Differential near-infrared probe sensor for precise non-invasive intracranial pressure monitoring and monitoring method

Abstract

The invention discloses a differential near-infrared probe sensor for precise noninvasive intracranial pressure monitoring and a monitoring method. The differential near-infrared probe sensor comprises a patch probe data acquisition module, a signal processing and transmitting module and an integrated display module, the patch probe data acquisition module comprises a flexible substrate, an LED light source and a photoelectric detector, wherein the LED light source and the photoelectric detector are arranged on the flexible substrate along a straight line; the photoelectric detector comprises a near-end detector which is 30 + / -0.5 mm away from the center of the LED light source and a far-end detector which is 40 + / -0.5 mm away from the center of the LED light source, and the LED light source, the near-end detector and the far-end detector are arranged in an axial differential detection layout. Compared with a near-infrared patch with a single sensor, the differential patch disclosed by the invention has the advantages that the anti-interference capability, the signal specificity, the sensitivity and the reliability of monitoring are remarkably improved through a double-sensor design and a differential signal processing technology, and a better solution is provided for noninvasive intracranial pressure monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of intracranial pressure monitoring, and particularly to a differential near-infrared probe sensor and a monitoring method for accurate non-invasive intracranial pressure monitoring. Background Art

[0002] The normal intracranial pressure of humans is 7-15 mmHg. Increased intracranial pressure (ICP) is common in brain diseases such as brain trauma, stroke, brain tumors, hydrocephalus, and intracranial infections. Intracranial hypertension can cause compression of brain tissue and even lead to brain herniation, resulting in adverse consequences. Therefore, ICP monitoring plays a very important role in neurocritical care.

[0003] ICP monitoring techniques can be divided into invasive and non-invasive categories. Invasive monitoring techniques require drilling holes in the skull and installing sensors at specific intracranial locations, such as the lateral ventricle, brain parenchyma, subdural, and epidural, etc., to achieve ICP monitoring. Among them, external ventricular drainage is regarded as the current gold standard for intracranial pressure monitoring due to its advantages such as high accuracy and real-time monitoring.

[0004] Currently, directly and continuously monitoring intracranial pressure by implanting an intracranial pressure probe is the most reliable means to evaluate the dynamic changes of a patient's intracranial pressure. However, due to factors such as high cost, complex operation, and operation-related complications such as intracranial hemorrhage and infection, the clinical application of invasive intracranial pressure has been considerably limited.

[0005] In view of this, small bioabsorbable pressure sensors and small wireless ultrasonic sensors implantable in the skull have been developed. The above devices exhibit excellent biocompatibility and overcome the disadvantage that previous invasive monitoring techniques would cause inconvenience to patients. Although the above technologies have tried their best to reduce the infection risk, reduce the nursing cost, and reduce the pain of patients, they still require surgical implantation of small sensors.

[0006] Non-invasive and continuous monitoring is the ideal choice for ICP monitoring. Currently, a variety of non-invasive and continuous monitoring methods have been developed. These monitoring methods all monitor physiological sites closely related to intracranial pressure and establish a mathematical model with invasive monitoring methods to judge their accuracy. For example, ophthalmic ultrasound measurement of the optic nerve sheath diameter (ONSD), pupillary reactivity detection, flash visual evoked potential (FVEP), transcranial Doppler ultrasound (TCD), TMD, CT, MRI, and electroencephalogram (EEG) techniques.

[0007] Among them, the ONSD has low precision, cannot ignore continuous eye movements, and cannot be used for patients with facial trauma. Pupillary reactivity detection is difficult to apply in clinical practice and can currently only be used as a screening tool before invasive intracranial pressure monitoring. In addition, drugs such as sedatives and vasopressors can affect pupillary reactivity. FVEP has poor effects in patients with poor eyesight, and there are quite large individual differences. TCD has signal transmission attenuation through the skull, and there is intensity attenuation when the TCD signal is transmitted through the skull, resulting in a decrease in the signal-to-noise ratio. There are problems of non-linear distortion and insufficient stability in signal acquisition, and a certain acoustic window is required, which has high requirements for operators. TMD is also limited by muscle relaxants and sedatives and is not applicable to anesthetic patients, patients with irritability, and patients with hearing defects. CT has electromagnetic radiation, cannot provide specific intracranial pressure values, and its specificity and sensitivity are not high enough. MRI is expensive and cannot display specific intracranial pressure values, which is not conducive to real-time monitoring. MRI indirectly judges the increase in intracranial pressure by monitoring various parameters, such as empty sella protrusion, optic nerve protrusion, vertical tortuosity, optic nerve sheath dilation, and flattening behind the eyeball. EEG detection requires wearing an electroencephalogram cap and applying electroencephalogram paste, etc., which takes a long time and is not suitable for some emergency situations.

[0008] In addition, most of the above-mentioned technologies are in the experimental research stage, and generally have disadvantages such as large measurement errors, inability to continuously monitor, or harsh usage conditions.

[0009] Changes in intracranial pressure will affect the thickness of the dura mater and subarachnoid space. When the thickness of the dura mater and subarachnoid space changes, it will affect the absorption and attenuation of light. Near-infrared spectroscopy (NIRS) has been widely used in the biomedical field due to its non-invasiveness, convenience, and repeatability. However, a single near-infrared sensor may have errors when monitoring intracranial pressure. This is mainly because the target monitoring area of the non-invasive monitoring system is the dura mater and subarachnoid space, and the patch of a single sensor simultaneously monitors the scalp, skull, dura mater, and subarachnoid space, and cannot eliminate the signal interference and measurement errors brought by the scalp and skull. Summary of the Invention

[0010] To solve the above problems, the present invention provides a differential near-infrared probe sensor and a monitoring method for accurate non-invasive intracranial pressure monitoring. Two near-infrared sensors are respectively placed at fixed positions, one as a reference sensor and the other as a measurement sensor. By continuously monitoring the signal changes of the two sensors and calculating their differences, the errors brought by non-target monitoring areas such as the scalp and skull can be effectively eliminated.

[0011] To achieve the above object, the present invention provides a differential near-infrared probe sensor for precise non-invasive intracranial pressure monitoring, including a patch probe data acquisition module, a signal processing and transmission module, and an integrated display module. The patch probe data acquisition module includes a flexible substrate, an LED light source and a photodetector arranged linearly on the flexible substrate;

[0012] The photodetector includes a proximal detector 30 ± 0.5 mm away from the center of the LED light source and a distal detector 40 ± 0.5 mm away from the center of the LED light source. The arrangement of the LED light source, the proximal detector and the distal detector adopts an axial differential detection layout.

[0013] Preferably, the LED light source is a miniaturized near-infrared light source array with a luminous wavelength of 800 - 820 nm.

[0014] Preferably, the luminous timing control part of the LED light source adopts a time-division multiplexing control system and a luminous timing control circuit. The time-division multiplexing control system is provided with a dynamic power modulation unit, and the luminous timing control circuit adopts PWM dimming technology. The luminous timing control circuit divides the driving of the LED light source into a high-sensitivity mode and a conventional detection mode;

[0015] When the LED light source is in the high-sensitivity mode, the LED light source emits light for 0.5 s within 1 s, with a pulse frequency of 100 Hz and a duty cycle of 10%;

[0016] When the LED light source is in the conventional detection mode, the LED light source emits light for 0.5 s within 1 s, with a pulse frequency of 50 Hz and a duty cycle of 5%.

[0017] Preferably, the receiving timing of the photodetector adopts full-time receiving. The photodetector is provided with an intelligent trigger mechanism, and automatically switches to the high-speed sampling mode when the detected signal fluctuation exceeds 20%.

[0018] Preferably, the signal processing and transmission module includes an integrated signal processing circuit, a Python digital signal processing module, and a signal transmission module;

[0019] The front end of the integrated signal processing circuit is composed of a signal filtering, amplifying and preprocessing module and a high-precision A / D conversion unit. The signal filtering, amplifying and preprocessing module includes a filtering unit, a hardware filter bank, a digital filtering algorithm, and a low-noise amplifying circuit;

[0020] The filtering unit adopts adaptive dynamic filtering. The hardware filter bank is a high-order Butterworth filter and a power frequency filter. The digital filtering algorithm adopts the LMS algorithm. The low-noise amplifying circuit adopts a three-stage cascaded transimpedance amplifier;

[0021] The high-precision A / D conversion unit applies oversampling technology and sets an optoelectronic isolation transmission channel.

[0022] Preferably, the signal transmission module is electrically connected to the integrated display module. The integrated display module receives and displays the ICP value processed by the signal processing and transmission module. The integrated display module adopts a dual-mode display design, and the dual modes include a monitoring mode and a diagnosis mode;

[0023] The monitoring mode displays the real-time ICP waveform of the patient and the rising and falling trend chart of ICP over a period of time in real time;

[0024] The diagnosis mode displays the ΔICP / time curve, showing the rate and amplitude of the ICP change.

[0025] On the other hand, the present invention provides a monitoring method for the differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring as described above, including the following steps:

[0026] S1. Fix the patch probe data acquisition module, turn on the LED light source, and the photodetector collects the optical signal reflected by the cranial tissue and converts it into an electrical signal;

[0027] Among them, the proximal detector collects the optical signals reflected by the scalp and skull, and the distal detector collects the optical signals reflected by the dura mater, arachnoid mater, and subarachnoid space;

[0028] S2. The signal processing and transmission module receives the electrical signal from the photodetector. After the electrical signal is pre-filtered and amplified by the signal filtering and amplification preprocessing module, it is converted into a digital signal by the high-precision A / D conversion unit;

[0029] S3. The Python digital signal processing module processes the digital signal that has been converted using the Python language. According to the Lambert-Beer law, the digital signal is converted into the absorbance change, and the calculation formula is as follows:

[0030]

[0031] Among them, A is the absorbance, which is used to describe the degree of light absorption, I t represents the intensity of the transmitted light, I0 represents the intensity of the incident light, ε'(λ) is the molar extinction coefficient of the substance, c is the concentration of the absorbing substance, and l represents the optical path distance;

[0032] The Python digital signal processing module uses 2 s as a monitoring period. After removing the singular values of the signals within a single monitoring period, the absorbance is calculated. The absorbance value is data-coupled with the ICP signal of the synchronous invasive monitoring. According to the correlation between the absorbance value and the ICP signal, a parameter model is established. Through the accumulation of data, the accuracy and stability of the parameter model are optimized, and a direct mathematical model between the absorbance and the ICP signal is established;

[0033] S4. The signal transmission module transmits the processed signal to the integrated display module.

[0034] The differential near-infrared probe sensor and monitoring method for precise non-invasive intracranial pressure monitoring of the present invention have the following beneficial effects:

[0035] (1) The present invention uses an LED light source to emit near-infrared light and uses a detector to collect the optical signal passing through the cranial tissue; the signal processing module consists of multiple processing units to complete the preprocessing of the optical signal, convert the collected optical signal into an electrical signal and then transmit it, and complete the post-processing of the signal in a dedicated supporting software, couple the measured optical signal value with the simultaneously monitored ICP value, and obtain the mathematical model of intracranial pressure and the absorbance of the dura mater and its subarachnoid space; the display module completes the calibration of the signal collected by the device, and at the same time establishes a dual-mode display to output the calculated ICP value and the monitoring waveform in real time.

[0036] (2) The present invention adopts a differential design concept, optimizes the design method of "single light source - single detector", and adopts the structure of "single light source - dual detectors", effectively excluding the interference of superficial brain tissue on the optical signal, and improving the anti-interference ability, signal specificity, sensitivity and reliability of non-invasive intracranial pressure monitoring.

[0037] (3) Compared with the traditional invasive ICP monitoring method, the near-infrared patch technology does not require surgical implantation of a catheter, reducing the risk of infection and the discomfort of patients.

[0038] (4) The present invention can realize bedside, continuous and real-time ICP monitoring, providing more comprehensive patient status information for clinicians, and helping to adjust the treatment plan in time. The infrared patch technology reduces the equipment cost and operation complexity, making it easier to promote in medical institutions at all levels, and having significant economic and social benefits.

[0039] The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments. Brief Description of the Drawings

[0040] Figure 1 It is a schematic diagram of the overall design of the present invention;

[0041] Figure 2 It is a schematic diagram of the working process of the flexible circuit and the hardware circuit of the present invention;

[0042] Figure 3 It is a schematic diagram of the monitoring principle of the patch probe data acquisition module of the present invention;

[0043] Figure 4 It is a schematic diagram of the structure of the patch probe data acquisition module of the present invention;

[0044] Figure 5This is the signal processing flow chart of the present invention;

[0045] Figure 6 It is a graph of the transmitted light intensity and intracranial pressure data collected by the sensor.

[0046] Reference numerals

[0047] 1. Scalp; 2. Skull; 3. Dura mater; 4. Arachnoid mater; 5. Subarachnoid space; 6. Proximal detector; 7. Distal detector; 8. Light source; 9. Plug. Detailed implementation manners

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following further describes the embodiments of the present invention in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application. Examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0049] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0050] Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0051] Embodiment 1

[0052] A near-infrared probe sensor for non-invasive intracranial pressure monitoring based on a differential dual-detector, the overall structure is as Figures 1 - 2 shown, including a patch probe data acquisition module, a signal processing and transmission module, and an integrated display module. Through the deep integration of flexible electronic technology and near-infrared spectroscopy analysis, real-time dynamic monitoring of intracranial pressure changes is achieved.

[0053] As Figure 3 shown, it is a schematic structural diagram of the patch probe data acquisition module. The patch probe data acquisition module includes a probe patch, and the probe patch is connected to a dedicated plug 9 that is convenient for plugging and unplugging, for electrical connection with the signal processing and transmission module.

[0054] The probe patch includes a flexible substrate, an LED light source 8, and a photodetector. The LED light source 8 emits near-infrared light at 820 nm, and the optical signals passing through the cranial tissue are collected by the photodetector. The LED light source 8 and the photodetector are arranged in an axial differential detection layout, with 1 LED light source 8 and 2 photodetectors arranged in a straight line on the flexible substrate.

[0055] The LED light source 8 uses a miniaturized near-infrared light source array with a luminous wavelength of 820 nm. It has high sensitivity when passing through the cranial tissue and can effectively receive the diffuse reflection light signals after being affected by the dura-arachnoid complex structure.

[0056] The photodetector includes a proximal detector 6 at a distance of 30 ± 0.5 mm from the center of the light source 8 and a distal detector 7 at a distance of 40 ± 0.5 mm from the center of the light source 8.

[0057] The schematic diagram of the patch monitoring principle is as Figure 4 shown. The proximal detector 6 is used to collect the reflection signals of the superficial tissues (scalp 1, skull 2), and the distal detector 7 is used to capture the reflection signals of the deep tissues (dura mater 3, arachnoid mater 4, subarachnoid space 5). Through subsequent algorithm processing, the optical signals carrying the physiological states of the dura mater and the subarachnoid space can be calculated differentially, and the problems of optical signal contamination in the superficial brain tissue can be effectively eliminated.

[0058] The luminous timing control part of the LED light source 8 uses a time-division multiplexing control system, adding a dynamic power modulation unit. The timing control circuit in it uses PWM dimming technology to divide the driving of the light source 8 into a high-sensitivity mode and a conventional detection mode. When working in the high-sensitivity mode, the LED light source 8 emits light for 0.5 s within 1 s, with a pulse frequency of 100 Hz and a duty cycle of 10%; in the conventional monitoring mode, the LED light source 8 emits light for 0.5 s within 1 s, using a pulse frequency of 50 Hz and a duty cycle of 5%. Through this technology, the light power on the surface of the biological tissue is controlled below the safety threshold (refer to the ANSI standard).

[0059] Considering the differences in the propagation path and time of the photon beam in the cranial tissue, the receiving timing of the photodetector adopts full-time reception. In addition, an intelligent trigger mechanism is incorporated to switch the working state with high sensitivity. When the detected signal fluctuation exceeds 20%, it automatically switches to the high-speed sampling mode.

[0060] The probe patch fully considers the surface contact optimization design and introduces a gradient adhesive layer structure: a low-viscosity silicone is used in the central area to cover the optical element area, which is convenient for repeated adjustment and positioning. To ensure its firm adhesion, medical-grade silicone is selected; a high-viscosity acrylic pressure-sensitive adhesive is used in the edge area to form an annular sealing band to prevent the edge from warping.

[0061] The processing flow of the optical signals collected by the patch probe data acquisition module is as follows Figure 5 as shown. The optical signals collected by the patch probe data acquisition module need to be further converted into electrical signals before being processed by the subsequent stage. The signal processing and transmission module is based on the STM32 single-chip microcomputer, and comprehensively utilizes an analog-to-digital converter (ADC) and a preamplifier circuit to achieve precise processing of the optical signals. The overall design consists of an integrated signal processing circuit, a Python digital signal processing module, and a signal transmission module.

[0062] The front-end design of the integrated signal processing circuit consists of a signal filtering, amplification, and preprocessing module and a high-precision A / D conversion unit. First, the signal is filtered at the front end to remove high-frequency noise and low-frequency drift. Subsequently, the filtered signal is amplified to increase the amplitude of the signal, making it suitable for subsequent digital processing.

[0063] The signal filtering, amplification, and preprocessing module includes: a filtering unit, a hardware filter bank, a digital filtering algorithm, and a low-noise amplification circuit. The filtering unit uses adaptive dynamic filtering. The hardware filter bank designs high-order Butterworth filters and power frequency filters. The digital filtering algorithm uses the LMS algorithm to process motion artifacts, eliminate them, and perform wavelet threshold denoising to ensure signal stability and accuracy. The low-noise amplification circuit uses a three-stage cascaded transimpedance amplifier to reasonably control the gain and ensure that the signal does not lose information due to oversaturation.

[0064] The high-precision A / D conversion unit uses oversampling technology to ensure that the collected optical signals can accurately reflect the minute changes in intracranial pressure. At the same time, an optoelectronic isolation transmission channel is used to increase the anti-interference ability of the system.

[0065] Python language is used to process the already converted digital signals, including steps such as absorbance calculation, signal coupling, and model establishment. According to the Lambert-Beer law, the digitized signals are converted into absorbance changes. The absorbance value reflects the physiological state changes of the intracranial dura mater and subarachnoid space, and this change is closely related to the increase and change of intracranial pressure.

[0066] The calculation formula is as follows:

[0067]

[0068] where A is the absorbance, used to describe the degree of light absorption, I t represents the intensity of the transmitted light, I0 represents the intensity of the incident light, ε'(λ) is the molar absorptivity of the substance, c is the concentration of the absorbing substance, and l represents the optical path distance.

[0069] To ensure the stability of monitoring and exclude the interference of optical signals caused by patient movement and medical operations during the monitoring process, 2 s is selected as a monitoring cycle. After removing the singular values of the signals within a single monitoring cycle, absorbance calculations are performed. Data coupling is carried out with the ICP signals synchronously monitored in the intensive care unit, and a parameter model for the correlation between the two is established. The linear mixed-effects model is used to evaluate the effectiveness of the experimental data for multiple patients. Through the accumulation of multiple clinical trial data, the accuracy and stability of data processing are optimized. Finally, using linear regression analysis and machine learning algorithms, a direct mathematical model between absorbance and ICP signals is established. By comparing the relationship between invasive ICP data and NIRS signals, the mathematical derivation of non-invasive ICP monitoring is ultimately achieved.

[0070] After signal processing is completed, it needs to be transmitted to the integrated display module. The signal transmission module uses wired data transmission and transmits all processed data to the integrated display module through the wired interface USB. This transmission method ensures that the data is not interfered by wireless signals during transmission, guaranteeing the stability and real-time nature of the data, especially for the high reliability requirements in the clinical environment.

[0071] The integrated display module is mainly responsible for receiving and displaying the intracranial pressure (ICP) values and waveforms after signal processing and providing detailed data analysis functions. This module can display the ICP signals of patients in real time and show the fluctuations of intracranial pressure through visual waveform diagrams, facilitating doctors to quickly evaluate the dynamic changes of the patient's condition. In addition to real-time waveform display, the system will also display the real-time ICP values and provide trend charts. A data backtracking function is added to observe the long-term trend of ICP changes and assist in clinical decision-making.

[0072] The integrated display module adopts a dual-mode display design. In the monitoring mode, it can display the ICP waveform and trend chart of the patient in real time. The ICP waveform can show the real-time changes of intracranial pressure, and the trend chart shows the rising and falling trend of ICP over a period of time, facilitating doctors to observe the fluctuations and make timely clinical interventions. The diagnostic mode will display the ΔICP / time curve to help doctors identify abnormal changes in intracranial pressure. The ΔICP / time curve shows the rate and amplitude of ICP changes and is especially suitable for quickly identifying situations of severe fluctuations or sharp increases in intracranial pressure.

[0073] To enhance the practicality and accuracy of the system, the integrated display module also provides data analysis functions, with a focus on monitoring abnormal waveforms of intracranial pressure. By analyzing the characteristics of ICP waveforms through intelligent algorithms, the system can automatically identify and label abnormal waveforms, and determine whether there are abnormal intracranial pressure changes based on the set thresholds. This function can effectively reduce the burden on doctors during their busy work and improve the response speed to changes in the patient's condition. When the ICP waveform shows abnormal fluctuations or exceeds the set threshold, the system will immediately issue an audible and visual alarm to prompt medical staff to handle it in a timely manner. The alarm mechanism ensures that corresponding measures can be taken promptly when the patient's condition changes.

[0074] Currently, the preliminary data collection has been completed clinically. The transmitted light intensity collected by the patch and the intracranial pressure data are as Figure 6 shown. The results show that the differential optical signal can well follow the changes in intracranial pressure, and the two show good correlation.

[0075] The differential near-infrared probe sensor and monitoring method for precise non-invasive intracranial pressure monitoring according to the present invention place two near-infrared sensors at symmetric positions on the head and use the differential principle to eliminate the errors caused by background noise and individual differences. This design not only improves the specificity and sensitivity of the signal but also enhances the anti-interference ability of the system; the system can achieve bedside, continuous, and real-time ICP monitoring, providing more comprehensive patient status information for clinicians, helping to adjust the treatment plan in a timely manner. The near-infrared patch technology reduces the equipment cost and operation complexity, making it easier to promote in medical institutions at all levels, and has significant economic and social benefits.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring, comprising a patch probe data acquisition module, a signal processing and transmission module and an integrated display module, characterized in that: The patch probe data acquisition module includes a flexible substrate, an LED light source and a photodetector arranged along a straight line on the flexible substrate; The photoelectric detector includes a proximal detector 30±0.5 mm away from the center of the LED light source and a distal detector 40±0.5 mm away from the center of the LED light source. The LED light source, the proximal detector and the distal detector are arranged in an axial differential detection layout.

2. A differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring according to claim 1, characterized in that: The LED light source is a miniaturized near-infrared light source array with a light emission wavelength of 800-820nm.

3. The differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring according to claim 1, characterized in that: The light-emitting timing control part of the LED light source adopts a time-division multiplexing control system and a light-emitting timing control circuit. The time-division multiplexing control system is equipped with a dynamic power modulation unit. The light-emitting timing control circuit adopts PWM dimming technology. The light-emitting timing control circuit divides the LED light source drive into a high-sensitivity mode and a conventional detection mode. When the LED light source is in high sensitivity mode, the LED light source emits light for 0.5 seconds within 1 second, with a pulse frequency of 100Hz and a duty cycle of 10%; When the LED light source is in the normal detection mode, the LED light source emits light for 0.5 seconds within 1 second, uses a 50 Hz pulse frequency, and a duty cycle of 5%.

4. The differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring according to claim 1, characterized in that: The receiving timing of the photoelectric detector adopts full-time receiving. The photoelectric detector is equipped with an intelligent trigger mechanism, which automatically switches to high-speed sampling mode when the signal fluctuation exceeds 20%.

5. The differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring according to claim 1, characterized in that: The signal processing and transmission module includes an integrated signal processing circuit, a Python digital signal processing module and a signal transmission module; The front end of the integrated signal processing circuit is composed of a signal filtering and amplifying preprocessing module and a high-precision A / D conversion unit. The signal filtering and amplifying preprocessing module includes a filtering unit, a hardware filter group, a digital filtering algorithm and a low-noise amplifying circuit. The filter unit adopts adaptive dynamic filtering, the hardware filter group is a high-order Butterworth filter and an industrial frequency filter, the digital filtering algorithm adopts the LMS algorithm, and the low-noise amplifier circuit adopts a three-stage cascade transimpedance amplifier; The high-precision A / D conversion unit uses oversampling technology and sets up a photoelectric isolation transmission channel.

6. The differential near-infrared probe sensor for accurate non-invasive intracranial pressure monitoring according to claim 1, characterized in that: The signal transmission module is electrically connected to the integrated display module, and the integrated display module receives and displays the ICP value processed by the signal processing transmission module. The integrated display module adopts a dual-mode display design, and the dual modes include a monitoring mode and a diagnosis mode; The monitoring mode displays the patient's real-time ICP waveform and the ICP rise and fall trend chart over a period of time; The diagnostic mode displays a ΔICP / time curve, showing the rate and magnitude of ICP changes.

7. A monitoring method for accurate non-invasive intracranial pressure monitoring using a differential near-infrared probe sensor according to any one of claims 1 to 6, characterized in that: The steps include: S1, fix the patch probe data acquisition module, turn on the LED light source, and the photoelectric detector collects the light signal reflected by the brain tissue and converts it into an electrical signal; Among them, the proximal detector collects light signals reflected by the scalp and skull, and the distal detector collects light signals reflected by the dura mater, arachnoid membrane, and subarachnoid space; S2, the signal processing transmission module receives the electrical signal from the photoelectric detector, and the electrical signal is subjected to front-end filtering and amplification processing by the signal filtering and amplification preprocessing module, and then converted into a digital signal by the high-precision A / D conversion unit; S3, Python digital signal processing module uses Python language to process the converted digital signal. According to Lambert-Beer law, the digital signal is converted into absorbance change. The calculation formula is as follows: Among them, A is absorbance, which is used to describe the degree of light absorption, I t represents the intensity of transmitted light, I0 represents the intensity of incident light, ε'(λ) is the molar absorption coefficient of the substance, c is the concentration of the absorbing substance, and l represents the optical path distance; The Python digital signal processing module uses 2s as a monitoring cycle, removes the singular values ​​of the signal within a single monitoring cycle, and then calculates the absorbance. It couples the absorbance value with the ICP signal of the synchronous invasive monitoring, and establishes a parameter model based on the correlation between the absorbance value and the ICP signal. The accuracy and stability of the parameter model are optimized through data accumulation, and a direct mathematical model between the absorbance and the ICP signal is established. S4. The signal transmission module transmits the processed signal to the integrated display module.

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