Dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomous adjusting and monitoring method and system

Abstract

The invention relates to the field of medical monitoring, in particular to a dual-wavelength near-infrared and PPG fused cerebral blood flow autonomous adjustment monitoring method and system, and is particularly suitable for real-time monitoring, evaluation and intervention control of a cerebral blood flow autonomous adjustment function in a neurosurgical operation. According to the system, firstly, intracranial blood oxygen saturation data are accurately obtained by adopting a dual-wavelength near infrared spectrum technology, meanwhile, blood pressure waveform characteristics are captured by combining a photoelectric volume pulse wave tracing method, and based on the key data, the system constructs a cerebrovascular automatic regulation function evaluation model; and finally, the system outputs a control signal to ensure that the cerebral perfusion pressure is stabilized in a target interval, the method is combined with a non-invasive monitoring technology, the risk of traditional invasive monitoring is effectively avoided, the real-time and accurate evaluation of the cerebral blood flow autonomous regulation function is realized, and the accuracy of the cerebral blood flow autonomous regulation function is improved. A safer and more efficient solution is provided for the field of medical monitoring.

Description

Technical Field

[0001] The present invention relates to the field of medical monitoring, and specifically to a method and system for monitoring cerebral blood flow autonomic regulation by integrating dual-wavelength near-infrared and PPG, which is particularly suitable for real-time monitoring, evaluation and intervention control of the cerebral blood flow autonomic regulation function during neurosurgery. Background Art

[0002] In neurosurgery, particularly during procedures like aneurysm clipping, maintaining adequate cerebral perfusion pressure is crucial for preventing intraoperative ischemia and postoperative neurological impairment. Cerebral autoregulation refers to the brain's ability to maintain stable cerebral blood flow despite changes in blood pressure by regulating vasodilation and constriction. When this autoregulatory capacity is impaired, brain tissue is at risk for severe ischemia or hyperperfusion, leading to irreversible neurological damage.

[0003] Currently, clinical monitoring methods for the autonomic regulation of cerebral blood flow are mainly divided into two categories: invasive and non-invasive. Invasive monitoring methods such as intracranial pressure monitoring and brain tissue oxygen partial pressure monitoring, although with high measurement accuracy, carry the risk of complications such as infection and bleeding. Non-invasive monitoring methods such as transcranial Doppler ultrasound and near-infrared spectroscopy, although with high safety, still need to improve their measurement accuracy and real-time performance. In addition, manual control is currently commonly used in clinical practice to adjust the infusion rate of pressor drugs, which has problems such as untimely and inaccurate adjustment, making it difficult to maintain cerebral perfusion pressure within the optimal range.

[0004] Therefore, developing a method and system that can monitor the autonomic regulation function of cerebral blood flow in real time and accurately, and automatically and precisely control cerebral perfusion pressure, is of great significance to improving the safety of neurosurgery and preventing postoperative neurological damage. Summary of the Invention

[0005] The purpose of the present invention is to provide a dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method and system to solve the problem of high incidence of cerebrovascular complications caused by inaccurate monitoring of cerebral blood flow autonomic regulation function and imprecise control of pressor drugs in the existing technology.

[0006] The present invention proposes a dual-wavelength near-infrared and PPG fusion cerebral blood flow autoregulation monitoring method, comprising:

[0007] The acquisition steps include:

[0008] Dual-wavelength near-infrared spectroscopy is used to obtain intracranial blood oxygen saturation data;

[0009] Photoplethysmography was used to obtain blood pressure waveform characteristics;

[0010] Processing steps include:

[0011] constructing a cerebral vascular autoregulation function evaluation model based on the intracranial blood oxygen saturation data and the blood pressure waveform characteristics;

[0012] According to the cerebrovascular autoregulation function evaluation model, a fuzzy logic control strategy is used to determine the pressor drug infusion rate;

[0013] Output steps include:

[0014] The pressor drug infusion rate control signal is output to stabilize the cerebral perfusion pressure in the target range.

[0015] Preferably, the dual-wavelength near-infrared spectroscopy technology includes:

[0016] The first channel is set to obtain forearm blood oxygen information, and the second channel is set to obtain near-infrared light information with wavelengths of 760nm and 805nm of arterial blood;

[0017] The blood oxygen saturation is inverted by calculating the difference in light absorption rates of two different wavelengths;

[0018] Brain oxygen saturation information is obtained based on the mapping relationship between blood oxygen saturation and brain oxygen uptake fraction.

[0019] Preferably, the photoplethysmography method comprises:

[0020] Extract blood pressure waveform features from photoplethysmography;

[0021] Constructing a cerebral vascular autoregulation function evaluation model based on the blood pressure waveform characteristics;

[0022] A correlation between the blood pressure waveform characteristic and the cerebral vasoconstriction state is determined.

[0023] Preferably, the construction of the cerebral vascular autoregulation function evaluation model includes:

[0024] Extract characteristic parameters of systolic blood pressure, diastolic blood pressure and heart rate variability;

[0025] Based on the relationship between cerebral oxygen uptake index and cerebral vascular resistance index, the relationship between arterial blood pressure fluctuations and cerebral hemodynamic parameters was determined;

[0026] Identify physiological and pathological changes in cerebral hemodynamic parameters caused by arterial blood pressure fluctuations.

[0027] Preferably, the fuzzy logic control strategy includes:

[0028] Blood pressure waveform characteristics, cerebral oxygen saturation, and cerebral vascular autoregulation coefficient were used as input variables;

[0029] Dynamically adjust fuzzy rules based on individual physiological characteristics;

[0030] The vasopressor infusion rate was calculated using a nine-step algorithmic process;

[0031] The pressor drug infusion rate is controlled to keep the autoregulatory index stable in the target range for a long time.

[0032] Preferably, the method further comprises:

[0033] Construct a correlation model between cerebral blood oxygenation and hemodynamics;

[0034] To analyze the association between abnormal blood oxygen saturation and the incidence of postoperative cerebrovascular accidents;

[0035] When the cerebral vascular resistance index falls below the preset threshold, a brain tissue hypoxia warning is issued.

[0036] Preferably, the method further comprises:

[0037] Real-time recording of preoperative arterial blood pressure, blood flow velocity, heart rate, cerebral oxygen saturation, and cerebral perfusion pressure parameters;

[0038] Real-time display of current cerebral hemodynamic characteristics;

[0039] Postoperative cerebrovascular complications are predicted based on the cerebral hemodynamic characteristics.

[0040] Preferably, the processing step further comprises:

[0041] Ultrasound Doppler is used to detect blood vessels such as the brain, cerebellum, basilar artery, vertebral artery, carotid artery, and ophthalmic artery;

[0042] By adjusting the infusion rate of pressor drugs, cerebral vasoconstriction and relaxation can be alternating;

[0043] Combined with dual-wavelength near-infrared spectroscopy to measure intracranial blood oxygen saturation, blood flow and brain oxygen saturation measurement and cerebral hemodynamic characteristics analysis can be achieved.

[0044] Preferably, determining the pressor drug infusion rate comprises:

[0045] Calculate the difference between arterial blood pressure waveform characteristics and pressor drug infusion volume, and construct a correlation model between pressor drugs and vasoconstriction state;

[0046] Inputting blood pressure waveform characteristics into a model associating pressor drugs with vasoconstriction state;

[0047] The pressor drug infusion volume is calculated based on the pressor drug waveform characteristics to keep the autoregulation index stable in the target range for a long time;

[0048] Maintain stable cerebral perfusion pressure.

[0049] Cerebral blood flow autonomic regulation monitoring system, including:

[0050] Dual-wavelength near-infrared spectroscopy module for obtaining intracranial blood oxygen saturation data;

[0051] A photoelectric acquisition module for acquiring blood pressure waveform characteristics using photoplethysmography;

[0052] Ultrasonic Doppler module, used to obtain blood flow velocity data of blood vessels;

[0053] a data acquisition module, for synchronously acquiring and processing the blood oxygen saturation data, blood pressure waveform characteristics, and blood flow velocity data;

[0054] a control module, configured to construct a cerebral vascular autoregulation function evaluation model based on the intracranial blood oxygen saturation data and the blood pressure waveform characteristics, and determine a pressor drug infusion rate using a fuzzy logic control strategy based on the cerebral vascular autoregulation function evaluation model;

[0055] an output module, configured to output a pressor drug infusion rate control signal to stabilize the cerebral perfusion pressure within a target range;

[0056] The display module is used to display the current cerebral hemodynamic characteristics in real time and predict the risk of postoperative cerebrovascular complications.

[0057] The present invention uses dual-wavelength near-infrared spectroscopy technology and photoplethysmography to obtain intracranial blood oxygen saturation data and blood pressure waveform characteristics, constructs a cerebral vascular autoregulation function evaluation model, and adopts a fuzzy logic control strategy to accurately regulate the pressor drug infusion rate to achieve stable control of cerebral perfusion pressure within the target range. At the same time, it predicts the risk of postoperative cerebrovascular complications and provides a scientific basis for clinical decision-making.

[0058] The present invention has the following beneficial effects: first, a non-invasive monitoring method combining dual-wavelength near-infrared spectroscopy technology and photoplethysmography is adopted to avoid the risks of traditional invasive monitoring; second, by constructing a cerebral vascular autoregulation function evaluation model, real-time and accurate evaluation of the cerebral blood flow autoregulation function is achieved; third, a fuzzy logic control strategy is adopted to achieve precise control of the pressor drug infusion rate, so that the cerebral perfusion pressure is stabilized in the target range; finally, by constructing a cerebral blood oxygenation and hemodynamic correlation model, early warning of postoperative cerebrovascular complications is achieved, effectively reducing the risk of postoperative neurological damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of the overall structure of the cerebral blood flow autonomic regulation monitoring system of the present invention;

[0060] Figure 2 This is a flow chart of the dual-wavelength near-infrared and PPG fusion cerebral blood flow autoregulation monitoring method of the present invention;

[0061] Figure 3 Schematic diagram of the optical path of the dual-wavelength near-infrared spectroscopy technology of the present invention;

[0062] Figure 4 Schematic diagram of waveform feature extraction of photoplethysmography of the present invention;

[0063] Figure 5 This is a flow chart for constructing a cerebral vascular autoregulation function evaluation model of the present invention;

[0064] Figure 6 Flowchart for the implementation of the fuzzy logic control strategy of the present invention;

[0065] Figure 7 This is a schematic diagram of the effect of the pressor drug infusion control of the present invention;

[0066] Figure 8 Schematic diagram of the structure of the cerebral blood oxygenation and hemodynamics correlation model of the present invention;

[0067] Figure 9 This is a workflow diagram of the postoperative cerebrovascular complications prediction system of the present invention;

[0068] Figure 10 Schematic diagram of the monitoring interface of the present invention in clinical application. DETAILED DESCRIPTION

[0069] Please refer to the attached Figure 1-10 ,The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0070] Example 1: Basic Implementation of Dual-Wavelength Near-Infrared and PPG Fusion Cerebral Blood Flow Autoregulation Monitoring Method

[0071] like Figure 2 As shown, the dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method provided by the present invention includes an acquisition step, a processing step and an output step.

[0072] In the acquisition step, the dual-wavelength near-infrared spectroscopy technique is first used to acquire intracranial blood oxygen saturation data. Specifically, Figure 3 As shown, the dual-wavelength near-infrared spectroscopy module 10 includes a light source unit 11 and a photodetection unit 12. Light source unit 11 emits near-infrared light at wavelengths of 760 nm and 805 nm, where 760 nm corresponds to the absorption peak of deoxyhemoglobin and 805 nm corresponds to the isosbestic point between oxyhemoglobin and deoxyhemoglobin. Photodetection unit 12 has two measurement channels: a first channel 13 acquires forearm blood oxygenation information as a reference, and a second channel 14 acquires near-infrared light information from intracranial arterial blood.

[0073] According to the propagation law of near-infrared light in tissue, the absorption rate of light of different wavelengths in tissue is related to the concentration of oxygenated hemoglobin and deoxygenated hemoglobin in the tissue. By measuring the absorption rate of two wavelengths of light, blood oxygen saturation can be calculated. The specific calculation formula is as follows:

[0074] ,

[0075] Among them, among them, Indicates blood oxygen saturation, It represents the ratio of the light absorption rates of two wavelengths. The calculation formula is:

[0076] ,

[0077] in, and Respectively represent the light intensity of 760nm and 805nm wavelength light after passing through the tissue, and Represent the incident light intensity respectively. and is the calibration coefficient, obtained through experimental calibration. Preferably, 110.7, , .

[0078] By calculating the difference in light absorption between two different wavelengths, blood oxygen saturation can be inverted, and brain oxygen saturation information can be further obtained based on the mapping relationship between blood oxygen saturation and brain oxygen uptake fraction. The calculation formula for brain oxygen uptake fraction (OEF) is:

[0079] ,

[0080] in, Indicates arterial oxygen saturation, Indicates venous oxygen saturation. The dual-wavelength near-infrared spectroscopy technology of the present invention can simultaneously obtain arterial and venous blood oxygen information, thereby calculating the brain oxygen uptake fraction and further obtaining brain oxygen saturation information.

[0081] In the acquisition step, photoplethysmography is also used to obtain blood pressure waveform characteristics. Figure 4 As shown, the photoplethysmography module 20 includes a photosensor 21 and a signal processing unit 22. The photosensor 21 emits light of a specific wavelength into tissue and detects changes in the intensity of the reflected or transmitted light. With each heartbeat, the blood volume within the arteries changes periodically, causing light absorption to vary, forming a pulse wave.

[0082] Blood pressure waveform features extracted from the photoplethysmography include, but are not limited to, peak value, trough value, rise time, fall time, waveform area, waveform width, waveform slope, and second-order derivative waveform feature points. These features reflect hemodynamic parameters such as vascular elasticity and peripheral resistance. Signal processing unit 22 uses a wavelet transform algorithm to extract these features from the raw pulse wave signal. The wavelet transform formula is as follows:

[0083] ,

[0084] in, represents the wavelet transform coefficients, represents the scale parameter, represents the translation parameter, Represents the original signal Represents the conjugate of the wavelet basis function. By selecting appropriate wavelet basis functions and scale parameters, various features of the pulse wave can be effectively extracted.

[0085] In the processing step, a cerebrovascular autoregulation function assessment model is constructed based on the acquired intracranial blood oxygen saturation data and blood pressure waveform characteristics. This model evaluates the cerebrovascular autoregulation function by analyzing the relationship between blood pressure changes and cerebral blood flow responses. Specifically, the construction of the cerebrovascular autoregulation function assessment model includes the following steps:

[0086] First, the characteristic parameters of arterial systolic pressure, arterial diastolic pressure and heart rate variability are extracted. Arterial systolic pressure and diastolic pressure can be indirectly calculated through photoplethysmography. The calculation formula is:

[0087] ,

[0088] ,

[0089] in, represents systolic blood pressure, represents diastolic blood pressure, represents the pulse wave velocity, and is the regression coefficient, obtained by comparing with the standard sphygmomanometer. Generally, 4.5±0.5 is used. Take -47±5, Pick , Take 15±3.

[0090] Heart rate variability was calculated by the standard deviation of consecutive RR intervals using the formula:

[0091] ,

[0092] in, represents the standard deviation of the RR interval, Indicates the RR intervals, Represents the average value of the RR interval Represents the total number of RR intervals.

[0093] Secondly, based on the relationship between cerebral oxygen uptake index and cerebral vascular resistance index, the relationship between arterial blood pressure fluctuations and cerebral hemodynamic parameters was determined. The calculation formula of cerebral vascular resistance index (CVRi) is:

[0094] ,

[0095] in, represents mean arterial pressure, Indicates cerebral blood flow. When the cerebral vascular autoregulation function is normal, the change in cerebral vascular resistance index caused by blood pressure changes should be negatively correlated with the change in cerebral oxygen uptake index, that is:

[0096] ,

[0097] in, Indicates the change of cerebral vascular resistance index, Indicates the change in brain oxygen uptake index, is the correlation coefficient. When it is greater than 0.8, it indicates that the cerebral vascular autoregulatory function is good; when When it is less than 0.5, it indicates that the cerebral vascular autonomic regulation function is impaired.

[0098] Finally, the assessment of cerebral vascular autoregulation is completed by identifying physiological and pathological changes caused by arterial blood pressure fluctuations in cerebral hemodynamic parameters. Specifically, when mean arterial pressure fluctuates within the range of 60-150 mmHg, if the change in cerebral blood flow does not exceed 10%, it is considered physiological, indicating normal cerebral vascular autoregulation. If the change in cerebral blood flow exceeds 20%, it is considered pathological, indicating impaired cerebral vascular autoregulation.

[0099] During the processing step, a fuzzy logic control strategy is used to determine the pressor infusion rate based on a cerebrovascular autoregulatory function evaluation model. This fuzzy logic control strategy accounts for the nonlinear characteristics of physiological systems and individual differences, making it more suitable for biomedical systems than traditional PID control.

[0100] The fuzzy logic control strategy includes the following steps: First, blood pressure waveform characteristics, cerebral oxygen saturation, and cerebral vascular autoregulation coefficient are used as input variables. A corresponding fuzzy set and membership function are defined for each input variable. For example, blood pressure waveform characteristics can be defined as three fuzzy sets: low, medium, and high, with either a triangular or trapezoidal membership function. Cerebral oxygen saturation can be defined as four fuzzy sets: dangerously low, low, normal, and high. The cerebral vascular autoregulation coefficient can be defined as three fuzzy sets: poor, medium, and good.

[0101] Secondly, fuzzy rules are dynamically adjusted based on individual physiological characteristics. Fuzzy rules use an IF-THEN format, for example, IF the blood pressure waveform is low AND cerebral oxygen saturation is low AND cerebral vascular autoregulation coefficient is poor THEN vasopressor infusion rate is high. Based on clinical expert experience and patient specific circumstances, a series of such rules are developed to form a complete rule base.

[0102] The vasopressor infusion rate is then calculated using a nine-step algorithm. The nine-step algorithm includes:

[0103] 1. Measure blood velocity in blood vessels, record the patient's preoperative arterial blood pressure, and record the blood pressure waveform characteristics in the photoplethysmography;

[0104] 2. Calculate the difference between arterial blood pressure waveform characteristics and pressor drug infusion volume, and construct a model for the association between pressor drugs and vasoconstriction state;

[0105] 3. Construct a mapping function between pressor drug input and blood pressure waveform characteristics;

[0106] 4. Input the blood pressure waveform characteristics into the association model between pressor drugs and vasoconstriction state, and the association model between pressor drugs and cerebral vascular resistance state;

[0107] 5. Calculate the pressor drug fluctuation when the pressor drug infusion volume is 0 based on the pressor drug waveform characteristics and the model of the association between pressor drugs and vasoconstriction state;

[0108] 6. Input the fluctuation of pressor drugs into the model of association between pressor drugs and cerebrovascular resistance status;

[0109] 7. Calculate the vasopressor fluctuation value when the vasopressor infusion volume is 0 based on the vasopressor infusion volume and the model of the association between vasopressor drugs and cerebral vascular resistance status;

[0110] 8. Input the pressor drug fluctuation value into the pressor drug and cerebral vascular resistance state association model to calculate the current pressor drug infusion volume;

[0111] 9. Calculate the target vasopressor infusion volume based on the vasopressor infusion volume and the model of the association between vasopressor drugs and cerebral blood flow autonomic regulation, adjust the vasopressor dose, and repeat steps 3-9;

[0112] The association model between pressor drugs and cerebral blood flow autonomic regulation state is a mathematical model that describes the relationship between drug dose and autoregulation index (ARI), which can be expressed as:

[0113] ,

[0114] in, represents drug dosage, CVRi represents cerebral vascular resistance index, Represents the cerebral oxygen uptake fraction. This model was established by analyzing historical data and is used to predict the autonomic regulation of cerebral blood flow under different drug doses.

[0115] This fuzzy logic control strategy controls the infusion rate of pressor drugs, keeping the autoregulatory index (ARI) stable within the target range for a long time. The target range is usually set between 0.6 and 0.8 because research shows that when the ARI is within this range, cerebrovascular autoregulation function is in optimal condition.

[0116] In the output step, the pressor drug infusion rate control signal is output to stabilize the cerebral perfusion pressure in the target range. The calculation formula of cerebral perfusion pressure (CPP) is:

[0117] ,

[0118] in, represents mean arterial pressure, Indicates intracranial pressure. For adults, normal cerebral perfusion pressure ranges from 60-70 mmHg. Maintaining cerebral perfusion pressure within this range is crucial in neurosurgery, especially aneurysm clipping. The present invention precisely controls the infusion rate of pressor drugs to stabilize cerebral perfusion pressure within the target range, avoiding the risk of cerebral ischemia caused by too low a pressure and cerebral edema caused by too high a pressure.

[0119] This invention avoids the risks of traditional invasive monitoring through an innovative non-invasive intracranial pressure estimation method. The specific method is:

[0120] Based on the blood flow velocity waveform characteristics measured by transcranial Doppler ultrasound; combined with the blood pressure waveform characteristics obtained by photoplethysmography; using the transfer function model to estimate intracranial pressure:

[0121] ,

[0122] in, and are the cerebral blood flow velocities during diastole and systole, respectively. and are systolic and diastolic blood pressure, respectively. is the pulsatility index, is the calibration coefficient. The estimation error of this method is within ±5 mmHg, which meets clinical needs.

[0123] Example 2: Detailed implementation of dual-wavelength near-infrared spectroscopy technology

[0124] Based on Example 1, the implementation method of dual-wavelength near-infrared spectroscopy technology is further described in detail. Figure 3 As shown, the dual-wavelength near-infrared spectroscopy technology includes setting a first channel 13 to obtain forearm blood oxygen information, and a second channel 14 to obtain near-infrared light information of arterial blood with wavelengths of 760nm and 805nm.

[0125] The first channel 13 includes a photoelectric sensor placed on the forearm, which measures the absorption of near-infrared light by the blood in the forearm artery to obtain forearm blood oxygen information. The second channel 14 includes a photoelectric sensor placed on the forehead or temporal region, which measures the absorption of near-infrared light by the blood in the intracranial artery to obtain intracranial blood oxygen information.

[0126] Dual-wavelength near-infrared spectroscopy technology inverts blood oxygen saturation by calculating the difference in light absorption rates at two different wavelengths. The specific calculation process is as follows: First, according to the Lambert-Beer law, the attenuation of light in tissue satisfies the following relationship:

[0127] ,

[0128] in, represents absorbance, represents the incident light intensity, represents the intensity of transmitted light, represents the molar absorption coefficient, Indicates concentration, Indicates optical path length.

[0129] For tissues containing oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb), at wavelength The absorbance at is:

[0130] ,

[0131] in, and Respectively represent the wavelengths of oxygenated hemoglobin and deoxygenated hemoglobin Molar absorption coefficient at and represent the concentrations of oxyhemoglobin and deoxyhemoglobin, respectively.

[0132] By measuring the absorbance at two wavelengths (760nm and 805nm), we can solve and Then, the blood oxygen saturation can be calculated by the following formula:

[0133] ,

[0134] Experimental verification demonstrates that the dual-wavelength near-infrared spectroscopy technology of this invention can achieve a blood oxygen saturation measurement accuracy of ±2%, meeting clinical requirements. Furthermore, the use of a differential measurement method effectively reduces the influence of tissues such as skin, fat, and bone on the measurement results, improving the accuracy of intracranial blood oxygen measurement.

[0135] Brain oxygen saturation information is obtained based on the mapping relationship between blood oxygen saturation and brain oxygen uptake fraction. The relationship between brain oxygen uptake fraction and blood oxygen saturation is as follows:

[0136] ,

[0137] in, represents the cerebral oxygen uptake fraction, Indicates arterial oxygen saturation, Indicates jugular venous oxygen saturation. By measuring forearm arterial oxygen saturation as The approximate value of intracranial venous oxygen saturation is measured as The cerebral oxygen uptake fraction can be calculated from the approximate value of .

[0138] Then, cerebral oxygen saturation can be estimated by the following formula:

[0139] ,

[0140] in, Indicates brain oxygen saturation. Under normal physiological conditions, About 0.3-0.4, corresponding to About 60% to 70%. When it is lower than 50%, it indicates that the brain tissue is in a state of hypoxia and requires timely intervention.

[0141] Example 3: Detailed Implementation of Photoplethysmography

[0142] Based on Example 1, the implementation method of the photoplethysmography method is further described in detail. Figure 4 As shown, the photoplethysmography method includes extracting blood pressure waveform features from the photoplethysmography wave, constructing a cerebral vascular autoregulation function evaluation model based on the blood pressure waveform features, and determining the correlation between the blood pressure waveform features and the cerebral vasoconstriction state.

[0143] Photoplethysmography (PPE) is acquired using either transmissive or reflective photoelectric sensors. Transmissive sensors are suitable for fingertips, earlobes, and other areas, while reflective sensors are suitable for foreheads, wrists, and other areas. The sensor emits red or infrared light (typically 940nm) and measures the intensity of the transmitted or reflected light, recording the periodic changes in vascular volume and generating a pulse wave.

[0144] Blood pressure waveform features extracted from photoplethysmography include:

[0145] 1. Systolic peak (SP): The maximum value of the pulse wave, corresponding to systolic blood pressure;

[0146] 2. Diastolic trough (DP): the minimum value of the pulse wave, corresponding to diastolic blood pressure;

[0147] 3. Peak time (PT): The time from trough to peak, reflecting vascular elasticity;

[0148] 4. Peak width (PW): The duration of the peak, reflecting the diastolic function of the heart;

[0149] 5. Tidal wave reflection index (RI): The amplitude ratio of the reflected wave to the main wave, reflecting the degree of arteriosclerosis;

[0150] 6. Augmentation Index (AI): The degree to which the reflected wave enhances the main wave, reflecting the peripheral resistance;

[0151] 7. Feature points of the second-order derivative of the data: Calculate the second-order derivative of the pulse wave data to extract more subtle features.

[0152] The extraction process for these features is as follows: First, the raw pulse wave signal is preprocessed, including removing baseline drift and high-frequency noise. Baseline drift is removed using a high-pass filter with a cutoff frequency set to 0.5 Hz. High-frequency noise is removed using a low-pass filter with a cutoff frequency set to 10 Hz.

[0153] Next, a peak detection algorithm is used to identify the systolic peaks and diastolic troughs of the pulse wave. This peak detection algorithm is based on the first-order derivative of the signal, marking peaks when the derivative changes from positive to negative and troughs when the derivative changes from negative to positive. These blood pressure waveform features are calculated for each cardiac cycle.

[0154] A model for evaluating cerebral vascular autoregulation was constructed based on the extracted blood pressure waveform features. This model is based on the following assumptions: blood pressure waveform features reflect vascular elasticity and peripheral resistance, parameters that are closely related to the state of cerebral vasoconstriction. Specifically, when blood vessels constrict, the peak duration shortens and the tidal wave reflection index increases; when blood vessels relax, the peak duration lengthens and the tidal wave reflection index decreases.

[0155] Experimental verification has shown a significant correlation between the tidal wave reflection index (RI) and the cerebral vascular resistance index (CVRi), with a correlation coefficient r reaching 0.78 (p<0.01). This means that by measuring the tidal wave reflection index of the photoplethysmography wave, the contraction state of the cerebral blood vessels can be indirectly assessed.

[0156] Determining the correlation between blood pressure waveform characteristics and cerebral vasoconstriction is key to developing a model to assess cerebrovascular autoregulation. By comparing blood pressure waveform characteristics in healthy individuals and patients with impaired cerebrovascular autoregulation, the tidal wave reflection index, augmentation index, and peak time were found to be the most discriminative features. Based on these features, a support vector machine (SVM) classification model was constructed with an accuracy of 87%.

[0157] Example 4: Construction of a model for evaluating cerebral vascular autoregulation function

[0158] Based on Example 1, the construction method of the cerebral vascular autoregulation function evaluation model is further described in detail. Figure 5 As shown in the figure, the construction of the cerebral vascular autoregulation function evaluation model includes extracting characteristic parameters of arterial systolic pressure, arterial diastolic pressure and heart rate variability, determining the relationship between arterial blood pressure fluctuations and cerebral hemodynamic parameters based on the relationship between cerebral oxygen uptake index and cerebral vascular resistance index, and identifying physiological and pathological changes caused by arterial blood pressure fluctuations in cerebral hemodynamic parameter indicators.

[0159] Extracting characteristic parameters of systolic blood pressure, diastolic blood pressure, and heart rate variability is the basis for evaluating cerebrovascular autoregulation function. Systolic and diastolic blood pressure can be indirectly estimated by photoplethysmography, and the calculation formula is as described in Example 1. Heart rate variability includes two types of indicators: time domain and frequency domain. The time domain indicators mainly include SDNN (standard deviation of adjacent normal RR intervals) and RMSSD (root mean square of the difference between adjacent normal RR intervals), and the frequency domain indicators mainly include LF power (low frequency power, 0.04-0.15Hz) and HF power (high frequency power, 0.15-0.4Hz). Heart rate variability reflects the regulation of the autonomic nervous system on the cardiovascular system and is closely related to cerebrovascular autoregulation function.

[0160] Based on the relationship between the cerebral oxygen uptake index and the cerebral vascular resistance index, the relationship between arterial blood pressure fluctuations and cerebral hemodynamic parameters was determined. The calculation method for the cerebral oxygen uptake index (OEF) is described in Example 2. The calculation method for the cerebral vascular resistance index (CVRi) is described in Example 1. Under normal physiological conditions, when arterial blood pressure rises, cerebral vasoconstricts, CVRi increases, and OEF decreases. When arterial blood pressure falls, cerebral vasodilates, CVRi decreases, and OEF increases. This negative feedback mechanism ensures relative stability of cerebral blood flow.

[0161] The cerebral vascular autoregulatory function can be quantitatively assessed by the autoregulatory index (ARI). The ARI is calculated as follows:

[0162] ,

[0163] in, represents the change in cerebral vascular resistance index, CVRi represents the baseline cerebral vascular resistance index, represents the change in mean arterial pressure, Represents baseline mean arterial pressure. ARI (Arithmetic Recognition Index) is a normal range of 0.5-1.0, with higher values ​​indicating improved cerebrovascular autoregulation. An ARI < 0.4 indicates significant impairment of cerebrovascular autoregulation. Clinical practice has shown that maintaining the ARI within a narrow range of 0.6-0.8 achieves optimal neuroprotection, and this range is therefore the target range for the present invention.

[0164] Identifying physiological and pathological changes in cerebral hemodynamic parameters caused by arterial blood pressure fluctuations is central to assessing cerebral autoregulation. Under normal physiological conditions, CBF remains relatively stable, fluctuating no more than 10% within the MAP range of 60-150 mmHg. This stability is maintained by cerebral autoregulation. When cerebral autoregulation is impaired, CBF exhibits a linear relationship with MAP, with changes in MAP directly leading to corresponding changes in CBF.

[0165] Comparing changes in CVRi and OEF before and after changes in MAP can determine whether the change is physiological or pathological. If an increase in MAP is accompanied by a corresponding increase in CVRi and a decrease in OEF, this indicates a physiological change. However, if an increase in MAP is accompanied by a minimal change in CVRi and a significant change in OEF, this indicates a pathological change, suggesting impaired cerebral autoregulation.

[0166] Example 5: Detailed implementation of fuzzy logic control strategy

[0167] Based on Example 1, the implementation method of the fuzzy logic control strategy is further described in detail. Figure 6 As shown in the figure, the fuzzy logic control strategy includes taking blood pressure waveform characteristics, cerebral oxygen saturation, and cerebral vascular autoregulation coefficient as input variables, dynamically adjusting fuzzy rules according to individual physiological characteristics, calculating the pressor drug infusion rate through a nine-step algorithm process, and controlling the pressor drug infusion rate to keep the autoregulation index stable in the target range for a long time.

[0168] The core of fuzzy logic control strategies is to convert precise numerical inputs into fuzzy linguistic variables, derive control decisions through fuzzy reasoning, and then convert fuzzy outputs into precise control variables. This approach is particularly well-suited for biomedical systems with nonlinear and time-varying characteristics.

[0169] Fuzzification of input variables is the first step in fuzzy control. The input variables of the present invention include blood pressure waveform characteristics, cerebral oxygen saturation, and cerebral vascular autoregulation coefficient. Blood pressure waveform characteristics can be defined as three fuzzy sets: low, medium, and high. The corresponding membership function is a triangular function, defined as follows:

[0170] ,

[0171] ,

[0172] ,

[0173] in, For example, for the tidal wave reflection index (R1), you can set .

[0174] Cerebral oxygen saturation can be defined as four fuzzy sets: dangerously low, low, normal, and high, and the corresponding membership function is a trapezoidal function. The cerebral vascular autoregulation coefficient can be defined as three fuzzy sets: poor, medium, and good, and the corresponding membership function is also a triangular function.

[0175] Dynamically adjusting fuzzy rules based on individual physiological characteristics is the key to achieving individualized control. Fuzzy rules use the IF-THEN format, for example:

[0176] Rule 1: IF the tidal wave reflection index is high AND the cerebral oxygen saturation is low AND the cerebrovascular autoregulation coefficient is poor THEN the vasopressor infusion rate is high

[0177] Rule 2: IF the tidal wave reflection index is low AND the cerebral oxygen saturation is normal AND the cerebrovascular autoregulation coefficient is good THEN the vasopressor infusion rate is low

[0178] Depending on the patient's specific situation, 15 to 20 such rules can be set to form a complete rule base. The rule base can be adjusted accordingly for different types of surgeries or patients of different age groups.

[0179] The core of the fuzzy control strategy is to calculate the pressor infusion rate through a nine-step algorithm. These nine steps are described in Example 1, with key steps including constructing models linking pressor drugs to vasoconstriction and cerebral vascular resistance, and calculating the final pressor infusion rate.

[0180] The model for the association between pressor drugs and vasoconstriction is based on the principles of drug pharmacodynamics and describes the relationship between drug concentration and vasoconstriction. It can generally be represented by a sigmoid function:

[0181] ,

[0182] in, Represents the effect, represents the maximum effect, represents the drug concentration, represents the drug concentration that produces 50% of the maximal effect. Represents the Hill coefficient.

[0183] Models linking vasopressor medications to cerebrovascular resistance describe how drug-induced vasoconstriction affects cerebrovascular resistance. This relationship is often nonlinear and can be fitted with polynomial or exponential functions.

[0184] The ultimate goal of fuzzy control is to control the infusion rate of pressor drugs to maintain the autoregulatory index within the target range for a long period of time. The target range is typically set between 0.6 and 0.8, which represents the optimal range for cerebral vascular autoregulation. By real-time monitoring of the autoregulatory index and adjusting the pressor infusion rate, the index can be maintained within the target range, ensuring the stability of cerebral blood flow.

[0185] Example 6: Construction of a Correlation Model between Cerebral Blood Oxygen and Hemodynamics

[0186] Based on Example 1, the method of the present invention also includes constructing a model for the correlation between cerebral blood oxygen and hemodynamics, analyzing the relationship between abnormal blood oxygen saturation and the incidence of postoperative cerebrovascular accidents, and issuing a brain tissue hypoxia warning when the cerebral vascular resistance index is lower than a preset threshold.

[0187] The purpose of constructing a model for the association of cerebral blood oxygenation and hemodynamics is to reveal the intrinsic relationship between cerebral blood oxygenation status and cerebral hemodynamic parameters. This model predicts the potential risk of cerebrovascular complications by analyzing the relationship between parameters such as cerebral oxygen saturation, cerebral blood flow, and cerebral vascular resistance.

[0188] The model linking cerebral blood oxygenation and hemodynamics is based on the following key equations:

[0189] ,

[0190] in, represents the brain oxygen metabolic rate, represents cerebral blood flow, represents the cerebral oxygen uptake fraction, This equation shows that brain tissue oxygen metabolism depends on three factors: cerebral blood flow, cerebral oxygen uptake, and arterial blood oxygen content.

[0191] When cerebral blood flow decreases, in order to maintain normal oxygen metabolism, will increase accordingly. However, The increase is limited, up to 0.7-0.8, and above this limit, the brain tissue will face the risk of hypoxia. Changes in blood pressure can timely detect potential risks of cerebral ischemia.

[0192] Analyzing the relationship between abnormal blood oxygen saturation and the incidence of postoperative cerebrovascular accidents is key to predicting postoperative complications. Studies have shown that intraoperative cerebral oxygen saturation that is persistently below 20% of the baseline value for more than 15 minutes, or below 50% of the absolute value for more than 5 minutes, is significantly associated with the risk of postoperative neurological dysfunction, with a relative risk ratio (RR) of 2.5-3.0.

[0193] Specifically, when the brain oxygen saturation is lower than the absolute value of 60%, a warning should be given; when it is lower than 55%, intervention measures should be taken, such as increasing the concentration of inhaled oxygen, raising blood pressure, lowering intracranial pressure, etc.; when it is lower than 50%, emergency measures should be taken immediately to avoid irreversible nerve damage.

[0194] When the cerebral vascular resistance index falls below a preset threshold, a warning of brain tissue hypoxia is issued. The normal value of the cerebral vascular resistance index (CVRi) is approximately 1.5-2.0 mmHg·min / ml. When the CVRi falls below 1.0 mmHg·min / ml, it indicates that the cerebral vessels are extremely dilated and that cerebral vascular autoregulation is approaching decompensation, at which point an alert should be issued. Preferably, the preset threshold is set at 1.2 mmHg·min / ml to provide a sufficient time window for clinical intervention.

[0195] Example 7: Postoperative cerebrovascular complications prediction function

[0196] Based on Example 1, the method of the present invention also includes real-time recording of preoperative arterial blood pressure, blood flow velocity, heart rate, cerebral oxygen saturation, cerebral perfusion pressure and other parameters, real-time display of current cerebral hemodynamic characteristics, and prediction of postoperative cerebrovascular complications based on cerebral hemodynamic characteristics.

[0197] Real-time recording of preoperative parameters such as arterial blood pressure, blood flow velocity, heart rate, cerebral oxygen saturation, and cerebral perfusion pressure provides basic data for predicting postoperative complications. These parameters are collected in real time by the monitoring system of the present invention, with a sampling frequency of no less than 100Hz, ensuring the temporal resolution of the data.

[0198] Real-time display of current cerebral hemodynamic characteristics allows medical staff to intuitively understand the patient's cerebral blood flow status. Display content includes but is not limited to: real-time blood pressure curve, brain oxygen saturation trend chart, cerebral perfusion pressure change chart, cerebral vascular autoregulation coefficient curve, etc. This information is displayed through a graphical interface, allowing medical staff to quickly identify abnormalities.

[0199] One of the innovations of this invention is the prediction of postoperative cerebrovascular complications based on cerebral hemodynamic characteristics. The prediction model is based on a machine learning algorithm that analyzes the changing patterns of intraoperative cerebral hemodynamic parameters to identify characteristic patterns associated with postoperative complications.

[0200] Specifically, the prediction model considers the following types of features:

[0201] 1. Blood pressure fluctuation characteristics: including standard deviation, coefficient of variation, and number of sudden fluctuations of intraoperative blood pressure;

[0202] 2. Brain oxygen saturation characteristics: including the average value, minimum value, and duration below the threshold of brain oxygen saturation;

[0203] 3. Cerebral perfusion pressure characteristics: including the average value, lowest value, and duration of cerebral perfusion pressure below 60 mmHg;

[0204] 4. Characteristics of cerebral vascular autonomic regulation: including the average value, fluctuation range, and duration of the autonomic regulation coefficient below 0.4.

[0205] The prediction model uses a random forest algorithm, which has advantages such as handling high-dimensional features, resisting overfitting, and being able to handle missing values. The model outputs the risk level of postoperative cerebrovascular complications, which is categorized as low, medium, and high. If the prediction is high risk, the system issues an alert, prompting medical staff to take preventive measures.

[0206] Example 8: Synergistic application of ultrasound Doppler technology and pressor drug regulation

[0207] Based on Example 1, the method of the present invention also includes using ultrasonic Doppler to detect blood vessels such as the brain, cerebellum, basilar artery, vertebral artery, carotid artery, and ophthalmic artery, adjusting the pressor drug infusion rate to cause alternating cerebral vasoconstriction and dilation, and combining dual-wavelength near-infrared spectroscopy to measure intracranial blood oxygen saturation to achieve blood flow and brain oxygen saturation measurement and cerebral hemodynamic characteristics analysis.

[0208] Ultrasound Doppler technology is a noninvasive method for assessing cerebral blood flow velocity. Transcranial Doppler ultrasound (TCD) can measure blood flow velocity in major cerebral arteries, including the middle, anterior, and posterior cerebral arteries. The ultrasound probe typically has a frequency of 2 MHz and is able to penetrate the skull and acquire Doppler signals from intracranial vessels.

[0209] The relationship between blood flow velocity and Doppler shift is as follows:

[0210] ,

[0211] in, Indicates blood flow velocity, Indicates the propagation speed of ultrasound in tissue (about 1540m / s), represents the Doppler shift, Indicates the frequency of ultrasonic emission. It represents the angle between the ultrasound beam and the direction of blood flow.

[0212] By adjusting the infusion rate of pressor drugs to alternating cerebral vasoconstriction and relaxation, cerebrovascular reactivity can be assessed. Cerebrovascular reactivity refers to the responsiveness of cerebral vessels to various stimuli (such as changes in blood pressure and carbon dioxide partial pressure), and is an important indicator for assessing cerebrovascular autoregulation.

[0213] The calculation formula of cerebrovascular reactivity index (CVRi) is:

[0214] ,

[0215] in, represents the change of cerebral blood flow velocity, CBFv represents the baseline cerebral blood flow velocity, Indicates the change in stimulus, while stimulus represents the baseline stimulus. Stimulus can be blood pressure, carbon dioxide partial pressure, etc.

[0216] Combined with dual-wavelength near-infrared spectroscopy to measure intracranial oxygen saturation, it enables measurement of blood flow and cerebral oxygen saturation, as well as analysis of cerebral hemodynamic characteristics, providing comprehensive information for cerebrovascular function assessment. By simultaneously monitoring blood flow velocity and oxygen saturation, the cerebral metabolic rate of oxygen (CMRO2) can be calculated, a key indicator for assessing brain function.

[0217] ,

[0218] in, represents the brain oxygen metabolic rate, represents cerebral blood flow, Indicates arterial oxygen saturation, Indicates jugular venous oxygen saturation.

[0219] This multimodal monitoring method can comprehensively assess cerebral vascular function and cerebral oxygen metabolism status, providing a reliable basis for clinical decision-making.

[0220] Example 9: Precise Control of Vasopressor Drug Infusion Rate

[0221] Based on Example 1, a method for determining the pressor infusion rate is further described in detail. The method includes calculating the difference between arterial blood pressure waveform characteristics and the pressor infusion rate, constructing a model for associating pressor drugs with vasoconstriction states, inputting the blood pressure waveform characteristics into the model for associating pressor drugs with vasoconstriction states, and calculating the pressor infusion rate based on the pressor waveform characteristics, thereby maintaining the autoregulation index within the target range for a long period of time and thereby stabilizing cerebral perfusion pressure.

[0222] Calculating the difference between the arterial blood pressure waveform characteristics and the pressor drug infusion volume is the first step in determining the drug dosage. The arterial blood pressure waveform characteristics are as described in Example 3, mainly including the tidal wave reflection index, enhancement index, peak time, etc. The calculation formula is:

[0223] ,

[0224] in, Indicates the current drug infusion volume, represents the target drug infusion volume, Determined based on blood pressure characteristics and individual patient characteristics.

[0225] Building a model that correlates pressor drugs with vasoconstriction is key to achieving precise control. This model describes the relationship between drug dose and vasoconstriction, and can be represented by a pharmacodynamic model:

[0226] ,

[0227] in, Indicates vasoconstriction effect, represents the maximum effect, represents the drug concentration, represents the drug concentration that produces 50% of the maximal effect. Represents the Hill coefficient. For commonly used pressor drugs such as norepinephrine, About 2 5ng / ml, About 1.5-2.5.

[0228] By inputting blood pressure waveform features into a model linking pressor drugs to vasoconstriction states, the vasoconstrictive effects of different drug doses can be predicted. This process uses a fuzzy inference system to map blood pressure waveform features to drug dosage adjustments through a series of if-then rules.

[0229] The pressor infusion rate is calculated based on the pressor drug waveform characteristics to maintain the autoregulatory index within the target range for a long period of time. The target autoregulatory index range is 0.6-0.8, which is the range where cerebrovascular autoregulation is at its optimal state. The drug infusion rate is calculated using the proportional-integral-derivative (PID) control algorithm, as shown in the following formula:

[0230] ,

[0231] in, Indicates time The amount of drug infusion, Indicates time The error (the difference between the target value and the actual value), 、 、 Represents the proportional, integral, and differential coefficients respectively. These coefficients are determined through clinical experiments, usually , .

[0232] The present invention adopts fuzzy PID control strategy, combining fuzzy logic and PID control to give full play to their respective advantages:

[0233] A nine-step fuzzy logic algorithm was used to handle nonlinear physiological processes and individual differences, determining the initial drug infusion rate and adjustment direction;

[0234] PID control algorithm is used for fine adjustment to achieve smooth changes and stable control of infusion rate;

[0235] In the specific implementation, the fuzzy logic controller determines the PID parameters Dynamic adjustment strategy

[0236] ,

[0237] ,

[0238] ,

[0239] in, is the error (the difference between the target ARI and the actual ARI), This fuzzy PID control structure is superior to a single control method and can better adapt to individual differences and changes in physiological status of patients.

[0240] By precisely controlling the infusion rate of pressor drugs, cerebral perfusion pressure (CPP) is maintained stable. The target CPP is typically 60-70 mmHg, the optimal range for ensuring adequate cerebral blood flow while avoiding cerebral edema. Experiments have shown that using the method of this invention, CPP fluctuations can be controlled within ±5 mmHg, significantly outperforming traditional manual control methods (±10-15 mmHg).

[0241] Example 10: Hardware Implementation of a Cerebral Blood Flow Autonomic Regulation Monitoring System

[0242] like Figure 1 As shown, the cerebral blood flow autonomic regulation monitoring system of the present invention includes a dual-wavelength near-infrared spectroscopy module 10, a photoelectric acquisition module 20, an ultrasound Doppler module 30, a data acquisition module 40, a control module 50, an output module 60, and a display module 70.

[0243] The dual-wavelength near-infrared spectroscopy module 10 is used to acquire intracranial blood oxygen saturation data. The module includes a light source unit 11, a photodetection unit 12, a first channel 13, and a second channel 14. The light source unit 11 emits near-infrared light at wavelengths of 760 nm and 805 nm, while the photodetection unit 12 detects changes in the intensity of the transmitted or reflected light. The first channel 13 acquires forearm blood oxygen saturation data, while the second channel 14 acquires intracranial blood oxygen saturation data.

[0244] The photoelectric acquisition module 20 uses photoplethysmography to acquire blood pressure waveform characteristics. This module includes a photoelectric sensor 21 and a signal processing unit 22. The photoelectric sensor 21 emits light of a specific wavelength into tissue and detects changes in the intensity of the reflected or transmitted light. The signal processing unit 22 performs processing on the raw signal, including filtering and feature extraction.

[0245] The ultrasonic Doppler module 30 is used to obtain blood flow velocity data. This module includes an ultrasonic probe 31 and a Doppler signal processing unit 32. The ultrasonic probe 31 transmits ultrasonic waves with a frequency of 2 MHz, and the Doppler signal processing unit 32 processes the received Doppler signals to calculate blood flow velocity.

[0246] The data acquisition module 40 is used to synchronously collect and process blood oxygen saturation data, blood pressure waveform characteristics, and blood flow velocity data. This module includes a multi-channel acquisition unit 41 and a data preprocessing unit 42. The multi-channel acquisition unit 41 simultaneously collects multiple physiological signals at a sampling frequency of no less than 100 Hz. The data preprocessing unit 42 performs preprocessing on the collected data, such as filtering and denoising.

[0247] The control module 50 is used to construct a cerebrovascular autoregulatory function assessment model and, based on this model, uses a fuzzy logic control strategy to determine the pressor infusion rate. This module comprises a model construction unit 51, a fuzzy logic control unit 52, and a decision support unit 53. The model construction unit 51 constructs the cerebrovascular autoregulatory function assessment model based on collected data. The fuzzy logic control unit 52 implements the fuzzy control strategy and calculates the pressor infusion rate. The decision support unit 53 provides recommendations for clinical decision-making.

[0248] Output module 60 is used to output a pressor drug infusion rate control signal to stabilize cerebral perfusion pressure within the target range. This module includes a signal conversion unit 61 and an output interface unit 62. Signal conversion unit 61 converts the digital control signal into a drive signal, and output interface unit 62 connects to the drug pump to control the drug infusion rate.

[0249] Display module 70 is used to display current cerebral hemodynamic characteristics in real time and predict the risk of postoperative cerebrovascular complications. This module includes a graphic display unit 71 and a risk warning unit 72. Graphic display unit 71 displays real-time waveforms and trend graphs of various physiological parameters, while risk warning unit 72 issues alerts when abnormal conditions are detected.

[0250] The cerebral blood flow autoregulation monitoring system of the present invention realizes intelligent management of the entire process from data acquisition to decision support through modular design, providing a powerful tool for brain protection during neurosurgery.

[0251] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method, characterized in that: include: The acquisition steps include: Dual-wavelength near-infrared spectroscopy is used to obtain intracranial blood oxygen saturation data; Photoplethysmography was used to obtain blood pressure waveform characteristics; Processing steps include: constructing a cerebral vascular autoregulation function evaluation model based on the intracranial blood oxygen saturation data and the blood pressure waveform characteristics; According to the cerebrovascular autoregulation function evaluation model, a fuzzy logic control strategy is used to determine the pressor drug infusion rate; Output steps include: The pressor drug infusion rate control signal is output to stabilize the cerebral perfusion pressure in the target range.

2. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1 is characterized in that: The dual-wavelength near-infrared spectroscopy technology includes: The first channel is set to obtain forearm blood oxygen information, and the second channel is set to obtain near-infrared light information with wavelengths of 760nm and 805nm of arterial blood; The blood oxygen saturation is inverted by calculating the difference in light absorption rates of two different wavelengths; Brain oxygen saturation information is obtained based on the mapping relationship between blood oxygen saturation and brain oxygen uptake fraction.

3. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1 is characterized in that: The photoplethysmography method comprises: Extract blood pressure waveform features from photoplethysmography; Constructing a cerebral vascular autoregulation function evaluation model based on the blood pressure waveform characteristics; A correlation between the blood pressure waveform characteristic and the cerebral vasoconstriction state is determined.

4. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1, characterized in that: The construction of the cerebral vascular autoregulation function evaluation model includes: Extract characteristic parameters of systolic blood pressure, diastolic blood pressure and heart rate variability; Based on the relationship between cerebral oxygen uptake index and cerebral vascular resistance index, the relationship between arterial blood pressure fluctuations and cerebral hemodynamic parameters was determined; Identify physiological and pathological changes in cerebral hemodynamic parameters caused by arterial blood pressure fluctuations.

5. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1, characterized in that: The fuzzy logic control strategy includes: Blood pressure waveform characteristics, cerebral oxygen saturation, and cerebral vascular autoregulation coefficient were used as input variables; Dynamically adjust fuzzy rules based on individual physiological characteristics; The vasopressor infusion rate was calculated using a nine-step algorithmic process; The pressor drug infusion rate is controlled to keep the autoregulatory index stable in the target range for a long time.

6. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1, characterized in that: The method further comprises: Construct a correlation model between cerebral blood oxygenation and hemodynamics; To analyze the association between abnormal blood oxygen saturation and the incidence of postoperative cerebrovascular accidents; When the cerebral vascular resistance index falls below the preset threshold, a brain tissue hypoxia warning is issued.

7. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1, characterized in that: The method further comprises: Real-time recording of preoperative arterial blood pressure, blood flow velocity, heart rate, cerebral oxygen saturation, and cerebral perfusion pressure parameters; Real-time display of current cerebral hemodynamic characteristics; Postoperative cerebrovascular complications are predicted based on the cerebral hemodynamic characteristics.

8. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1, characterized in that: The processing steps further include: Doppler ultrasound was used to detect blood vessels such as the brain, cerebellum, basilar artery, vertebral artery, carotid artery, and ophthalmic artery; By adjusting the infusion rate of pressor drugs, cerebral vasoconstriction and relaxation can be alternating; Combined with dual-wavelength near-infrared spectroscopy to measure intracranial blood oxygen saturation, blood flow and brain oxygen saturation measurement and cerebral hemodynamic characteristics analysis can be achieved.

9. The dual-wavelength near-infrared and PPG fusion cerebral blood flow autonomic regulation monitoring method according to claim 1, characterized in that: Determining the pressor drug infusion rate includes: Calculate the difference between arterial blood pressure waveform characteristics and pressor drug infusion volume, and construct a correlation model between pressor drugs and vasoconstriction state; Inputting blood pressure waveform characteristics into a model associating pressor drugs with vasoconstriction state; The pressor drug infusion volume is calculated based on the pressor drug waveform characteristics to keep the autoregulation index stable in the target range for a long time; Maintain stable cerebral perfusion pressure.

10. A cerebral blood flow autonomic regulation monitoring system, used to execute the method according to any one of claims 1 to 9, characterized in that: include: Dual-wavelength near-infrared spectroscopy module for obtaining intracranial blood oxygen saturation data; A photoelectric acquisition module for acquiring blood pressure waveform characteristics using photoplethysmography; Ultrasonic Doppler module, used to obtain blood flow velocity data of blood vessels; a data acquisition module, for synchronously acquiring and processing the blood oxygen saturation data, blood pressure waveform characteristics, and blood flow velocity data; a control module, configured to construct a cerebral vascular autoregulation function evaluation model based on the intracranial blood oxygen saturation data and the blood pressure waveform characteristics, and determine a pressor drug infusion rate using a fuzzy logic control strategy based on the cerebral vascular autoregulation function evaluation model; an output module, configured to output a pressor drug infusion rate control signal to stabilize the cerebral perfusion pressure within a target range; The display module is used to display the current cerebral hemodynamic characteristics in real time and predict the risk of postoperative cerebrovascular complications.

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