, ensures that the tooth pad fits tightly with the occlusal surface, reduces displacement interference, and improves sensor signal stability and monitoring accuracy.

[0051] The flexible circuit and sandwich packaging structure (thickness ≤ flexible circuit and sandwich) of the present invention takes into account both miniaturization and waterproof performance (IPX7 level), avoids circuit failure caused by oral humidity, and prolongs the service life of the equipment.

[0052] The present invention eliminates the influence of individual physiological differences based on signal standardization of baseline data, improves the consistency of data analysis between different patients, and reduces the misjudgment rate.

[0053] The dynamic time warping algorithm (error ≤ dynamic time warping) of the present invention realizes accurate timing alignment of pressure and temperature signals, combined with multi-feature extraction (such as Δ peak , σ T ), enhancing the multi-dimensional information support for breathing pattern recognition.

[0054] The particle swarm optimization algorithm of the present invention dynamically adjusts the fusion weight (w1=0.6, etc.), combined with S down Assisted verification improves the robustness of abnormal exhalation event determination and reduces missed alerts and false alerts.

[0055] The present invention adopts convolutional neural network to extract multidimensional parameters (rhythm, phase) of respiratory waveform, and combines Hermite interpolation to generate continuous smooth waveform, which can directly reflect the dynamic characteristics of breathing and improve the efficiency of clinical interpretation.

[0056] The present invention adopts a gradient elastic structure (surface buffer + middle porous silica gel + bottom rigid frame) to balance the bite force distribution, reduce local stress, and improve wearing comfort and durability of the tooth pad structure.

[0057] The present invention uses 3D printing gradient density porous silica gel layer (porosity 50% to 30%) to optimize cushioning and supporting performance, reduce material fatigue caused by long-term use, and extend the service life of the dental pad. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic structural line diagram of an embodiment of the present application;

[0059] Figure 2 This is a schematic three-dimensional diagram of the structure of one embodiment of the present application.

[0060] Figure numerals: 1, feedback system; 2, headband; 3, dual-mode combination sensor; 4, tooth pad body; 5, baffle. DETAILED DESCRIPTION

[0061] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0062] Example 1

[0063] The present invention provides a dental pad for real-time monitoring of respiratory status, comprising:

[0064] The bite block body has an occlusal surface designed as a structure in which the lower end surface protrudes forward relative to the upper end surface; a dual-mode combination sensor is integrated in the bite block body, and the dual-mode combination sensor includes a pressure sensor and a temperature sensor, the pressure sensor is used to detect the respiratory airflow pressure signal, and the temperature sensor is used to detect the respiratory airflow temperature signal;

[0065] A signal conversion module, which is electrically connected to the dual-mode combination sensor, and is used to convert the pressure signal and the temperature signal into an electrical signal;

[0066] A signal processing module, which is used to perform data fusion and analysis on the electrical signal and generate a breathing pattern waveform;

[0067] A feedback system is connected to the signal processing module. The feedback system is used to determine the breathing state according to the breathing pattern waveform and trigger an alarm device when no breathing or abnormal breathing is detected.

[0068] The present embodiment provides a mouthpiece for real-time monitoring of respiratory status ( Figure 1-2 ), its core innovation lies in combining multimodal sensing technology with oral medical devices. The tooth pad body 4 is made of medical-grade silicone material (Shore hardness 25A) and is integrally formed through an injection molding process. The occlusal surface is designed as a structure in which the lower end face protrudes 3mm relative to the upper end face. The protrusion has been verified by biomechanical simulation and can move the mandible forward by an average of 5-8mm, effectively expanding the cross-sectional area of ​​the pharyngeal cavity by about 30%. The dual-mode combination sensor 3 is integrated in the palatal area of ​​the tooth pad body 4 and includes a pressure sensitive array (3×3mm 2 )(pressure sensor) and temperature sensitive film (2×2mm 2 ) (temperature sensor), embedded in a silicone matrix by micro-injection molding.

[0069] The signal conversion module uses the AD7793 analog-to-digital converter from ADI to achieve 24-bit high-precision signal conversion. The signal processing module is built on the STM32L476 microcontroller and integrates 1MB on-chip Flash to store baseline data. Feedback system 1 includes the MAX30102 heart rate and blood oxygen module (for synchronous monitoring of vital signs) and the SI4432 wireless communication module (supporting BLE 4.2 protocol). The alarm device uses a combination of a micro vibration motor (3mm in diameter) and an LED indicator (wavelength 590nm), with a response time ≤ combination, response.

[0070] The working principle of the dental pad provided in this embodiment is as follows: When the patient bites the dental pad, the dual-mode combined sensor 3 is in close contact with the oral mucosa. During inhalation, the airflow enters the pharyngeal cavity through the nasal cavity, the pressure sensor detects the negative pressure change of -0.1 to -0.5 kPa, and the temperature sensor senses the temperature difference between the inhaled air (about 22 pressure) and the exhaled air (about 35 exhalation). The signal processing module collects two signals synchronously, eliminates motion artifacts through the Kalman filter algorithm, and calculates parameters such as respiratory rate (RR) and tidal volume (TV) in real time.

[0071] The feedback system obtains the breathing pattern waveform data from the signal processing module in a timely manner, judges the breathing status according to the breathing pattern waveform, and provides timely feedback. Specifically, a prompt light can be set, such as the green light is on (normal breathing): when a regular breathing signal is detected, the system will judge it as normal breathing, and then trigger the green light to turn on, indicating that the breathing is normal.

[0072] Red light on and alarm (no breathing): If the system does not detect a valid breathing signal for a long time, or the signal is abnormal (such as the waveform disappears or is completely flat), the system will assume that breathing has stopped and trigger the red light and alarm device. The alarm device uses sound to remind the operating doctor and anesthesiologist.

[0073] Example 2

[0074] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein the dual-mode combined sensor uses a three-dimensional buckled carbon nanofiber material as an active material, wherein:

[0075] The pressure sensor is composed of a three-dimensional porous network structure of carbon nanofiber material. The piezoresistive response sensitivity of the pressure sensor is 0.1 to 1.5 kPa-1, and it detects a respiratory airflow pressure signal of 0.1 to 5 kPa.

[0076] The temperature sensor is composed of a carbon nanofiber composite layer doped with graphene quantum dots. The temperature sensor has a thermal resistance temperature coefficient TCR of -2.5 to -4.5% / °C, a response time of 50 to 200ms, and detects a respiratory airflow temperature signal of 0.1 to 2°C.

[0077] The dual-mode combination sensor 3 of this embodiment is made of nanocomposite materials. The pressure sensor layer is composed of a three-dimensional buckled carbon nanofiber network, which is grown on a flexible polyimide substrate by chemical vapor deposition (CVD). The structure produces a 15% resistance change under a pressure of 0.1 kPa and maintains a linear response when overloaded with 5 kPa. The temperature sensor layer adopts graphene quantum dot doping technology, and the GQD dispersion (concentration 0.5 mg / mL) is spin-coated on the surface of the carbon nanofiber, and annealed at 120 to form a composite sensitive film.

[0078] Performance parameters: The linearity of the pressure sensor is 0.998 in the range of 0.1-5kPa, and the temperature drift is ≤ a; the temperature sensor has a response time of 80ms in the range of 0.1-2, and the long-term stability test shows that the drift is <0.05 in 30 days. The sensor array is led out through microelectrodes and connected to the flexible circuit using anisotropic conductive glue (ACF).

[0079] Manufacturing process: First, Cr / Au electrodes (thickness 50nm / 200nm) are sputtered on the PI substrate, and the electrode pattern is formed by photolithography. Then CVD growth is carried out in an Ar / H2 atmosphere at a temperature of 800°C, a pressure of 100Torr, and a growth time of 30 minutes. The temperature sensitive layer is prepared by dip coating, with a pulling speed of 5mm / s and a coating thickness of about 1 coating. Finally, the sensor unit is formed by laser cutting.

[0080] Example 3

[0081] The present invention provides a tooth pad for real-time monitoring of respiratory status, wherein the lower end surface protrudes forward by 2mm, 5mm or 8mm relative to the upper end surface.

[0082] The dental pad body 4 of this embodiment provides three types of protrusion models (2mm, 5mm, 8mm), and models with different forward displacement distances to adapt to individual differences of patients; customized production is adopted using CAD / CAM technology. The protrusion design is based on the analysis of the mandibular movement trajectory. The patient's dentition model is obtained by a three-dimensional scanner, and the optimal protrusion angle (15-25 system) is determined by finite element analysis. Different types of dental pads are realized by replacing the injection mold, and the mold accuracy reaches ±. Different models.

[0083] Clinical fitting process: The patient first undergoes a mandibular protrusion test, and an electronic pressure gauge is used to measure the airway resistance at different protrusion amounts. Select a model that makes the resistance drop ≥ bed-fitting and the patient has no temporomandibular joint pain. During fitting, place the dental pad body 4 between the upper and lower teeth, and check the uniformity of the bite through the bite indicator sheet (thickness 0.1mm). The protrusion structure can place the mandible in 70% of the maximum tolerable protrusion position, and it has been clinically proven that it can reduce the apnea hypopnea index (AHI) by 55%. Bed-validated.

[0084] Example 4

[0085] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein a dual-mode combined sensor and a signal conversion module are connected via a flexible printed circuit board, wherein the flexible printed circuit board adopts a polyimide substrate and integrates a silver nanowire conductive layer, and has a thickness of ≤0.1 mm; the surface of the flexible circuit is covered with two layers of 0.05 mm thick medical-grade silicone waterproof films to form a sandwich packaging structure, thereby achieving IPX7 waterproof performance.

[0086] The flexible circuit of this embodiment uses a polyimide substrate (thickness 25 square meters) and is coated with a silver nanowire conductive layer (square resistance ≤, surface coating). The circuit pattern is prepared by microcontact printing technology, and the line width / spacing is 50 by microcontact printing. The waterproof package uses two layers of medical grade silicone film (Dow Corning MDX4-4210) to form a sandwich structure through a hot pressing process (120 degrees, 5MPa, 10 minutes).

[0087] Waterproof test: The encapsulated circuit is immersed in 37% saline for 72 hours, and the insulation resistance remains ≥ 30%. The dynamic bending test shows that after being bent 180 degrees for 5000 times, the resistance change of the conductive layer is less than 5%.

[0088] Connection process: The sensor and the circuit are bonded by anisotropic conductive adhesive (3M 9703) and cured for 30 seconds at a pressure of 150° and 10N. The shear strength of the bonded joint is 5N / mm, which can withstand a bite force of 3-5N in the mouth.

[0089] Example 5

[0090] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein the steps of data fusion and analysis of electrical signals by a signal processing module include:

[0091] The pressure signal and the temperature signal are subjected to low-pass filtering with a cut-off frequency of 10 Hz and digital band-pass filtering with a passband range of 0.1 to 2 Hz;

[0092] The filtered raw signal is normalized based on the mean and standard deviation of the patient's baseline data:

[0093]

[0094] The baseline data refers to the data of the pressure signal and temperature signal of the respiratory airflow output by the signal conversion module, which are continuously sampled at a frequency of 100 Hz within the first 10 seconds after the dual-mode combination sensor is activated and filtered. raw is the original signal after filtering, corresponding to the original data of the pressure sensor (unit: kPa) and the temperature sensor (unit: °C) after filtering; μbaseline is the mean of the baseline data, σ baseline is the standard deviation of the baseline data, S norm is the normalized signal.

[0095] The signal preprocessing process of this embodiment is as follows: the original signal first passes through a Butterworth low-pass filter (cutoff frequency 10 Hz), and then passes through an IIR band-pass filter (0.1-2 Hz). When collecting baseline data, the patient needs to remain in a resting state, the sampling frequency is 100 Hz, and it lasts for 10 seconds, and a total of 1000 data points are collected.

[0096] Normalization formula:

[0097]

[0098] Among them, μ baseline is the mean of baseline data, σ baseline is the standard deviation. This process normalizes the respiratory signals of different patients to the range of [-3, +3] to eliminate the influence of individual differences. Clinically verified, the accuracy of the standardized signal in respiratory pattern recognition is increased to 92.3%.

[0099] Example 6

[0100] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein the step of data fusion and analysis of the electrical signal by the signal processing module further includes: aligning the timing of the normalized pressure signal and the temperature signal through a dynamic time warping algorithm, compensating for the inherent delay of the temperature sensor, and outputting the aligned pressure signal P′(t) and the temperature signal T′(t), wherein the timing error does not exceed 5ms (milliseconds); extracting the peak interval Δt from the aligned pressure signal P′(t) peak (unit: times / min), amplitude A1 (unit: kPa), waveform falling slope S down (unit: kPa / s), and extract the standard deviation of fluctuation σ from the aligned temperature signal T′(t) T (Unit: °C), and rising edge time t rise , (unit: ms); construct the fusion feature value F fused , (used to comprehensively evaluate the stability of respiratory status) formula is:

[0101]

[0102] Among them, A baseline , T cycle , σ baseline are the amplitude mean of the pressure signal, the respiratory cycle mean, and the standard deviation of the temperature signal from the baseline data, respectively.

[0103] The dynamic time warping (DTW) algorithm of this embodiment adopts the Sakoe-Chiba constraint band, and the window size is set to 15% of the respiratory cycle. The timing error of the aligned signal is controlled within 3ms. Feature extraction includes:

[0104] Pressure signal: Δ force peak (interval between adjacent peaks), A1 (peak amplitude of inhalation), S down (Expiratory downslope) Temperature signal: σ T (temperature fluctuation standard deviation), t rise (Inhalation rising edge time)

[0105] Fusion feature F fused The weight coefficients were optimized by 5-fold cross validation through weighted summation. Clinical tests showed that F fused The AUC value in apnea detection reached 0.962.

[0106] Example 7

[0107] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein the step of the signal processing module fusing and analyzing the electrical signal further comprises: determining in the training data set by a particle swarm optimization algorithm: w1=

[0108] 0.6, w2=0.3, and w3=0.1;

[0109] The fusion eigenvalue F fused With the preset threshold F threshold Compare:

[0110] If F fused >F threshold , determined as abnormal respiratory intensity;

[0111] If F fused <0.5·F threshold , determined as abnormal respiratory rate;

[0112] At the same time, the waveform slope S down As an auxiliary verification parameter, if S down <0.5 kPa / s, it is marked as an abnormal exhalation event;

[0113] When abnormal breathing intensity, abnormal breathing frequency or abnormal exhalation events are detected, the alarm device is triggered.

[0114] In this example, the particle swarm optimization algorithm parameters are set as follows: number of particles 30, inertia weight 0.8, acceleration coefficient c1=c2=1.5, and iteration number 100. The training data set contains 100 OSA patients (AHI data set) and 50 healthy controls, with a feature dimension of 6. After optimization, the weights w1=0.6, w2=0.3, and w3=0.1, and the F1 score on the test set reaches 0.947.

[0115] Auxiliary verification parameter S down The threshold setting is based on large sample statistics: when S down When it is <0.5kPa / s, it indicates that the expiratory flow is limited. Clinical data show that this parameter can increase the specificity of abnormal exhalation event detection to 95.8%.

[0116] Example 8

[0117] The present invention provides a tooth pad for real-time monitoring of respiratory status, wherein the specific steps of the signal processing module generating a respiratory pattern waveform include:

[0118] Based on the aligned pressure signal P′(t) and temperature signal T′(t), the convolutional neural network model is used to extract features from the time series data and output multidimensional waveform parameters including respiratory rhythm, amplitude and phase.

[0119] The multi-dimensional waveform parameters are input into the waveform synthesizer, and the discrete parameters are processed into a continuous state using the piecewise cubic Hermite interpolation algorithm to generate a real-time breathing pattern waveform;

[0120] The horizontal axis of the breathing pattern waveform is time t, and the vertical axis is the weighted fusion value W(t) of the normalized pressure and temperature signals (the fusion value W(t) is a real-time waveform generation parameter). The calculation formula is:

[0121] W(t)=α·P′(t)+β·T′(t)

[0122] Among them, α=0.7, β=0.3, and α+β=1, and the weight value is determined by optimizing the clinical data set validation.

[0123] The structure of the convolutional neural network model in this example is: input layer (1 convolutional neural network) → → neural network model (32, kernel_size = 5) → →, kernel_size = 5. Respiration during combined value (64, kernel_size = 3) → →, kernel_size = 3. Respiration pattern waveform during combined value; breathing rhythm, (16) → → output layer (3). The model was trained on a dataset containing 2000 hours of polysomnography data, and the RMSE of waveform parameter prediction was 0.08 kPa (pressure) and 0.12 (temperature).

[0124] The waveform synthesis adopts segmented cubic Hermite interpolation with an interpolation interval of 5ms. The weight coefficients α and β of the fused waveform W(t) are verified by the subjective scoring of clinicians, and this ratio can best reflect the comprehensive characteristics of respiratory airflow.

[0125] Example 9

[0126] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein the dental pad body comprises:

[0127] The surface anti-slip texture structure is composed of a honeycomb micro-protrusion array distributed on the occlusal surface. The height of a single protrusion is 0.3-0.8mm, and the distance between adjacent protrusions is 1-2mm. The material is medical grade silicone, with a Shore hardness of 20-30A, and the occlusal surface friction coefficient ≥0.6;

[0128] The internal flexible support layer is a gradient elastic structure, comprising:

[0129] The surface buffer layer is made of a superelastic polyurethane material with an elastic modulus of 0.5 to 1.5 MPa and a thickness of 1 to 2 mm;

[0130] The middle transition layer is made of porous silica gel with a porosity of 30-50% and a thickness of 3-5 mm;

[0131] The bottom rigid frame is made of biocompatible polycarbonate with an elastic modulus of 2 to 3 GPa and a thickness of 0.5 to 1 mm.

[0132] In this example, the surface anti-slip texture structure is prepared by micro-injection molding, and the mold uses laser etching technology to form 0.5mm high honeycomb protrusions. The friction coefficient test shows that the friction coefficient of this structure in a wet state is 0.68, which is 42% higher than that of a smooth surface.

[0133] The internal support layer adopts gradient elastic design:

[0134] Surface buffer layer: super elastic polyurethane (Shore hardness 15A), formed by injection molding into a 1.5mm thick continuous phase;

[0135] Middle transition layer: porous silica gel (porosity 40%), prepared by 3D printing technology;

[0136] Bottom rigid frame: polycarbonate (thickness 0.7 mm), embedded in silicone matrix by injection molding;

[0137] Mechanical properties: The maximum deformation of the tooth pad body 4 under a bite force of 50N is ≤ the maximum under the combined force, and the residual deformation after unloading is <0.05mm. Finite element analysis shows that the gradient structure improves the uniformity of stress distribution by 65%.

[0138] Example 10

[0139] The present invention provides a dental pad for real-time monitoring of respiratory status, wherein the porous silica gel of the middle transition layer presents a gradient density distribution along the thickness direction, the porosity decreases linearly from 50% to 30% from the surface layer to the bottom layer, and the average diameter of the holes gradually decreases from 200 μm to 50 μm; the gradient density distribution is achieved by a 3D printing process.

[0140] In this example, the gradient density structure of the middle transition layer is realized by 3D printing technology, using silicone-based photosensitive resin (DSM Watershed XC 11122). Printing parameters: layer thickness 50 printing, laser power 100mW, scanning speed 1000mm / s. Porosity control is achieved by changing the filling rate of the scanning path, which decreases linearly from 50% (filling spacing 0.4mm) in the surface layer to 30% (filling spacing 0.7mm) in the bottom layer.

[0141] Performance test: The compression modulus of the gradient structure changes from 0.8MPa on the surface to 1.5MPa on the bottom layer, which is 38% higher than that of the uniform structure. Fatigue tests show that after 100,000 cycles of loading, the material still maintains 85% of its initial elastic modulus.

[0142] The above implementation methods have all been prototyped and tested, and the key performance indicators have met the design requirements, and some parameters are better than similar products (for example, the AHI detection accuracy of the comparative product ResMed S9 is 89.7%, while this implementation method reaches 92.3%). All materials have passed ISO 10993 biocompatibility testing and meet medical device safety standards.

[0143] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A dental pad for real-time monitoring of respiratory status, characterized in that: include: The tooth pad body has an occlusal surface designed as a structure in which the lower end surface protrudes forward relative to the upper end surface; A dual-mode combined sensor, which is integrated in the dental pad body, includes a pressure sensor and a temperature sensor, the pressure sensor is used to detect the respiratory airflow pressure signal, and the temperature sensor is used to detect the respiratory airflow temperature signal; A signal conversion module, which is electrically connected to the dual-mode combination sensor, and is used to convert the pressure signal and the temperature signal into an electrical signal; A signal processing module, which is used to perform data fusion and analysis on the electrical signal and generate a breathing pattern waveform; A feedback system is connected to the signal processing module. The feedback system is used to determine the breathing state according to the breathing pattern waveform and trigger an alarm device when no breathing or abnormal breathing is detected.

2. The mouthpiece for real-time monitoring of respiratory status according to claim 1, characterized in that: The dual-mode combined sensor uses three-dimensional buckled carbon nanofiber material as the active material, where: The pressure sensor is composed of a three-dimensional porous network structure of carbon nanofiber material. The piezoresistive response sensitivity of the pressure sensor is 0.1 to 1.5 kPa-1, and it detects a respiratory airflow pressure signal of 0.1 to 5 kPa. The temperature sensor is composed of a carbon nanofiber composite layer doped with graphene quantum dots. The temperature sensor has a thermal resistance temperature coefficient TCR of -2.5 to -4.5% / °C, a response time of 50 to 200ms, and detects a respiratory airflow temperature signal of 0.1 to 2°C.

3. The mouthguard for real-time monitoring of respiratory status according to claim 1, characterized in that: The lower end surface protrudes forward by 2mm, 5mm or 8mm relative to the upper end surface.

4. The mouthguard for real-time monitoring of respiratory status according to claim 1, characterized in that: The dual-mode combination sensor and the signal conversion module are connected through a flexible printed circuit board. The flexible printed circuit board uses a polyimide substrate and integrates a silver nanowire conductive layer with a thickness of ≤0.1mm; the surface of the flexible circuit is covered with two layers of 0.05mm thick medical-grade silicone waterproof film.

5. The mouthguard for real-time monitoring of respiratory status according to claim 1, characterized in that: The steps of data fusion and analysis of electrical signals by the signal processing module include: The pressure signal and the temperature signal are subjected to low-pass filtering with a cut-off frequency of 10 Hz and digital band-pass filtering with a passband range of 0.1 to 2 Hz; The filtered raw signal is normalized based on the mean and standard deviation of the patient's baseline data: The baseline data refers to the data of the pressure signal and temperature signal of the respiratory airflow output by the signal conversion module, which are continuously sampled at a frequency of 100 Hz within the first 10 seconds after the dual-mode combination sensor is activated and filtered. raw is the original signal after filtering, corresponding to the original data of the pressure sensor and the temperature sensor after filtering; μ baseline is the mean of the baseline data, σ baseline is the standard deviation of the baseline data, S norm is the normalized signal.

6. The mouthguard for real-time monitoring of respiratory status according to claim 5, characterized in that: The step of data fusion and analysis of the electrical signal by the signal processing module also includes: aligning the timing of the normalized pressure signal and the temperature signal through a dynamic time warping algorithm, compensating for the inherent delay of the temperature sensor, and outputting the aligned pressure signal P′ The timing error between the aligned pressure signal P′(t) and the temperature signal T′(t) is less than 5 ms; the peak interval Δt is extracted from the aligned pressure signal P′(t) peak , amplitude A1, waveform falling slope S down , and extract the standard deviation σ of the fluctuation from the aligned temperature signal T′(t) T , and rising edge time t rise ,; construct fusion feature value F fused , the formula is: Among them, A baseline , T cycle , σ baseline are the amplitude mean of the pressure signal, the respiratory cycle mean, and the standard deviation of the temperature signal from the baseline data, respectively.

7. The mouthguard for real-time monitoring of respiratory status according to claim 6, characterized in that: The step of the signal processing module performing data fusion and analysis on the electrical signal further includes: determining in the training data set by a particle swarm optimization algorithm: w1=0.6, w2=0.3, and w3=0.1; The fusion eigenvalue F fused With the preset threshold F threshold Compare: If F fused >F threshold , determined as abnormal respiratory intensity; If F fused <0.5·F threshold , determined as abnormal respiratory rate; At the same time, the waveform slope S down As an auxiliary verification parameter, if S down <0.5 kPa / s, it is marked as an abnormal exhalation event; When abnormal breathing intensity, abnormal breathing frequency or abnormal exhalation events are detected, the alarm device is triggered.

8. The mouthguard for real-time monitoring of respiratory status according to claim 7, characterized in that: The specific steps of the signal processing module to generate the breathing pattern waveform include: Based on the aligned pressure signal P′(t) and temperature signal T′(t), the convolutional neural network model is used to extract features from the time series data and output multidimensional waveform parameters including respiratory rhythm, amplitude and phase. The multi-dimensional waveform parameters are input into the waveform synthesizer, and the discrete parameters are processed into a continuous state using the piecewise cubic Hermite interpolation algorithm to generate a real-time breathing pattern waveform; The horizontal axis of the breathing pattern waveform is time t, and the vertical axis is the weighted fusion value W(t) of the normalized pressure and temperature signals (the fusion value W(t) is a real-time waveform generation parameter). The calculation formula is: W(t)=α·P′(t)+β·T′(t) Among them, α=0.7, β=0.3, and α+β=1, and the weight value is determined by optimizing the clinical data set validation.

9. The mouthguard for real-time monitoring of respiratory status according to claim 1, characterized in that: The tooth pad body includes: The surface anti-slip texture structure is composed of a honeycomb micro-protrusion array distributed on the occlusal surface. The height of a single protrusion is 0.3-0.8mm, and the distance between adjacent protrusions is 1-2mm. The material is medical grade silicone, with a Shore hardness of 20-30A, and the occlusal surface friction coefficient ≥0.6; The internal flexible support layer is a gradient elastic structure, comprising: The surface buffer layer is made of a superelastic polyurethane material with an elastic modulus of 0.5 to 1.5 MPa and a thickness of 1 to 2 mm; The middle transition layer is made of porous silica gel with a porosity of 30-50% and a thickness of 3-5 mm; The bottom rigid frame is made of biocompatible polycarbonate with an elastic modulus of 2 to 3 GPa and a thickness of 0.5 to 1 mm.

10. The bite block for real-time monitoring of respiratory status according to claim 9, characterized in that: The porous silica gel in the middle transition layer has a gradient density distribution along the thickness direction, the porosity decreases linearly from 50% to 30% from the surface to the bottom layer, and the average pore diameter gradually decreases from 200 to 50; the gradient density distribution is achieved through 3D printing technology.

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