CN121196535A

CN121196535A - Intelligent respiration training monitoring patch driven by implantable nano generator

Info

Publication number: CN121196535A
Application number: CN202511775557.7A
Authority: CN
Inventors: 徐万美
Original assignee: First Affiliated Hospital of Army Medical University
Current assignee: First Affiliated Hospital of Army Medical University
Priority date: 2025-11-28
Filing date: 2025-11-28
Publication date: 2025-12-26

Abstract

The invention discloses an intelligent respiration training monitoring patch driven by an implantable nano generator, and belongs to the field of implantable medical equipment and intelligent respiration rehabilitation. The energy collection layer comprises a triboelectric nano generator unit, an airflow guide channel and an energy storage module, the monitoring sensing layer comprises a laser engraving graphene strain sensing array, a signal conditioning module and a data preprocessing module, the control intervention layer comprises a BLE communication module, a data analysis module, a neuromuscular electrical stimulation unit and a triggering control module, and the telescopic laminated structure further comprises a first telescopic adjusting layer and a second telescopic adjusting layer. The invention innovatively provides a telescopic laminated structure, so that self power supply is reliable and stable, and the integrated functions of stable self power supply, accurate dynamic monitoring, safety and personalized intervention are realized.

Description

Intelligent respiration training monitoring patch driven by implantable nano generator

Technical Field

The invention relates to the field of implantable medical equipment and intelligent respiratory rehabilitation, in particular to a nano generator driven intelligent respiratory training monitoring patch integrating self-adaptive airflow collection, respiratory phase perception weighting, multi-physiological linkage intervention and cross-layer energy management, which is suitable for long-term respiratory function monitoring and personalized training intervention of severe respiratory dysfunction patients (such as mechanical ventilation and withdrawal machine and high paraplegia with diaphragmatic paralysis).

Background

The rehabilitation process of patients with severe respiratory dysfunction (such as after mechanical ventilation and withdrawal machine and high paraplegia with diaphragmatic paralysis) is based on long-term accurate monitoring and real-time safe intervention of diaphragmatic movement. In-vitro respiration monitoring equipment (such as chest belt type strain gauges and oronasal airflow sensors) are easy to suffer from body movement, clothing shielding and environmental airflow interference, the data error rate is often more than 20%, and continuous monitoring requirements of patients during night or activity cannot be met, and implantable equipment is a clinical preferred scheme of the patients due to the fact that the implantable equipment is directly close to a diaphragm projection area and has high anti-interference capability. However, the following core drawbacks still exist in the prior art:

The invention patent 202011470564.3 discloses a self-driven respiration monitoring system based on a triboelectric nano generator (TENG), but adopts a rigid plastic airflow channel, and the periodic movement of chest wall tissues after implantation easily causes channel deformation and blockage, so that the fluctuation range of the respiration airflow utilization rate reaches more than 35%, and the fluctuation range of the TENG output voltage is 2.1-6.8V, and continuous and stable power supply cannot be provided for a low-power consumption sensing module, and external batteries are required to be relied on to supplement electric energy.

The respiratory sensor based on the graphene composite material and the preparation method thereof disclosed in Chinese patent 202310233882.5 provide a flexible graphene sensing structure for respiratory monitoring, but a global unified signal fusion algorithm is adopted, so that strain heterogeneity of the diaphragm in the inspiration (the strain peak value in the central area reaches 1.2%) and expiration (the strain peak value in the edge area reaches 0.9%) stages is not considered, the problem of interference of respiratory humidity on a sensing signal is not solved, the displacement calculation error is often more than 0.9mm, and physiological fluctuation and pathological abnormality of the diaphragm cannot be distinguished.

The intervention safety is low, and the Chinese patent 202411629424.4 discloses an electric stimulation regulation and control scheme based on muscle contraction signals, but only triggers neuromuscular electric stimulation (NMES) according to single muscle signals, and does not relate to the circulation and oxygenation state of a patient. Clinical data show that blind stimulation may trigger a sudden increase in respiratory muscle oxygen consumption, leading to exacerbation of hypoxia, with a risk occurrence of 27%, when patient blood oxygen saturation is < 93%.

The energy collection, monitoring and intervention module of the existing implantable respiratory device such as the external phrenic nerve electric stimulator with synchronous breathing of Chinese patent invention 202510754097.3 is of independent closed loop design, and the energy management module is only responsible for storing electric energy and does not establish linkage with the functional module. When the TENG generating capacity is insufficient (the super capacitor voltage is less than 2.7V), the intervention module still maintains full-power operation, and equipment outage occurs once every 48 hours on average, so that a monitoring data chain is interrupted.

Disclosure of Invention

In view of the above, the invention provides an intelligent respiration training monitoring patch driven by an implantable nano-generator, which solves the problems of unstable energy supply, low monitoring data precision, high risk of intervention operation and discontinuous system operation through four core innovations of self-adaptive airflow collection, respiration stage weighted monitoring, multi-physiological linkage intervention and cross-layer energy management, and realizes the integrated functions of stable self-power supply, accurate dynamic monitoring and safe personalized intervention.

In order to achieve the above purpose, the present invention provides the following technical solutions:

An intelligent respiration training monitoring patch driven by an implantable nano-generator adopts a telescopic laminated structure, and sequentially comprises an energy acquisition layer, a monitoring sensing layer, a control intervention layer and a biocompatible layer from top to bottom, wherein the layers are integrated through a flexible connection structure;

the energy collection layer comprises a triboelectric nano generator unit, an airflow guide channel and an energy storage module, wherein the triboelectric nano generator unit adopts a layered structure formed by a polydimethylsiloxane flexible film and a graphene electrode, 3-5 groups are arranged in total and symmetrically distributed at the edge of a patch, the airflow guide channel is of an arc micro-channel structure, one end of the airflow guide channel is communicated with an implantation area beside a trachea, the other end of the airflow guide channel is opened in a chest wall tissue gap, and the energy storage module is a miniature super capacitor and is connected with the triboelectric nano generator unit through a flexible lead;

The monitoring sensing layer comprises a laser engraving graphene strain sensing array, a signal conditioning module and a data preprocessing module, wherein the graphene strain sensing array is a sensing unit array with the specification of 5 multiplied by 5 to 7 multiplied by 7, the coverage area is matched with the projection area of diaphragm muscle on the chest wall, the signal conditioning module is integrated with an operational amplifier and a filter circuit, the data preprocessing module is a micro MCU, strain signals are converted into diaphragm contraction displacement data through a bilinear interpolation algorithm, and a diaphragm contraction vector diagram is generated by combining an area weighting fusion algorithm;

The control intervention layer comprises a Bluetooth low-power consumption communication module, a data analysis module, a neuromuscular electrical stimulation unit (neuromuscular electrical stimulation unit) and a trigger control module, wherein the data analysis module pre-stores diaphragm movement characteristic parameters of a normal breathing mode, and the breathing efficiency is evaluated through a breathing efficiency index calculation algorithm, and the formula is as follows:

wherein, the For maximum contraction displacement of diaphragm muscle in 3-5 respiratory cycles, f is respiratory frequency, sigma is standard deviation of synchronous displacement data, k is correction coefficient, and synchronicity is judged through a chest and abdomen movement synchronicity formula:

,

wherein, the Is chest displacement data,As the abdominal displacement data,As the phase difference, the normal phase differenceThe threshold is set at 30-60,

The neuromuscular electrical stimulation unit comprises 3-5 groups of microelectrodes and a stimulation signal generator, the triggering control module can trigger the neuromuscular electrical stimulation unit when abnormal breathing mode is detected,

The biocompatible layer adopts a medical polyether-ether-ketone film as an outer layer, the inner layer is coated with a polylactic acid-glycolic acid copolymer coating, the thickness of the two layers is 0.08-0.12mm,

The telescopic laminated structure further comprises a first telescopic adjusting layer arranged between the energy acquisition layer and the monitoring sensing layer and a second telescopic adjusting layer arranged between the monitoring sensing layer and the control intervention layer, wherein the first telescopic adjusting layer is a super-elastic SMA fiber woven net and a porous PLGA composite buffer layer, and the second telescopic adjusting layer comprises a flexible hinge array and a plurality of low-friction PTFE positioning grooves.

Further, in the energy collection layer, the area of each group of triboelectric nano generator units is 12-18mm multiplied by 8-12mm, the diameter of the air flow guide channel is 1.0-1.4mm, the capacity of the micro super capacitor is 400-600 mu F, the output voltage of the single group of triboelectric nano generator units is 3.2-5.3V under the respiratory air flow with the flow speed of 0.4-2.2m/s, and the output voltage is as follows:

Wherein, the In order for the air flow rate to be high,And the total power consumption of the equipment is less than or equal to 120 mu W for experimental fitting coefficients.

Further, in the monitoring sensing layer, the area of each sensing unit of the graphene strain sensing array is 1.8-2.2mm multiplied by 1.8-2.2mm, the distance between the sensing units is 1.2-1.8mm, the initial resistance value R is 4-6k omega, and the relation between strain and resistance change satisfies:

wherein, the In order to obtain the variation of the resistance,For initial resistance, k=120-150 is the gauge coefficient, epsilon is the strain value;

The gain of an operational amplifier in the signal conditioning module is 800-1200 times, the cut-off frequency of a filter circuit is 8-12Hz, the data preprocessing module adopts STM32L476 or MCU with the equivalent performance model, the fitting coefficient in the bilinear interpolation algorithm is solved through the simultaneous equation system of coordinates of adjacent 4 sensing units and displacement data, and the displacement precision of the vector diagram after the region weighted fusion is 0.3-0.6mm.

Further, in the control intervention layer, the BLE communication module adopts nRF52832 or chips with the same series of equivalent performance models, the transmission rate is 0.8-1.2Mbps, the transmission distance is less than or equal to 12m, when the data analysis module calculates REI,Taking the maximum displacement in 3-5 respiratory cycles,Taking standard deviation of synchronous displacement data:

,

For a single value of the cyclic displacement, Average displacement, normal phase difference in synchronous determination of chest and abdomen movementThe threshold value is set to 30-60 degrees, and the abnormal breathing pattern judgment condition is REI <23-35 lasting for 4-6s orFor 2-4s.

Further, in the control intervention layer, the diameter of the microelectrode of the neuromuscular electric stimulation unit is 0.6-1.0mm, the exposed end of the electrode protrudes out of the biocompatible layer by 0.15-0.25mm, the pulse signal parameters generated by the stimulation signal generator are that the frequency is 15-55Hz, the current intensity is 0.08-0.55mA, the pulse width is 180-220 mu s, the stimulation intensity is related to the functional state of diaphragm, and the requirements are met

,

Wherein the method comprises the steps ofOnly whenWhen enabled.

Further, the preparation process of the energy acquisition layer comprises the steps of preparing a PDMS flexible film with the thickness of 45-55 mu m by adopting a mould pressing method, depositing a graphene electrode with the thickness of 8-12nm on the surface of the film by magnetron sputtering, assembling the film into a triboelectric nano generator unit, adopting 3D printing to prepare an arc airflow guide channel made of PLGA material, bonding the arc airflow guide channel and the triboelectric nano generator unit through medical glue, welding a miniature super capacitor with the size of 4-6mm multiplied by 2.5-3.5mm multiplied by 0.8-1.2mm with the triboelectric nano generator unit through gold wires, connecting a rectifying circuit, and outputting voltage after rectification to meet the requirements of。

The preparation process of the monitoring sensing layer comprises the steps of preparing a graphene strain sensing array on a polyimide substrate with the thickness of 20-30 mu m through a laser engraving technology, electrically connecting a signal conditioning module and a data preprocessing module with the sensing array through a flexible PCB, wherein program codes of a bilinear interpolation algorithm and a region weighted fusion algorithm are built in the data preprocessing module, the whole is packaged in a PI film, and response time of a sensing unit after packaging is less than or equal to 15ms.

Further, the triggering condition of the triggering control module is set to be that the duration of the abnormal breathing mode is more than or equal to 4-6s, the stimulation parameters of the neuromuscular electric stimulation unit are adjusted through a hierarchical stimulation algorithm after triggering, the current intensity adjusting formula is It=It-1+0.05-0.1 mA, the current intensity adjusting formula is started only when the abnormal mode is uncorrected within 5-8s after the stimulation at the moment T-1, the It is less than 0.55mA, the stimulation duration T=10+2× (T-1), T is the stimulation times, and T is more than or equal to 1, and only when T is more than or equal to 1And enabling the abnormal mode in 5-8s after the moment stimulation when the abnormal mode is not corrected, and feeding back an intervention result to the nurse terminal in real time through the BLE communication module.

Furthermore, the biocompatible layer is covered on the surface of the control intervention layer by adopting a hot press molding technology, sealing is realized by laser welding, and the welding strength meets the requirement in a tensile testThe water-proof and tissue fluid invasion-proof performance meets the leak rate in the soaking test。

Furthermore, the microelectrode of the neuromuscular electric stimulation unit is made of platinum iridium alloy or medical grade pure silver, the electrode position corresponds to the innervation area related to the movement of the diaphragm one by one, the diaphragm displacement data are acquired in real time through a feedback regulation algorithm in the stimulation process, and if the displacement fluctuation range is reached,In order to displace the membrane during the stimulation,For the displacement before stimulation, the current intensity delta I=0.03-0.08 mA is automatically reduced, and the current after adjustment is carried out。

The invention has the beneficial effects that:

1. The invention adopts a composite structure with a multi-layer functional structure matched with a multi-layer telescopic structure to realize a telescopic laminated structure, so that the thickness and the shape are obviously changed along with the respiratory movement of a human body, the energy in the change is easier to be accepted, and the self power generation is possible;

2. according to the invention, through the composite airflow channel of the nickel-titanium memory alloy spring and the medical silicone rubber tube and the pressure regulating valve, the fluctuation range of the input airflow of the triboelectric nano generator unit is obviously reduced, and the output voltage is stabilized at 3.3 V+/-0.05V by combining with the TPS73633 voltage stabilizing circuit, so that the continuous power supply requirement of the whole module is met.

3. According to the invention, through the respiratory phase identification unit and the self-adaptive weighting algorithm, the central high-strain signal of the diaphragm is mainly acquired during inspiration, the edge signal is emphasized during expiration, and the displacement monitoring precision is improved to 0.1-0.3mm by combining the bilinear interpolation algorithm, so that the displacement monitoring precision is far lower than the clinical error threshold of 0.5 mm.

Meanwhile, the sensing array is packaged in the PI film, so that the breathing water vapor is effectively isolated, the resistance drift rate is reduced, and long-term reliability of monitoring data is ensured. Doctors can clearly distinguish physiological shallow respiration caused by short-term fatigue of patients from pathological abnormality of diaphragmatic muscle force reduction after withdrawal based on accurate diaphragmatic contraction displacement data, avoid the misregulation of a rehabilitation scheme and provide data support for personalized treatment.

4. According to the invention, a three-dimensional evaluation matrix of diaphragm displacement-heart rate-blood oxygen is constructed, intervention is triggered only when multiple indexes are abnormal at the same time (such as displacement <5 mm+heart rate exceeds a resting value by 15%), and a triggering threshold is dynamically adjusted along with blood oxygen, so that accurate triggering of safety priority is realized.

The grading stimulation strategy and the real-time feedback regulation mechanism (the descending flow is 20% when the displacement fluctuation is more than 30%) further avoid overstimulation, namely, low-intensity stimulation of 0.08-0.2mA is adopted when the patient is slightly abnormal, and the stimulation is increased to 0.2-0.55mA when the patient is severely abnormal, so that the respiratory stress risk in a hypoxia state is obviously reduced, and the method is particularly suitable for physiological vulnerability of patients suffering from weight symptoms (such as slow lung obstruction and ARDS recovery period), and the intervention safety is obviously improved.

5. According to the invention, a cross-layer energy linkage module is additionally arranged, and the functional mode is dynamically adjusted according to the voltage of the super capacitor, wherein the NMES redundant function is closed (power consumption is reduced by 40%) when the voltage is 2.5-3.0V, intervention is suspended but monitoring and communication are reserved when the voltage is less than 2.5V (power consumption is less than or equal to 50 mu W), and meanwhile, low-power early warning is sent.

6. The biological compatible layer adopts a composite structure of PEEK film, PLGA coating and nano silver antibacterial coating, the antibacterial rate is obviously improved, the leakage rate is obviously reduced after laser welding and sealing, the invasion and infection risks of tissue fluid are avoided, all layers are connected through flexible polyimide hinges, deformation of +/-20 degrees can be born, periodic motion of chest wall is perfectly adapted, and the foreign body sensation of tissue after implantation is reduced.

In a word, the invention innovatively provides a telescopic laminated structure, so that self power supply is reliable and stable, the comfort and compliance of use are obviously improved, the telescopic laminated structure is suitable for four core innovations of self-adaptive airflow collection, respiration stage weighted monitoring, multi-physiological linkage intervention and cross-layer energy management after mechanical ventilation and removal, the problems of unstable energy supply, low monitoring data precision, high intervention operation risk and discontinuous system operation are solved, and the integrated functions of stable self-power supply, accurate dynamic monitoring, safe personalized intervention are realized.

Drawings

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:

FIG. 1 is a cross-sectional view of the present invention;

FIG. 2 is a cross-sectional view of an energy harvesting layer;

FIG. 3 is a cross-sectional view of a monitoring sensing layer;

FIG. 4 is a cross-sectional view of a control intervention layer;

FIG. 5 is a cross-sectional view of a biocompatible layer;

FIG. 6 is a cross-sectional view of a first telescoping adjustment layer;

fig. 7 is a cross-sectional view of a second telescoping adjustment layer.

Reference numerals illustrate:

1-energy acquisition layer, 2-monitoring sensing layer, 3-control intervention layer, 4-bio-compatible layer, 5-first telescopic adjustment layer, 6-second telescopic adjustment layer, 7-friction electric nano generator unit, 8-air flow guide channel, 9-energy storage module, 10-laser engraved graphene strain sensing array, 11-signal conditioning module, 12-data preprocessing module, 13-BLE communication module, 14-data analysis module, 15-neuromuscular electric stimulation unit, 16-triggering control module, 17-PEEK film, 18-PLGA coating, 19-super-elastic SMA fiber woven net, 20-porous PLGA buffer layer, 21-flexible hinge array and 22-low friction PTFE positioning groove.

Detailed Description

Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.

In which the drawings are for illustrative purposes only and are not intended to be construed as limiting the invention, and in which certain elements of the drawings may be omitted, enlarged or reduced in order to better illustrate embodiments of the invention, and not to represent actual product dimensions, it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.

As shown in fig. 1-7, this embodiment provides an intelligent respiration training monitoring patch driven by an implantable nano-generator, which adopts a telescopic lamination structure, and sequentially includes an energy acquisition layer 1, a monitoring sensing layer 2, a control intervention layer 3 and a biocompatible layer 4 from top to bottom, wherein each layer is integrated through a flexible connection structure, the overall thickness is less than or equal to 2.8mm, and specific structures and parameters are as follows:

As shown in fig. 2, the energy collection layer 1 of the embodiment is used for converting mechanical energy of respiratory airflow into electric energy to supply power to the whole device, and comprises a triboelectric nano generator unit 7, an airflow guiding channel 8 and an energy storage module 9, wherein the triboelectric nano generator unit 7 adopts a layered structure formed by a Polydimethylsiloxane (PDMS) flexible film and a graphene electrode, and is totally provided with 3-5 groups and symmetrically distributed at the edge of a patch; the area of the single-group triboelectric nano generator unit 7 is 12-18mm multiplied by 8-12mm, the output voltage can be 3.2-5.3V under the respiratory air flow with the flow speed of 0.4-2.2m/s, the output voltage and the air flow speed mu meet the fitting relation (fitting coefficients a and b are calibrated through experiments), the air flow guide channel 8 is of an arc-shaped micro-channel structure, is made of polylactic acid-glycolic acid copolymer (PLGA) and has the diameter of 1.0-1.4mm, one end of the air flow guide channel is communicated with an implantation area beside an air pipe, the other end of the air flow guide channel is opened in a chest wall tissue gap and is used for guiding respiratory air flow to pass through the triboelectric nano generator unit 7, the energy storage module 9 is a micro super capacitor with the capacity of 400-600 mu F and the size of 4-6mm multiplied by 2.5-3.5mm multiplied by 0.8-1.2mm, is connected with the triboelectric nano generator unit 7 through a flexible gold wire, and is connected with a rectifying circuit in series, and the output voltage is stabilized at 3.0-3.3V after rectification, so that the power supply is supplied to the subsequent monitoring sensing layer 2 and the control intervention layer 3.

As shown in FIG. 3, the monitoring sensing layer 2 of the embodiment is used for collecting strain signals of diaphragm movement and converting the strain signals into quantized shrinkage displacement data, and comprises a laser engraved graphene strain sensing array 10, a signal conditioning module 11 and a data preprocessing module 12, wherein the laser engraved graphene strain sensing array 10 is prepared based on Polyimide (PI) substrates with the thickness of 20-30 mu m, a sensing unit array with the specification of 5 multiplied by 5-7 multiplied by 7 is prepared, the coverage area is completely matched with the projection area of diaphragm on the chest wall, the area of a single sensing unit is 1.8-2.2mm multiplied by 1.8-2.2mm, the distance is 1.2-1.8mm, the initial resistance value is 4-6kΩ, and the relation between strain and resistance change is satisfied(Where k=120-150 is the gauge factor, epsilon is the strain value,The device is characterized by comprising a signal conditioning module 11, a filtering circuit, a data preprocessing module 12, a dual linear interpolation algorithm and a region weighted fusion algorithm, wherein the signal conditioning module 11 integrates an operational amplifier and the filtering circuit, the gain of the operational amplifier is 800-1200 times and is used for amplifying weak strain signals, the cut-off frequency of the filtering circuit is 8-12Hz and is used for filtering high-frequency noise outside respiratory signals, the STM32L476 or a micro MCU (micro control unit) with performance is adopted, the dual linear interpolation algorithm and the region weighted fusion algorithm are built in the data preprocessing module 12, the dual linear interpolation algorithm solves fitting coefficients through a simultaneous equation system of coordinates and displacement data of 4 adjacent sensing units and converts the strain signals into diaphragm contraction displacement, the weight coefficient of the region weighted fusion algorithm is inversely related to the distance d between the sensing units and the center of the diaphragm, the displacement precision is 0.3-0.6mm, and the response time is less than or equal to 15ms.

As shown in FIG. 4, the control intervention layer 3 of the embodiment is used for evaluating respiratory efficiency, judging respiratory mode and triggering intervention when abnormal respiration is detected, and comprises a Bluetooth low-power consumption communication module (BLE communication module 13), a data analysis module 14, a neuromuscular electrical stimulation unit 15 and a triggering control module 16, wherein the BLE communication module 13 adopts nRF52832 or a same-series chip, the transmission rate is 0.8-1.2Mbps, the transmission distance is less than or equal to 12m, the data analysis module 14 is used for feeding back diaphragmatic displacement data, respiratory efficiency parameters and intervention results to a nurse terminal in real time, and the data analysis module 14 is used for pre-storing diaphragmatic motion characteristic parameters of a normal respiratory mode, and the evaluation is realized through two algorithms, namely Respiratory Efficiency Index (REI) calculation: (wherein Maximum contraction displacement of diaphragm muscle in 3-5 respiratory cycles, f is respiratory frequency, sigma is standard deviation of synchronous displacement data, di is single cycle displacement value, average displacement and k is correction coefficient), chest and abdomen movement synchronism judgment, namely, based on formula Cs, chest displacement data Ti, abdomen displacement data Ai and phase differenceCorrelation, normal phase differenceThe threshold is set to be 30-60 DEG, the neuromuscular electric stimulation unit 15 comprises 3-5 groups of microelectrodes and a stimulation signal generator, the microelectrodes are made of platinum iridium alloy or medical grade pure silver material, the diameter is 0.6-1.0mm, the exposed ends protrude out of the biocompatible layers by 40.15-0.25mm, the positions of the electrodes correspond to the innervation areas related to the movements of the diaphragm, the stimulation signal generator generates pulse signals, the parameters are frequency 15-55Hz, current intensity is 0.08-0.55mA, pulse width is 180-220 mu s, the stimulation intensity is related to REI, and the requirements are met(WhereinEnabled only when REI < 40), triggering the control module 16 that the abnormal breathing pattern determination condition is "phase difference"For 2-4s 'or "other abnormal characteristics for 4-6 s', starting the neuromuscular electrical stimulation unit 15 when the triggering condition (abnormal breathing duration ≡4-6 s) is met, and adjusting the parameters by a hierarchical stimulation algorithm, namely current intensity it=it-1+0.05-0.1 mA (only enabled when the abnormal mode is not corrected within 5-8s after stimulation at time T-1 and It is <0.55 mA), stimulation duration T=10+2× (T-1) (T is the stimulation times, T is not less than 1), and simultaneously acquiring diaphragm displacement in real time by a feedback adjustment algorithm if the displacement fluctuation range is not corrected(In order to displace the membrane during the stimulation,For displacement before stimulation), automatically reducing current intensity Δi=0.03-0.08 mA, and regulating current after adjustment。

As shown in FIG. 5, the bio-compatible layer 4 of the present embodiment is used for protecting the internal structure and ensuring the biosafety of long-term implantation, and has a double-layer structure, wherein the outer layer is a medical polyether ether ketone film (PEEK film 17) with the thickness of 0.08-0.12mm, the inner layer is a polylactic acid-glycolic acid copolymer coating (PLGA coating 18) with the thickness of 0.08-0.12mm, the surface of the control intervention layer 3 is covered by a hot-press molding technology, the laser welding sealing is performed, the welding strength satisfies F & gt 8N in a tensile test, and the waterproof and anti-tissue fluid invasion performance satisfies the leakage rate in a soaking test。

In particular, as shown in fig. 6 and 7, in this embodiment, in order to better receive the fluctuation caused by respiration to realize self power supply, the telescopic laminated structure further includes a first telescopic adjustment layer 5 and a second telescopic adjustment layer 6, the first telescopic adjustment layer 5 is disposed between the energy collecting layer 1 and the monitoring sensing layer 2, the second telescopic adjustment layer 6 is disposed between the monitoring sensing layer 2 and the control intervention layer 3, specifically, the first telescopic adjustment layer 5 is a super-elastic SMA fiber woven mesh 19+porous PLGA buffer layer 20, the structure has strong elastic deformation, shape changes when receiving motion impact and respiratory impact, so as to generate deformation energy, while the second telescopic adjustment layer 6 includes a flexible hinge array 21 (medical TPU material) and a low friction PTFE positioning groove 22, on one hand, the flexible hinge array 21 is tiled on the whole surface to form a plane that can have a certain angle change, and changes with the morphological change of the upper energy collecting layer 1 and the monitoring sensing layer 2, so as to fully release the energy, thereby ensuring that the energy collecting layer 1 receives more impact energy, and the low friction layer is disposed in the layer has a certain friction positioning groove 22 or forms a certain constraint on the surface to form a certain constraint on the PTFE positioning groove or a certain positioning groove.

Meanwhile, all the functional layers and the structural layers are integrated through medical-grade flexible adhesives, the overall power consumption is extremely low, continuous self-power supply is realized through the energy acquisition layer 1, external power supply or battery replacement is not needed, and the use is simple and stable.

In view of the above structure, in particular, the present embodiment also proposes a preparation method:

1. preparation of energy harvesting layer 1 (core function: conversion of mechanical energy into electric energy)

1. Triboelectric nano generator unit 7 preparation

And (3) preparing the PDMS flexible film, namely uniformly mixing the PDMS prepolymer and the curing agent (the mass ratio is 10:1) by adopting a mould pressing method, pouring the mixture into a custom mould (corresponding to the size of the single group of triboelectric nano generator units 7 of 12-18mm multiplied by 8-12 mm), curing the mixture in an 80 ℃ oven for 2 hours after vacuum defoaming to form the PDMS film with the thickness of 45-55 mu m, and demoulding the PDMS film for later use.

Graphene electrode deposition, namely depositing a graphene electrode with the thickness of 8-12nm on one side of a PDMS film by a magnetron sputtering technology, wherein the sputtering power is controlled to be 150-200W, and the vacuum degree is controlledEnsure that the electrode is covered evenly and combined firmly with PDMS.

And cutting the triboelectric nano generator unit 7, namely cutting the PDMS film deposited with the electrodes according to the size of 12-18mm multiplied by 8-12mm to prepare 3-5 groups for standby.

2. Air flow guiding channel 8 is prepared

Adopting a 3D printing technology (photo-curing molding), taking PLGA as a printing material, designing a model according to an arc-shaped micro-channel structure with the diameter of 1.0-1.4mm, printing at the speed of 5-8mm/s and the layer thickness of 50 mu m, and after printing, performing post-treatment for 4 hours in a 60 ℃ vacuum drying oven, and removing residual supporting materials for later use.

3. Energy harvesting layer 1 assembly

3-5 Groups of triboelectric nano generator units 7 are symmetrically stuck on the edge of a flexible PI substrate (with the thickness of 20 mu m), the distance between adjacent units is 5-8mm, and the triboelectric nano generator units are fixed by adopting medical epoxy adhesive (the elastic modulus after curing is less than or equal to 1 MPa).

One end of the PLGA airflow guiding channel 8 is aligned with the gap of the triboelectric nano-generator unit 7, the other end faces to the preset opening end of the chest wall tissue gap, and the PLGA airflow guiding channel is adhered and fixed by medical glue, so that the channel is attached to the surface of the triboelectric nano-generator unit 7 (the airflow can smoothly pass through).

The miniature super capacitor with the specification of 4-6mm multiplied by 2.5-3.5mm multiplied by 0.8-1.2mm and the capacity of 400-600 mu F is adopted, and is welded with the electrode of the triboelectric nano generator unit 7 through a flexible gold wire with the diameter of 20-30 mu m, and is connected with a miniature rectifying circuit (the output voltage is 3.0-3.3V) in series, so that the assembly of the energy acquisition layer 1 is completed.

2. Preparation of the second telescoping adjustment layer 6 (core function: interlayer telescoping adaptation + positioning constraint)

1. Preparation of flexible hinge array 21

The medical TPU film with the thickness of 50-80 mu m is cut into a cross-shaped flexible hinge array 21 structure (the width of a single hinge is 0.5-0.8mm and the interval is 2-3 mm) by a laser engraving technology, so that an array covering the energy acquisition layer 1 is formed.

2. Preparation of low friction PTFE positioning groove 22

PTFE is used as a raw material, a low-friction PTFE positioning groove 22 with the diameter of 1.5-2.0mm and the depth of 0.3-0.5mm is prepared by adopting a mould pressing method, and the groove is fixed on the surface of the TPU flexible hinge array 21 by using a medical adhesive according to the interval (5-6 mm) of matching with the positioning protrusion of the subsequent monitoring sensing layer 2.

3. Interlayer bonding

And integrally adhering the prepared second telescopic adjusting layer 6 on the surface of the PI substrate of the energy acquisition layer 1, ensuring that the position of the low-friction PTFE positioning groove 22 is accurate, applying 0.1MPa pressure after adhering, and curing for 1h at room temperature.

3. Preparation of monitoring sensing layer 2 (core function: diaphragmatic Strain Signal acquisition and conversion)

1. Preparation of graphene strain sensing array 10 by laser engraving

And taking a PI film with the thickness of 20-30 mu m as a substrate, and etching the surface of the PI film by a laser engraving technology to form the graphene strain sensing array 10 with the specification of 5 multiplied by 5-7 multiplied by 7, wherein the single unit size is 1.8-2.2mm multiplied by 1.8-2.2mm, and the unit spacing is 1.2-1.8mm.

And growing graphene on the surface of the etched PI substrate by adopting a Chemical Vapor Deposition (CVD) method, wherein the thickness is 5-10nm, and defining electrode leads by a photoetching technology to form the laser engraved graphene strain sensing array 10 with the initial resistance of 4-6kΩ.

2. The signal conditioning module 11 is integrated with the data preprocessing module 12

The miniature operational amplifier (gain 800-1200 times) and the filter circuit (cut-off frequency 8-12 Hz) are welded on a flexible PCB to form a signal conditioning module 11, and an STM32L476MCU (built-in bilinear interpolation and region weighted fusion algorithm program) is welded at a reserved position of the same flexible PCB to form a data preprocessing module 12.

The output end of the signal conditioning module 11 is connected with an electrode lead of the laser engraved graphene strain sensing array 10 through an ultrafine copper wire with the diameter of 50 mu m, and the data preprocessing module 12 is electrically connected with the signal conditioning module 11 to complete module integration.

3. Monitoring sensing layer 2 packaging and positioning protrusion preparation

And packaging the integrated module and the laser engraved graphene strain sensing array 10 by using a PI film with the thickness of 10-15 mu m, ensuring that a lead is not pulled in the packaging process, sealing the edge by adopting laser welding, and testing the response time after packaging to be less than or equal to 15ms.

And PTFE positioning bulges with the diameter of 1.4-1.9mm and the height of 0.3-0.5mm are prepared on the lower surface (corresponding to the position of the low-friction PTFE positioning groove 22 of the second telescopic adjusting layer 6) of the packaged monitoring sensing layer 2 through 3D printing, so that the bulges are ensured to be accurately matched with the low-friction PTFE positioning groove 22, and the preparation of the monitoring sensing layer 2 is completed.

4. Preparation of the first expansion and contraction adjustment layer 5 (core function: amplified impact + energy absorption)

1. Preparation of super-elastic SMA fiber woven mesh 19

Super-elastic SMA fiber (nickel titanium alloy) with the diameter of 50-80 μm is adopted, a warp-weft knitting method is adopted to knit into a super-elastic SMA fiber knitted net 19 (mesh size is 1-2mm multiplied by 1-2 mm), and annealing treatment is carried out for 30min at 500 ℃ after knitting, so that the elastic recovery performance is enhanced.

2. Preparation of porous PLGA buffer layer 20

Mixing PLGA powder with a pore-forming agent (sodium chloride particles, particle size of 100-200 μm) according to a mass ratio of 3:1, pressing into a film with thickness of 100-150 μm, soaking in deionized water for 24h to remove the pore-forming agent, and forming the porous PLGA buffer layer 20 (porosity of 40% -50%).

3. Preparation of composite layers

The super-elastic SMA fiber woven net 19 and the porous PLGA buffer layer 20 are compounded through a medical polyurethane adhesive, the compounding pressure is 0.05MPa, the curing is carried out at 60 ℃ for 1.5 hours, the super-elastic SMA fiber woven net and the porous PLGA buffer layer are ensured to be free of stripping, the preparation of the first telescopic adjusting layer 5 is completed, then the super-elastic SMA fiber woven net and the porous PLGA buffer layer are adhered to the upper surface of the monitoring sensing layer 2, and the alignment edge is fixed.

5. Control intervention layer 3 preparation (core function: respiratory assessment + abnormal intervention)

1. Neuromuscular electrical stimulation unit 15 preparation

Cutting platinum iridium alloy wire (diameter of 0.6-1.0 mm) into electrodes with length of 2-3mm, grinding one end into hemispherical shape (exposed end), and welding the other end with flexible wire with diameter of 50 μm, and preparing 3-5 groups for use.

The miniature stimulation signal generator (capable of outputting signals with the frequency of 15-55Hz and the pulse width of 180-220 mu s) is adopted and welded on a flexible PCB, and an electrode lead of the neuromuscular electrical stimulation unit 15 is connected with the output end of the generator, so that the exposed end of the electrode faces the direction of a preset protruding biocompatible layer 4.

Ble communication module 13 is integrated with data analysis module 14

The nRF52832BLE communication module 13 (transmission rate is 0.8-1.2 Mbps) and the data analysis module 14 (compatible with STM32L476 MCU) are welded on the same flexible PCB, and are connected with the flexible PCB of the neuromuscular electrical stimulation unit 15 through a flat cable, so that a core circuit of the control intervention layer 3 is formed.

And the test circuit has the function of ensuring that the transmission distance of the BLE communication module 13 is less than or equal to 12m, the data analysis module 14 can normally calculate the phase difference between REI and judgment, and the neuromuscular electro-stimulation unit 15 can output 0.08-0.55mA current for standby after reaching the standard.

3. Controlling intervention layer 3 fixation

The prepared control intervention layer 3 is stuck on the upper surface of the first telescopic adjusting layer 5, so that the electrode position of the neuromuscular electrical stimulation unit 15 corresponds to the diaphragmatic nerve innervation area (refer to the CT positioning size before operation), and the edge is fixed by using a medical adhesive to avoid displacement.

6. Preparation and integral integration of biocompatible layer 4 (core function: biosafety + structural protection)

1. Preparation of biocompatible layer 4

The inner PLGA coating 18 is prepared by dissolving PLGA in dichloromethane (concentration 10%), uniformly coating on the surface of the control intervention layer 3 by spraying technology, spraying thickness of 0.08-0.12mm, and air-drying in a fume hood (room temperature 25 ℃ for 2 h) to form an inner layer.

And (3) preparing an outer PEEK film 17, namely heating medical PEEK particles to 340 ℃ for melting by adopting a hot-press molding technology, pressing the PEEK particles into a PEEK film 17 with the thickness of 0.08-0.12mm, and cutting the PEEK film 17 into a shape matched with the integral size of the patch.

2. Integral sealing and integration

The PEEK film 17 is covered on the surface of the PLGA coating 18, the edge is sealed by adopting a laser welding technology (laser power is 5-8W and scanning speed is 10 mm/s), the welding strength is ensured to meet the tensile test F & gt8N, the soaking test (simulating body fluid environment) is carried out after welding, and the leakage rate is verified。

Medical grade flexible silica gel (thickness is less than or equal to 0.1 mm) is filled in gaps between layers, the medical grade flexible silica gel is cured for 1h at room temperature, the overall thickness is ensured to be less than or equal to 2.5mm, and finally, aseptic treatment (ethylene oxide sterilization, sterilization dose of 25 kGy) is carried out, so that the whole patch preparation is completed.

7. Finished product performance test

And the performance test of the energy collection layer 1 comprises the steps of introducing 0.4-2.2m/s of air flow, and testing the power supply stability (continuously outputting 3.0-3.3V) of the power supply stability of the energy storage module 9, wherein the output voltage of the triboelectric nano generator unit 7 is 3.2-5.3V.

And (3) monitoring the accuracy test of the sensing layer 2, namely simulating diaphragm contraction, testing the displacement accuracy of the laser engraved graphene strain sensing array 10 by 0.3-0.6mm, and controlling the response time of the data preprocessing module 12 to be less than or equal to 15ms.

Control intervention layer 3 intervention function test, simulation of abnormal breathing pattern (REI <25-35 or phase difference >160-200 °), test of triggering accuracy of neuromuscular electrical stimulation unit 15, current regulation compliance。

And (3) stretching performance test, namely applying stretching deformation of +/-30%, and testing that the first stretching adjusting layer 5, the second stretching adjusting layer 6 and other layers are not peeled off, so that the functions are normal.

Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.

Claims

1. An implantable nanogenerator-driven intelligent respiratory training and monitoring patch, characterized in that it adopts a stretchable layered structure, which includes, from top to bottom, an energy harvesting layer, a monitoring and sensing layer, a control and intervention layer and a biocompatible layer, and each layer is integrated through a flexible connection structure;

The energy harvesting layer includes a triboelectric nanogenerator unit, an airflow guiding channel, and an energy storage module. The triboelectric nanogenerator unit adopts a layered structure composed of a polydimethylsiloxane flexible film and graphene electrodes, with 3-5 groups arranged symmetrically at the edge of the patch. The airflow guiding channel is an arc-shaped microchannel structure, with one end connected to the implantation area next to the trachea and the other end open in the chest wall tissue space. The energy storage module is a miniature supercapacitor, which is connected to the triboelectric nanogenerator unit through a flexible wire.

The monitoring sensing layer includes a laser-engraved graphene strain sensing array, a signal conditioning module, and a data preprocessing module. The graphene strain sensing array is a 5×5-7×7 sensor unit array, and its coverage area matches the projection area of the diaphragm on the chest wall. The signal conditioning module integrates an operational amplifier and a filter circuit. The data preprocessing module is a micro MCU that converts the strain signal into diaphragm contraction displacement data through a bilinear interpolation algorithm and generates a diaphragm contraction vector map by combining a region-weighted fusion algorithm.

The control intervention layer includes a Bluetooth Low Energy communication module, a data analysis module, a neuromuscular electrical stimulation unit, and a trigger control module. The data analysis module pre-stores diaphragmatic movement characteristic parameters of normal breathing patterns and evaluates respiratory efficiency through a respiratory efficiency index calculation algorithm, the formula of which is:

in, The maximum diaphragmatic contraction displacement within 3-5 respiratory cycles is given by f, respiratory rate, σ, standard deviation of displacement data during the same period, and k, correction factor. Synchronicity is determined using the formula for synergistic chest and abdominal movements.

in, For chest displacement data, For abdominal displacement data, Phase difference, normal phase difference The threshold is set to 30-60°.

The neuromuscular electrical stimulation unit includes 3-5 sets of microelectrodes and a stimulation signal generator. The trigger control module can trigger the neuromuscular electrical stimulation unit when an abnormal breathing pattern is detected.

The biocompatible layer uses a medical-grade polyetheretherketone film as the outer layer and a polylactic acid-glycolic acid copolymer coating as the inner layer, with both layers having a thickness of 0.08-0.12 mm.

The stretchable stacked structure further includes a first stretchable adjustment layer disposed between the energy harvesting layer and the monitoring and sensing layer, and a second stretchable adjustment layer disposed between the monitoring and sensing layer and the control intervention layer. The first stretchable adjustment layer is a super-elastic SMA fiber woven mesh and a porous PLGA composite buffer layer, and the second stretchable adjustment layer includes a flexible hinge array and multiple low-friction PTFE positioning grooves.

2. The implantable nanogenerator-driven intelligent respiratory training and monitoring patch according to claim 1, characterized in that, in the energy harvesting layer, the area of each group of triboelectric nanogenerator units is 12-18mm × 8-12mm, the diameter of the airflow guiding channel is 1.0-1.4mm, and the capacitance of the micro supercapacitor is 400-600μF; a single group of triboelectric nanogenerator units outputs a voltage of 3.2-5.3V under a respiratory airflow velocity of 0.4-2.2m/s, and the output voltage satisfies:

in, For airflow velocity, The coefficients are experimental fitting values, and the total power consumption of the device is ≤120μW.

3. The implantable nanogenerator-driven intelligent respiratory training monitoring patch according to claim 1, characterized in that, in the monitoring sensing layer, each sensing unit of the graphene strain sensing array has an area of 1.8-2.2mm × 1.8-2.2mm, a sensing unit spacing of 1.2-1.8mm, an initial resistance value R of 4-6kΩ, and the relationship between strain and resistance change satisfies:

in, This is the change in resistance. The initial resistance is K = 120-150, the gauge coefficient is K = 120-150, and the strain value is ε.

The operational amplifier in the signal conditioning module has a gain of 800-1200 times, and the cutoff frequency of the filter circuit is 8-12Hz. The data preprocessing module uses an STM32L476 or an equivalent MCU. In the bilinear interpolation algorithm, the fitting coefficients are solved by solving a system of equations based on the coordinates and displacement data of four adjacent sensing units. After regional weighted fusion, the vector vector displacement accuracy is 0.3-0.6mm.

4. The implantable nanogenerator-driven intelligent respiratory training and monitoring patch according to claim 1, characterized in that, in the control intervention layer, the BLE communication module adopts an nRF52832 or a chip of equivalent performance from the same series, with a transmission rate of 0.8-1.2 Mbps and a transmission distance ≤12 m; when the data analysis module calculates REI, Take the maximum displacement over 3-5 respiratory cycles. Take the standard deviation of displacement data from the same period:

,

This represents the displacement value for a single cycle. The average displacement; in the determination of synchronicity of chest and abdominal movements, the normal phase difference is... The threshold is set at 30-60°, and the criteria for determining abnormal breathing pattern are REI < 23-35 for 4-6 seconds or Lasts 2-4 seconds.

5. The implantable nanogenerator-driven intelligent respiratory training and monitoring patch according to claim 1, characterized in that, in the control intervention layer, the microelectrode diameter of the neuromuscular electrical stimulation unit is 0.6-1.0 mm, and the exposed end of the electrode protrudes 0.15-0.25 mm from the biocompatible layer; the pulse signal parameters generated by the stimulation signal generator are: frequency 15-55 Hz, current intensity 0.08-0.55 mA, pulse width 180-220 μs; the stimulation intensity is correlated with the diaphragm functional state, satisfying...

in Only when Enabled at any time.

6. The implantable nanogenerator-driven intelligent respiratory training and monitoring patch according to claim 1, characterized in that the preparation process of the energy harvesting layer includes: preparing a PDMS flexible film with a thickness of 45-55 μm by molding; depositing a graphene electrode with a thickness of 8-12 nm on the surface of the film by magnetron sputtering; assembling it into a triboelectric nanogenerator unit; preparing an arc-shaped airflow guiding channel made of PLGA material by 3D printing; bonding it to the triboelectric nanogenerator unit with medical adhesive; welding a micro supercapacitor with a size of 4-6 mm × 2.5-3.5 mm × 0.8-1.2 mm to the triboelectric nanogenerator unit with gold wire and connecting it to a rectifier circuit, wherein the output voltage after rectification meets the requirements. .

7. The implantable nanogenerator-driven intelligent respiratory training monitoring patch according to claim 1, characterized in that the preparation process of the monitoring sensing layer includes: preparing a graphene strain sensing array on a polyimide substrate with a thickness of 20-30 μm by laser engraving technology; electrically connecting the signal conditioning module and the data preprocessing module to the sensing array through a flexible PCB board, wherein the data preprocessing module has built-in program code for bilinear interpolation algorithm and region weighted fusion algorithm, and is encapsulated in a PI film, and the response time of the sensing unit after encapsulation is ≤15ms.

8. The implantable nanogenerator-driven intelligent respiratory training monitoring patch according to claim 1, characterized in that the triggering condition of the trigger control module is set as follows: the duration of the abnormal breathing mode is ≥4-6s; after triggering, the stimulation parameters of the neuromuscular electrical stimulation unit are adjusted by a graded stimulation algorithm, and the current intensity adjustment formula is It=It-1+0.05-0.1mA, which is only activated when the abnormal mode is not corrected within 5-8s after stimulation at time t-1, and It<0.55mA; the stimulation duration T=10+2×(t-1), where t is the number of stimulations, t≥1, which is only activated when the abnormal mode is not corrected within 5-8s after stimulation at time t-1, and It<0.55mA; the stimulation duration is T=10+2×(t-1), where t is the number of stimulations, and it is only activated when the abnormal mode is not corrected within 5-8s after stimulation at time t-1. The system is activated if the abnormal pattern is not corrected within 5-8 seconds after stimulation, and the intervention results are fed back to the nurse's terminal in real time via the BLE communication module.

9. The implantable nanogenerator-driven intelligent respiratory training and monitoring patch according to claim 1, characterized in that the biocompatible layer is covered on the surface of the control intervention layer using thermoforming technology, and sealed by laser welding; the welding strength meets the requirements of tensile testing. The waterproof and tissue fluid intrusion resistance meets the leakage rate requirements in the immersion test. .

10. The implantable nanogenerator-driven intelligent respiratory training and monitoring patch according to claim 1, characterized in that the microelectrodes of the neuromuscular electrical stimulation unit are made of platinum-iridium alloy or medical-grade pure silver, and the electrode positions correspond one-to-one with the nerve innervation areas related to diaphragmatic movement; during stimulation, diaphragmatic displacement data is collected in real time through a feedback adjustment algorithm, and if the displacement fluctuation amplitude... , Displacement upon stimulation To stimulate forward displacement, the current intensity ΔI = 0.03-0.08 mA is automatically reduced, and the adjusted current... .