A smart diagnostic and treatment method, system, and storage medium based on personalized blood pressure management.

Abstract

This invention provides an intelligent diagnostic and treatment method, system, device, computer-readable storage medium, and its applications based on personalized blood pressure management, relating to the field of intelligent healthcare. The method includes: acquiring a subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure data; analyzing the subject's EEG signals and cerebral blood flow velocity data to obtain a neurovascular coupling index; obtaining an individualized blood pressure range based on the subject's neurovascular coupling index and arterial blood pressure data; determining whether the subject's blood pressure is within the individualized blood pressure range; and outputting a prompt for blood pressure control when the subject's blood pressure is outside the individualized blood pressure range. This invention guides blood pressure management by determining the individualized blood pressure range for patients with cardiovascular and cerebrovascular diseases, providing valuable resources for researchers and clinicians in the field of cardiovascular and cerebrovascular disease diagnosis and treatment, and possessing significant scientific research and clinical value.

Description

Technical Field

[0001] This invention belongs to the field of intelligent healthcare, and more specifically, relates to an intelligent diagnosis and treatment method, system, device, computer-readable storage medium, and its application based on personalized blood pressure management. Background Technology

[0002] my country bears a heavy burden of cardiovascular and cerebrovascular diseases, with hypertension being the most common risk factor among these patients. However, the management of perioperative blood pressure in cardiovascular and cerebrovascular patients, especially after revascularization following acute ischemic stroke, remains unclear. Several randomized controlled trials have attempted to determine whether lower blood pressure targets can further improve the prognosis of stroke patients, but none have yielded positive results. This is because stroke risk factors vary significantly among patients; and during the acute phase of stroke, neurometabolism, cerebrovascular reactivity, and blood-brain barrier permeability are constantly changing. Therefore, a "one-size-fits-all" approach to blood pressure targets may not be appropriate. Individualized and precise blood pressure management is an urgent need for the management of acute stroke.

[0003] Neurovascular coupling (NVC) refers to the ability of increased neuronal electrical activity to increase cerebral blood flow. Impaired NVC is involved in the development of various neurological diseases, including cerebral infarction. The inventors' team found that NVC function is worse on the side of stenosis in intracranial large vessels than on the healthy side. Relatively intact NVC function reflects minimal damage to the neurovascular unit structure. Therefore, the longer blood pressure remains within the range of relatively intact NVC function, the better it is for improving the patient's neurological function. Determining the individualized blood pressure range most conducive to neurological function recovery through real-time monitoring and calculation of various physiological parameters is a pressing issue that needs to be addressed. Summary of the Invention

[0004] This application targets patients with cardiovascular and cerebrovascular diseases, especially those in the acute phase after cerebral infarction recanalization. Through multi-parameter physiological function monitoring, using phase-amplitude cross-frequency coupling and Granger causality analysis, it quantitatively evaluates resting-state NVC function, thereby determining individualized blood pressure ranges. Furthermore, it verifies the advantages and mechanisms of individualized blood pressure management strategies compared to fixed target blood pressure management at the clinical, neuroimaging, and biomarker levels, laying the foundation for individualized treatment of patients with cardiovascular and cerebrovascular diseases, especially those in the acute phase after cerebral infarction recanalization.

[0005] The first aspect of this application discloses an intelligent diagnosis and treatment method based on personalized blood pressure management, the method comprising:

[0006] S1: Acquire data on the subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure;

[0007] S2: The neurovascular coupling index was obtained by using phase-amplitude cross-frequency coupling and / or Granger causality analysis based on the subjects' electroencephalogram signals and cerebral blood flow velocity data;

[0008] S3: Individualized blood pressure range is obtained based on the subject's neurovascular coupling index and arterial blood pressure data;

[0009] S4: Determine whether the subject's blood pressure is within the individualized blood pressure range. If the subject's blood pressure is not within the individualized blood pressure range, output a prompt to control blood pressure.

[0010] Furthermore, the phase-amplitude cross-frequency coupling is used to describe the relationship between the phase of the low-frequency component in the cerebral blood flow velocity waveform and the amplitude modulation of the high-frequency component of the EEG signal.

[0011] Preferably, the amplitude modulation relationship is quantified by modulation coefficients, and the calculation steps of the modulation coefficients include:

[0012] S21: Select the low-frequency components of the cerebral blood flow velocity waveform and the high-frequency components of the EEG signal to obtain the filtered cerebral blood flow velocity and the filtered EEG signal.

[0013] Preferably, the low-frequency component of the cerebral blood flow velocity waveform is 0-0.10Hz, and the high-frequency component of the electroencephalogram (EEG) signal is 13-30Hz;

[0014] S22: Based on the filtered cerebral blood flow velocity and the filtered EEG signal, perform Hilbert transform to obtain the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal;

[0015] S23: Construct the first complex-valued signal based on the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal;

[0016] S24: The first composite complex-valued signal is given a random time delay to obtain a second composite complex-valued signal whose amplitude sequence undergoes random displacement;

[0017] S25: The uniformization difference between the first composite complex-valued signal and the second composite complex-valued signal is the modulation coefficient.

[0018] Furthermore, the Granger causality analysis yields the Granger causality index, which reflects changes in cerebral blood flow velocity caused by electroencephalogram (EEG) signals; the calculation steps for the Granger causality index include:

[0019] S26: Establish the first autoregressive model to describe the intrinsic relationship of cerebral blood flow velocity;

[0020] S27: Establish a second autoregressive model to describe changes in cerebral blood flow velocity caused by changes in EEG signals;

[0021] S28: The F-test is used to compare the goodness of fit of the first autoregressive model and the second autoregressive model. The calculated F-value is the Granger causality index.

[0022] Furthermore, the first autoregressive model is:

[0023]

[0024] Where CBFV is cerebral blood flow velocity, m is the model order, and a i ε is the first parameter. t The residuals of the first autoregressive model;

[0025] Optionally, the second autoregressive model is:

[0026]

[0027] Wherein, CBFV is cerebral blood flow velocity, EEG is electroencephalogram (EEG) signal, m is the model order, ai is the first parameter, bi is the second parameter, and ε' is the second parameter. t The residuals of the second autoregressive model;

[0028] Optionally, the formula for calculating the F value is:

[0029]

[0030] Where RSSx is the sum of squared residuals of the first autoregressive model, RSS'x is the sum of squared residuals of the second autoregressive model, m is the model order, and T is the number of observations in the second autoregressive model.

[0031] Furthermore, the steps for obtaining an individualized blood pressure range based on the subject's neurovascular coupling index and arterial blood pressure data include:

[0032] S31: Divide the time period into 1 window, calculate the average value of the modulation coefficient of each channel of the EEG signal and / or the average value of the Granger causality index for each window, wherein the average value of the modulation coefficient of each channel of the EEG signal is the first neural coupling index, and the average value of the Granger causality index is the second neural coupling index, and calculate the average blood pressure corresponding to each window;

[0033] Preferably, the fixed time period is 300 seconds;

[0034] S32: Move the window by one at fixed time intervals and recalculate the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure;

[0035] Preferably, the fixed time interval is 10 seconds;

[0036] S33: Plot the first neural coupling index, the second neural coupling index and the corresponding average blood pressure obtained during the entire monitoring process, with the average blood pressure as the x-axis and the first neural coupling index and / or the second neural coupling index as the y-axis. Fit a quadratic function curve to obtain the neural coupling index-average blood pressure curve.

[0037] S34: The average of the ordinate of the peak of the neural coupling index-mean blood pressure curve of all subjects included in the cohort study was used as the first tangent point for diagnosing neurovascular coupling damage.

[0038] Preferably, the average value of the ordinate of the peak of the neurovascular coupling index-mean blood pressure curve of subjects who developed large-area cerebral infarction and / or symptomatic hemorrhage after vascular recanalization treatment is selected as the second tangent point for diagnosing neurovascular coupling damage.

[0039] S35: Determine the two intersection points of the neural coupling index-mean blood pressure curve of S33 with the first or second tangent point of S34 on the ordinate. The mean blood pressure range corresponding to these two intersection points is the individualized blood pressure range of the subject.

[0040] Furthermore, the method also includes:

[0041] S36: Calculate the duration during which the subject's blood pressure was above and / or below the individualized blood pressure range within a fixed time after vascular recanalization treatment, statistically analyze the correlation between the duration and the classical neurovascular coupling function assessment, and determine the neuroprotective effect of the individualized blood pressure range.

[0042] Optionally, the method further includes S37: calculating the correlation between the duration and the subject's oxidative stress level and blood-brain barrier permeability to determine the neuroprotective effect of individualized blood pressure management.

[0043] Furthermore, the method also includes:

[0044] S38: Set a fixed target blood pressure range, calculate the duration of blood pressure above and / or below the individualized blood pressure range for a single patient within a fixed time after vascular recanalization treatment, compare the correlation differences between the two types of durations and neurological function and imaging outcomes, and determine the advantages of individualized blood pressure management relative to fixed target blood pressure.

[0045] Preferably, the fixed target blood pressure range is 120-140 mmHg;

[0046] Preferably, the neurological function and imaging outcomes include any one or more of the following: functional independence, ischemic stroke tissue volume progression, and symptomatic hemorrhage transformation.

[0047] The second aspect of this application discloses a computer device, the device comprising: a memory and a processor; the memory being used to store program instructions; the processor being used to invoke the program instructions, and when the program instructions are executed, to perform the method steps disclosed in the first aspect above.

[0048] The third aspect of this application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps disclosed in the first aspect.

[0049] The fourth aspect of this application discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method disclosed in the first aspect.

[0050] This application has the following beneficial effects:

[0051] 1. The method disclosed in this application avoids the shortcomings of the "one-size-fits-all" approach to managing patients with fixed blood pressure target values. By integrating three physiological monitoring data—electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure—it quantitatively evaluates resting-state NVC function and determines the individualized optimal blood pressure range to guide blood pressure management.

[0052] 2. The method disclosed in this application uses phase-amplitude cross-frequency coupling and / or Granger causality analysis to assess the relationship between EEG signals and cerebral blood flow velocity, and quantifies the degree of neurovascular coupling by modulation coefficient and Granger causality index, thus achieving a theoretical innovation in defining the neurovascular coupling index. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram of the intelligent diagnosis and treatment method based on individualized blood pressure management provided in the first aspect of the present invention;

[0055] Figure 2 This is a schematic diagram of an intelligent diagnosis and treatment system based on personalized blood pressure management provided in an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of the architecture of an exemplary computing device provided in an embodiment of the present invention;

[0058] Figure 5 This is a schematic diagram of the storage medium provided in an embodiment of the present invention;

[0059] Figure 6 This is a schematic diagram of individualized blood pressure based on resting-state neurovascular coupling monitoring provided by an embodiment of the present invention. Figure 6 In section (a), the index reflecting neurovascular coupling function is denoted as NVCx. An NVCx-BP curve is plotted. The horizontal axis representing the peak of the curve is the blood pressure at which the coupling function is optimal. Figure 6 In (b), it is assumed that NVCx is always impaired in patients with large-area cerebral infarction and symptomatic hemorrhage regardless of blood pressure changes. The ordinate of the peak of the NVCx-BP curve (red) for these two types of patients is used as the diagnostic cut-off point (NVCx') indicating impaired NVC function. For other cerebral infarction patients, the optimal blood pressure range (green) is between y=NVCx' and the intersection of the curve. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0061] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Figure 1 This invention provides an intelligent diagnosis and treatment method based on individualized blood pressure management, the method comprising:

[0064] S1: Acquire data on the subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure;

[0065] In one embodiment, the inclusion criteria for subjects include:

[0066] 1) Age 18-80 years, gender not limited; 2) Acute ischemic stroke confirmed by CT or MRI; 3) Received intravenous thrombolysis and / or endovascular treatment, with blood flow in the responsible artery reaching mTICI grade 2b-3; 4) Able to initiate multimodal monitoring within 4 hours after vascular recanalization; 5) mRS score ≤2 before this onset; 6) The responsible vessel in this case is the M1 segment of the middle cerebral artery or the internal carotid artery on one side.

[0067] In one embodiment, the exclusion criteria for subjects include:

[0068] 1) Hemorrhagic stroke confirmed by CT or MRI before the start of multimodal monitoring; 2) Cerebral vascular malformation, intracranial tumor, brain abscess, or other major nonvascular intracranial diseases (e.g., multiple sclerosis) diagnosed by clear imaging evidence; 3) Stroke caused by arteritis, migraine, vasospasm, drug abuse, or neurological genetic diseases (e.g., mitochondrial encephalomyopathy, CADASIL); 4) Poor sound transmission at the temporal window, making TCD cerebral blood flow monitoring impossible; 5) Impaired consciousness, delirium, mental abnormalities, or cognitive impairment, preventing cooperation in monitoring and follow-up; 6) Upper limb arterial stenosis, occlusion, dissection, or arteriovenous fistula, making radial artery pressure measurement unreliable; 7) Severe heart failure (NYHA class III-IV), or left ventricular ejection fraction <35%, or in the past 3 8) Patients diagnosed with acute coronary syndrome within the past month; 9) Patients with a history or current aortic dissection; 10) Patients with frequent premature ventricular contractions or other malignant arrhythmias; 11) Pregnant or planning to become pregnant; 12) Informed consent not obtained from the study participants.

[0069] In one embodiment, baseline characteristics of the subjects are collected, including: age, sex, stroke risk factors (hypertension, diabetes, coronary artery disease, hyperlipidemia, atrial fibrillation, smoking history, alcohol consumption history), etiological classification of the current cerebral infarction, NIHSS score, responsible vessel, mTICI grade after vessel recanalization, time from onset to recanalization, time from recanalization to multimodal monitoring initiation, blood pressure, heart rate, blood glucose, serum total protein, albumin, total cholesterol, low-density lipoprotein, high-density lipoprotein, homocysteine, glycated hemoglobin at admission, and arterial blood pH, CO2 partial pressure, O2 partial pressure, and HCO3- during multimodal monitoring. - Concentration, serum estradiol, and progesterone levels.

[0070] In one embodiment, multimodal monitoring is used to acquire the subject's electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure data. The multimodal monitoring includes:

[0071] ① Monitoring preparation

[0072] All tests were conducted in a quiet, temperature-controlled (20-24℃) ward with curtains to block out light. Subjects were placed in a supine position and monitored while awake and with their eyes closed.

[0073] ② CBFV

[0074] Cerebral blood flow was monitored using a DWL dual-channel transcranial Doppler ultrasound system (Compumedics DWL, Germany). Two 2MHz probes were placed in the bilateral temporal windows, the head was fixed in place, and the bilateral middle cerebral arteries were probed. The sampling depth was 40-60 mm, and the sampling volume was 8-10 mm.

[0075] ③ EEG

[0076] Before electrode placement, the subject should wash their hair or clean their scalp locally with alcohol or scrub to ensure the impedance between the electrode and scalp is between 0.1 and 5.0 kΩ. Silver / silver chloride disc electrodes are used, and 19-lead electrodes are placed according to the international 10-20 system. EEG and EKG are simultaneously acquired using a Nicolet EEG monitoring system (Natus, USA).

[0077] ④ ABP

[0078] After enrollment, patients' brachial artery cuff pressure was measured bilaterally, three times on each side, and the average value was taken. If the difference in systolic blood pressure between the two sides was greater than 20 mmHg, the side with the higher pressure was selected to monitor stroke-weighted arterial pressure (ABP). Using the Arrow arterial catheterization kit (Teleflex, USA), the radial artery pulsation was punctured near the wrist crease where it was most prominent. The guidewire was exchanged, and the arterial pressure measuring catheter was inserted. High-pressure heparin-lactated Ringer's solution and a disposable pressure sensor were connected, and the device was connected to a Mindray BeneVision N17 monitor (Shenzhen Mindray). After zeroing, the arterial pressure measuring catheter tee was opened, and the arterial pressure waveform was displayed.

[0079] ⑤ Signal acquisition and storage

[0080] Simultaneous acquisition of CBFV and EEG signals was performed, and the multi-mode monitoring signals were input into a Nicolet EEG V44 amplifier (Natus, USA) at a sampling rate of 512Hz. After signal stabilization, CBFV+EEG+ABP monitoring was conducted for at least 4 hours; ABP monitoring continued for 24 hours after vessel recanalization. Multi-mode monitoring signals were stored in edf format using NicoletOne Monitor 5.94 software (Natus, USA), stored on a dedicated external hard drive, and backed up regularly.

[0081] S2: The neurovascular coupling index was obtained by using phase-amplitude cross-frequency coupling and / or Granger causality analysis based on the subjects' electroencephalogram signals and cerebral blood flow velocity data;

[0082] The phase-amplitude cross-frequency coupling is used to describe the relationship between the phase of the low-frequency component in the cerebral blood flow velocity waveform and the amplitude modulation of the high-frequency component of the EEG signal.

[0083] The amplitude modulation relationship is quantified by modulation coefficients, and the calculation steps of the modulation coefficients include:

[0084] S21: Select the low-frequency components of the cerebral blood flow velocity waveform and the high-frequency components of the EEG signal to obtain the filtered cerebral blood flow velocity and the filtered EEG signal.

[0085] In one embodiment, the low-frequency component of the cerebral blood flow velocity waveform is 0-0.10Hz, and the high-frequency component of the electroencephalogram (EEG) signal is 13-30Hz.

[0086] S22: Based on the filtered cerebral blood flow velocity and the filtered EEG signal, perform Hilbert transform to obtain the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal;

[0087] S23: Construct the first complex-valued signal based on the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal;

[0088] S24: The first composite complex-valued signal is given a random time delay to obtain a second composite complex-valued signal whose amplitude sequence undergoes random displacement;

[0089] S25: The uniformization difference between the first composite complex-valued signal and the second composite complex-valued signal is the modulation coefficient.

[0090] In one specific embodiment, the analysis steps for phase-amplitude cross-frequency coupling (PAC) include:

[0091] S201: Signal Preprocessing

[0092] A 0.0055–0.10 Hz bandpass filter (Chebyshev Type II filter) was used on the CBFV signal. A notch filter was used on the EEG signal to remove linear noise, and a 0.5–70 Hz bandpass filter was also applied. Independent component analysis (ICA) was used to remove artifacts from the electrooculogram and electrocardiogram.

[0093] S202: Calculate the PAC modulation index (MI)

[0094] PAC is used to describe the relationship between the phase of the low-frequency components (0-0.10Hz) in the CBFV waveform and the amplitude modulation of the high-frequency components (13-30Hz) in the EEG. After filtering CBFV and EEG according to the above frequency bands, the phase sequence of CBFV(t) and the amplitude sequence AEEG(t) of EEG(t) are extracted by Hilbert transform, and a composite complex-valued signal is constructed:

[0095]

[0096] The mean of z(t) is denoted as Mreal.

[0097] To obtain a distribution where AEEG and φCBFV are not coupled, a composite signal is constructed in which the amplitude sequence undergoes random displacement relative to the corresponding phase sequence; that is, a random time delay τ is introduced to construct a composite complex-valued signal.

[0098] The mean of z(t, τ) is denoted as μ, and the standard deviation is denoted as σ.

[0099]

[0100] The uniformized difference between z(t) and z(t, τ) is the modulation coefficient MI:

[0101] The Granger causality analysis yields the Granger causality index, which reflects changes in cerebral blood flow velocity caused by electroencephalogram (EEG) signals; the calculation steps for the Granger causality index include:

[0102]

[0103] S26: Establish the first autoregressive model to describe the intrinsic relationship of cerebral blood flow velocity;

[0104] The first autoregressive model is:

[0105]

[0106] S27: Establish a second autoregressive model to describe changes in cerebral blood flow velocity caused by changes in EEG signals;

[0107] The second autoregressive model is:

[0108]

[0109] Wherein, CBFV is cerebral blood flow velocity, EEG is electroencephalogram (EEG) signal, m is the model order, ai is the first parameter, bi is the second parameter, and ε' is the second parameter. t The residuals of the second autoregressive model;

[0110] The formula for calculating the F-value is:

[0111]

[0112] Where RSSx is the sum of squared residuals of the first autoregressive model, RSS'x is the sum of squared residuals of the second autoregressive model, m is the model order, and T is the number of observations in the second autoregressive model.

[0113] S28: The F-test is used to compare the goodness of fit of the first autoregressive model and the second autoregressive model. The calculated F-value is the Granger causality index.

[0114] In one specific embodiment, the analysis steps for Granger causality (GC) include:

[0115] S203: Signal Preprocessing

[0116] All missing channels or obvious EMG or EVO artifacts were removed. Data for each EEG and CBFV channel were normalized by subtracting the channel mean and dividing by the standard deviation. A 0.0055–0.4 Hz bandpass filter (Chebyshev Type II filter) was applied to the CBFV signal. Notch filtering and a 0.5–70 Hz bandpass filter were applied to the EEG signal to remove linear noise. The stationarity of the EEG and CBFV sequences was tested using the Augmented Dickey-Fuller unit root test.

[0117] S204: Calculation of Instantaneous Amplitude of EEG Signal

[0118] The instantaneous amplitudes of five frequency bands [δ (0.5-4Hz), θ (4-7Hz), α (7-13Hz), β (13-30Hz), γ (30-45Hz)] of the EEG signal were extracted using Hilbert transform. Let vt represent the EEG channel, and its complex form Vt (Equation 1) is defined as:

[0119]

[0120] In the virtual signal It is derived from the Hilbert transformation of vt (Equation 2):

[0121]

[0122] Where PV is the Cauchy principal value. At any given time point, the instantaneous amplitude At of the EEG signal vt can be calculated as the analytic amplitude of the complex vector Vt, i.e. (Equation 3):

[0123]

[0124] S205: Determination of Granger Causality Index (GI) between EEG and CBFV

[0125] Because EEG causes changes in CBFV, we set up two autoregressive models:

[0126] Formula 4

[0127] Formula 5

[0128] Where m is the model order, ai and bi are parameters, and εt and This represents the model residuals. The statistical significance of the F-test, used to compare the goodness of fit of each model, can be used to estimate the strength of the Granger causal relationship between CBFV and EEG. In this case, the F-statistic is (Equation 6):

[0129]

[0130] Where RSSX and RSS'X are the residual sums of squares of the models in equations (4) and (5), respectively, and T is the number of observations used to estimate the binary model in equation (5). The F-statistic approximately follows an F-distribution with m degrees of freedom and (T-2m-1) under the null hypothesis. Using the Bayesian information criterion, the optimal order m is selected with a maximum order of m=15 (without downsampling). The F-value calculated according to equation (6) is the Granger causality index (GI) reflecting the causal relationship of EEG→CBFV.

[0131] In summary, the neurovascular coupling index (NVCx) is calculated using phase-amplitude cross-frequency coupling (PAC) and Granger causality (GC).

[0132] S3: Individualized blood pressure ranges are derived based on the subject's neurovascular coupling index and arterial blood pressure data, including:

[0133] S31: Divide the time period into 1 window, calculate the average value of the modulation coefficient of each channel of the EEG signal and / or the average value of the Granger causality index for each window, wherein the average value of the modulation coefficient of each channel of the EEG signal is the first neural coupling index, and the average value of the Granger causality index is the second neural coupling index, and calculate the average blood pressure corresponding to each window;

[0134] In one embodiment, the fixed time period is 300 seconds;

[0135] S32: Move the window by one at fixed time intervals and recalculate the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure;

[0136] In one embodiment, the fixed time interval is 10 seconds;

[0137] S33: Plot the first neural coupling index, the second neural coupling index and the corresponding average blood pressure obtained during the entire monitoring process, with the average blood pressure as the x-axis and the first neural coupling index and / or the second neural coupling index as the y-axis. Fit a quadratic function curve to obtain the neural coupling index-average blood pressure curve.

[0138] S34: The average of the ordinate of the peak of the neural coupling index-mean blood pressure curve of all subjects included in the cohort study was used as the first tangent point for diagnosing neurovascular coupling damage.

[0139] In one embodiment, the average value of the ordinate of the peak of the neurovascular coupling index-mean blood pressure curve of subjects who developed large-area cerebral infarction and / or symptomatic hemorrhage after vascular recanalization therapy was selected as the second tangent point for diagnosing neurovascular coupling damage.

[0140] S35: Determine the two intersection points of the neural coupling index-mean blood pressure curve of S33 with the first or second tangent point of S34 on the ordinate. The mean blood pressure range corresponding to these two intersection points is the individualized blood pressure range of the subject.

[0141] In one specific embodiment, the individualized blood pressure range is determined by calculating the midpoint (MI) and gamma (GI) of each EEG lead based on phase-amplitude cross-frequency coupling and Granger causality, using 300-second segments (epochs). The average values ​​are then calculated, effectively obtaining NVCx (denoted as NVCx1 and NVCx2) within a 300-second window. Simultaneously, the mean blood pressure (MAP) within this window is calculated. NVCx and the corresponding MAP are calculated every 10 seconds as the window moves. MAP is grouped into sets of 5 mmHg. The NVCx and paired MAP sets obtained from a single full-length monitoring session are plotted to obtain the NVCx-MAP curve, as shown in the figure. Figure 6 As shown, the MAP corresponding to the vertex of the fitted quadratic function curve is the optimal MAP.

[0142] All patients who developed large-area cerebral infarction (defined as infarct size ≥ 2 / 3 of the middle cerebral artery watershed on one side) or symptomatic hemorrhagic transformation (defined as hematoma with mass effect, accompanied by altered consciousness or an increase in NIHSS score ≥ 4 points) within 24 hours after vascular recanalization were selected. The average ordinate of the peak of the NVCx-MAP curve for these patients was calculated as the cut-off point for diagnosing NVC damage, thus obtaining the individualized optimal MAP range. The above calculation process was implemented using Matlab 2019b software (Mathworks, USA).

[0143] The optimal NVC algorithm was determined by comparing the correlation between the individualized optimal blood pressure range derived from NVCx1 and NVCx2 and clinical and imaging outcomes.

[0144] S4: Determine whether the subject's blood pressure is within the individualized blood pressure range. If the subject's blood pressure is not within the individualized blood pressure range, output a prompt to control blood pressure.

[0145] S36: Calculate the duration during which the subject's blood pressure was above and / or below the individualized blood pressure range within a fixed time period after vascular recanalization treatment, statistically analyze the correlation between the duration and the classic neurovascular coupling function assessment, and determine the neuroprotective effect of the individualized blood pressure range.

[0146] In some embodiments, the method further includes S37: calculating the correlation between the duration and the subject's oxidative stress level and blood-brain barrier permeability to determine the neuroprotective effect of individualized blood pressure management.

[0147] The classic neurovascular coupling functional assessment evaluates the response of blood flow in the posterior cerebral artery to visual stimuli. This test is always performed in a dark room. The classic neurovascular coupling functional assessment includes:

[0148] ① Place two 2MHz TCD probes in the bilateral temporal windows, fix the head frame, and probe the bilateral posterior cerebral arteries. The sampling depth is 65mm and the sampling volume is 8-10mm.

[0149] ② Place a 15-inch monitor 50cm directly in front of the subject. The baseline test lasts for 2 minutes. Observe the black cross (approximately 1.5cm high and wide) in the center of the dark gray background on the monitor.

[0150] ③ After the baseline test, provide visual stimulation for 10 cycles, each lasting 40 seconds. Each cycle includes a 20-second (ON) flashing black and white checkerboard pattern, followed by a 20-second (OFF) rest period of the baseline pattern.

[0151] ④ Statistically determine the percentage increase in bilateral posterior cerebral artery flow velocity during the stimulation period compared to the baseline test.

[0152] The oxidative stress level was determined by measuring urinary 8-OHdG. Urine samples were collected from subjects 24 hours after cerebral infarction reperfusion and stored at -20°C. Urinary 8-OHdG levels were analyzed using an ELISA kit (Shanghai Sangon Biotech).

[0153] The blood-brain barrier permeability imaging and measurement includes:

[0154] Quantitative dynamic contrast-enhanced imaging (DCE-MRI): (Multi-flip angle T1 mask) TR 2472ms, TE 1196ms, slice thickness 5mm, matrix 160×128, flip angles: 3°, 6°, 9°, 12°, 15°; (T1-weighted DCE) flip angle 15°, other parameters same as multi-flip angle T1 mask, single scan time 6s, total 40 phases, total dynamic enhancement scan time 240s. After the second dynamic enhancement phase of the dynamic enhancement scan, a low molecular weight gadodiamine contrast agent, Omniscan (GE Healthcare, USA), was injected intravenously using a high-pressure injector at a rate of 2ml / s and a dose of 0.1mmol / kg. The catheter was then flushed with 15ml of normal saline at the same rate.

[0155] A circular region of interest (ROI) of 20-40 mm² was delineated at the center of the superior sagittal sinus. Time-concentration curves were obtained. Referring to T1WI, T2WI, and ADC, a circular ROI of 20-60 mm² was manually delineated in the area of ​​maximum enhancement of the infarct lesion on T1-weighted DCE images, avoiding softening lesions, cystic changes, hemorrhage, and normal vessels as much as possible. All ROIs were delineated on functional color images of permeability parameters and T1 perfusion parameters. The vascular permeability parameters of DCE-MRI were calculated using an Extended Tofts Linear dual-compartment model: transport constant (Ktrans) and extracellular extravascular space volume (ve). Higher Ktrans and ve indicate higher vascular permeability.

[0156] S38: Set a fixed target blood pressure range, calculate the duration of blood pressure above and / or below the individualized blood pressure range for a single patient within a fixed time after vascular recanalization treatment, compare the correlation differences between the two types of durations and neurological function and imaging outcomes, and determine the advantages of individualized blood pressure management relative to fixed target blood pressure.

[0157] In one embodiment, the fixed target blood pressure range is 120-140 mmHg;

[0158] In one embodiment, the neurological function and imaging outcomes include any one or more of the following: functional independence, ischemic stroke tissue volume progression, and symptomatic hemorrhagic transformation.

[0159] In one specific embodiment, the clinical outcome includes:

[0160] ① Main ending:

[0161] Functional independence on day 90 after onset: Modified Rankin Scale (mRS) score of 0-2, including complete asymptomatic; or symptomatic but without significant functional impairment, able to perform daily activities; or mild disability, not requiring assistance, and able to live independently.

[0162] ② Secondary ending:

[0163] 24 hours after recanalization, classic NVC evaluation: visual stimulation induced changes in posterior cerebral artery flow velocity.

[0164] Oxidative stress level 24 hours after recanalization: urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG, oxidative stress biomarker) concentration.

[0165] Blood-brain barrier permeability 24 hours after recanalization therapy: transport constant (Ktrans) and partial extracellular extravascular space volume (ve) measured by DCE-MRI.

[0166] The infarct volume increased on the 7th day after reperfusion treatment compared to 24 hours after reperfusion.

[0167] Symptomatic hemorrhagic transformation within 7 days after recanalization (defined as hematoma with mass effect, accompanied by altered consciousness or an increase of ≥4 points in NIHSS).

[0168] Figure 2 This invention provides an intelligent diagnostic and treatment system based on personalized blood pressure management, the system comprising:

[0169] 101 Acquisition Unit: Used to acquire the subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure data;

[0170] 102 Neurovascular Coupling Index Calculation Unit: Used to obtain the neurovascular coupling index based on the subject's electroencephalogram (EEG) signals and cerebral blood flow velocity data using phase-amplitude cross-frequency coupling and Granger causality analysis;

[0171] 103 Individualized Blood Pressure Range Calculation Unit: Used to obtain an individualized blood pressure range based on the subject's neurovascular coupling index and arterial blood pressure data;

[0172] 104 Intelligent Diagnosis and Treatment Unit: Used to determine whether the subject's blood pressure is within the individualized blood pressure range. When the subject's blood pressure is not within the individualized blood pressure range, it outputs a prompt to control blood pressure.

[0173] In some embodiments, the information prompts are auxiliary prediction results. The auxiliary prediction results output based on the information prompts include, but are not limited to, paper or electronic reports. These results are obtained by the intelligent machine based on the relevant data of the test subject and are only used as a reference for medical staff, and are not used as the final diagnosis result of the test subject.

[0174] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention, such as... Figure 3 As shown, the device may include: one or more processors and one or more memories; wherein the memories store computer-readable code that, when run by the one or more processors, can perform the methods described above.

[0175] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, operations, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 or ARM architecture.

[0176] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0177] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 4 The architecture of the computing device 3000 shown is used for implementation. For example... Figure 4As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the computing device shown.

[0178] This invention also includes a computer-readable storage medium, such as... Figure 5 The diagram illustrates a storage medium provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0179] This disclosure also provides a computer program product or system, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0180] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0181] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0182] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0184] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0185] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0186] The exemplary embodiments of this disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of this disclosure, and such modifications should fall within the scope of this disclosure.

Claims

1. A computer device, characterized in that, The device includes: a memory and a processor; the memory is used to store program instructions; the processor is used to invoke the program instructions, and when the program instructions are executed, to perform the steps of the following method, the method including: S1: Acquire data on the subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure; S2: The neurovascular coupling index was obtained by using phase-amplitude cross-frequency coupling and Granger causality analysis based on the subjects' electroencephalogram signals and cerebral blood flow velocity data; S3: Obtain an individualized blood pressure range based on the subject's neurovascular coupling index and arterial blood pressure data; this includes: S31: Dividing the time period into one window, calculating the average value of the modulation coefficients of each conduction of the EEG signal and / or the average value of the Granger causality index for each window, wherein the average value of the modulation coefficients of each conduction of the EEG signal is the first neural coupling index, and the average value of the Granger causality index is the second neural coupling index, and calculating the average blood pressure corresponding to each window; S32: Moving one window at fixed time intervals, recalculating the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure; S33: Plotting the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure obtained throughout the monitoring process, and averaging... Using blood pressure as the x-axis and the first and / or second neural coupling indices as the y-axis, a quadratic function curve is fitted to obtain the neural coupling index-mean blood pressure curve; S34: The average ordinate of the peak of the neural coupling index-mean blood pressure curve of all subjects included in the cohort study is used as the first tangent point for diagnosing neurovascular coupling damage; The average ordinate of the peak of the neural coupling index-mean blood pressure curve of subjects who developed large-area cerebral infarction and / or symptomatic hemorrhage after vascular recanalization treatment is used as the second tangent point for diagnosing neurovascular coupling damage; S35: Determine the two intersection points of the neural coupling index-mean blood pressure curve of S33 with the ordinate of the first or second tangent point of S34, and the average blood pressure range corresponding to the two intersection points is the individualized blood pressure range of the subject; S4: Determine whether the subject's blood pressure is within the individualized blood pressure range. If the subject's blood pressure is not within the individualized blood pressure range, output a prompt to control blood pressure. S36: Calculate the duration during which the subject's blood pressure was above and / or below the individualized blood pressure range within a fixed time period after vascular recanalization treatment, statistically analyze the correlation between the duration and the classic neurovascular coupling function assessment, and determine the neuroprotective effect of the individualized blood pressure range.

2. The computer device according to claim 1, characterized in that, The phase-amplitude cross-frequency coupling is used to describe the relationship between the phase of the low-frequency component in the cerebral blood flow velocity waveform and the amplitude modulation of the high-frequency component of the EEG signal.

3. The computer device according to claim 2, characterized in that, The amplitude modulation relationship is quantified by modulation coefficients, and the calculation steps of the modulation coefficients include: S21: Select the low-frequency components of the cerebral blood flow velocity waveform and the high-frequency components of the EEG signal to obtain the filtered cerebral blood flow velocity and the filtered EEG signal. S22: Based on the filtered cerebral blood flow velocity and the filtered EEG signal, perform Hilbert transform to obtain the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal; S23: Construct the first complex-valued signal based on the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal; S24: The first composite complex-valued signal is given a random time delay to obtain a second composite complex-valued signal whose amplitude sequence undergoes random displacement; S25: The uniformization difference between the first composite complex-valued signal and the second composite complex-valued signal is the modulation coefficient.

4. The computer device according to claim 1, characterized in that, The low-frequency component of the cerebral blood flow velocity waveform is 0-0.10Hz, and the high-frequency component of the electroencephalogram (EEG) signal is 13-30Hz.

5. The computer device according to claim 1, characterized in that, The Granger causality analysis yields the Granger causality index, which reflects changes in cerebral blood flow velocity caused by electroencephalogram (EEG) signals; the calculation steps for the Granger causality index include: S26: Establish the first autoregressive model to describe the intrinsic relationship of cerebral blood flow velocity; S27: Establish a second autoregressive model to describe changes in cerebral blood flow velocity caused by changes in EEG signals; S28: The F-test is used to compare the goodness of fit of the first autoregressive model and the second autoregressive model. The calculated F-value is the Granger causality index.

6. The computer device according to claim 5, characterized in that, The first autoregressive model is: Where CBFV is cerebral blood flow velocity, m is the model order, and a i ε is the first parameter. t The residuals are from the first autoregressive model.

7. The computer device according to claim 5, characterized in that, The second autoregressive model is: Where CBFV is cerebral blood flow velocity, EEG is electroencephalogram (EEG) signal, m is the model order, and a i b is the first parameter i ε' is the second parameter. t The residuals are from the second autoregressive model.

8. The computer device according to claim 5, characterized in that, The formula for calculating the F value is: Where RSSx is the sum of squared residuals of the first autoregressive model, RSS'x is the sum of squared residuals of the second autoregressive model, m is the model order, and T is the number of observations in the second autoregressive model.

9. The computer device according to claim 1, characterized in that, The fixed time period is 300 seconds.

10. The computer device according to claim 1, characterized in that, The fixed time interval is 10 seconds.

11. The computer device according to claim 1, characterized in that, The method further includes: S37: calculating the correlation between the duration and the subject's oxidative stress level and blood-brain barrier permeability to determine the neuroprotective effect of individualized blood pressure management.

12. The computer device according to claim 11, characterized in that, The method further includes: S38: Set a fixed target blood pressure range, calculate the duration for which blood pressure is above and / or below the individualized blood pressure range within a fixed time after vascular recanalization treatment for a single patient, compare the correlation differences between the two types of durations and neurological function and imaging outcomes, and determine the advantages of individualized blood pressure management relative to fixed target blood pressure.

13. The computer device according to claim 12, characterized in that, The fixed target blood pressure range is 120-140 mmHg.

14. The computer device according to claim 12, characterized in that, The neurological and imaging outcomes include any one or more of the following: functional independence, ischemic stroke tissue volume progression, and symptomatic hemorrhagic transformation.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the following method, the method comprising: S1: Acquire data on the subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure; S2: The neurovascular coupling index was obtained by using phase-amplitude cross-frequency coupling and Granger causality analysis based on the subjects' electroencephalogram signals and cerebral blood flow velocity data; S3: Obtain an individualized blood pressure range based on the subject's neurovascular coupling index and arterial blood pressure data; this includes: S31: Dividing the time period into one window, calculating the average value of the modulation coefficients of each conduction of the EEG signal and / or the average value of the Granger causality index for each window, wherein the average value of the modulation coefficients of each conduction of the EEG signal is the first neural coupling index, and the average value of the Granger causality index is the second neural coupling index, and calculating the average blood pressure corresponding to each window; S32: Moving one window at fixed time intervals, recalculating the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure; S33: Plotting the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure obtained throughout the monitoring process, and averaging... Using blood pressure as the x-axis and the first and / or second neural coupling indices as the y-axis, a quadratic function curve is fitted to obtain the neural coupling index-mean blood pressure curve; S34: The average ordinate of the peak of the neural coupling index-mean blood pressure curve of all subjects included in the cohort study is used as the first tangent point for diagnosing neurovascular coupling damage; The average ordinate of the peak of the neural coupling index-mean blood pressure curve of subjects who developed large-area cerebral infarction and / or symptomatic hemorrhage after vascular recanalization treatment is used as the second tangent point for diagnosing neurovascular coupling damage; S35: Determine the two intersection points of the neural coupling index-mean blood pressure curve of S33 with the ordinate of the first or second tangent point of S34, and the average blood pressure range corresponding to the two intersection points is the individualized blood pressure range of the subject; S4: Determine whether the subject's blood pressure is within the individualized blood pressure range. If the subject's blood pressure is not within the individualized blood pressure range, output a prompt to control blood pressure. S36: Calculate the duration during which the subject's blood pressure was above and / or below the individualized blood pressure range within a fixed time period after vascular recanalization treatment, statistically analyze the correlation between the duration and the classic neurovascular coupling function assessment, and determine the neuroprotective effect of the individualized blood pressure range.

16. The computer-readable storage medium according to claim 15, characterized in that, The phase-amplitude cross-frequency coupling is used to describe the relationship between the phase of the low-frequency component in the cerebral blood flow velocity waveform and the amplitude modulation of the high-frequency component of the EEG signal.

17. The computer-readable storage medium according to claim 16, characterized in that, The amplitude modulation relationship is quantified by modulation coefficients, and the calculation steps of the modulation coefficients include: S21: Select the low-frequency components of the cerebral blood flow velocity waveform and the high-frequency components of the EEG signal to obtain the filtered cerebral blood flow velocity and the filtered EEG signal. S22: Based on the filtered cerebral blood flow velocity and the filtered EEG signal, perform Hilbert transform to obtain the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal; S23: Construct the first complex-valued signal based on the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal; S24: The first composite complex-valued signal is given a random time delay to obtain a second composite complex-valued signal whose amplitude sequence undergoes random displacement; S25: The uniformization difference between the first composite complex-valued signal and the second composite complex-valued signal is the modulation coefficient.

18. The computer-readable storage medium according to claim 15, characterized in that, The low-frequency component of the cerebral blood flow velocity waveform is 0-0.10Hz, and the high-frequency component of the electroencephalogram (EEG) signal is 13-30Hz.

19. The computer-readable storage medium according to claim 15, characterized in that, The Granger causality analysis yields the Granger causality index, which reflects changes in cerebral blood flow velocity caused by electroencephalogram (EEG) signals; the calculation steps for the Granger causality index include: S26: Establish the first autoregressive model to describe the intrinsic relationship of cerebral blood flow velocity; S27: Establish a second autoregressive model to describe changes in cerebral blood flow velocity caused by changes in EEG signals; S28: The F-test is used to compare the goodness of fit of the first autoregressive model and the second autoregressive model. The calculated F-value is the Granger causality index.

20. The computer-readable storage medium according to claim 19, characterized in that, The first autoregressive model is: Where CBFV is cerebral blood flow velocity, m is the model order, and a i ε is the first parameter. t The residuals are from the first autoregressive model.

21. The computer-readable storage medium according to claim 19, characterized in that, The second autoregressive model is: Where CBFV is cerebral blood flow velocity, EEG is electroencephalogram (EEG) signal, m is the model order, and a i b is the first parameter i ε' is the second parameter. t The residuals are from the second autoregressive model.

22. The computer-readable storage medium according to claim 19, characterized in that, The formula for calculating the F value is: Where RSSx is the sum of squared residuals of the first autoregressive model, RSS'x is the sum of squared residuals of the second autoregressive model, m is the model order, and T is the number of observations in the second autoregressive model.

23. The computer-readable storage medium according to claim 15, characterized in that, The fixed time period is 300 seconds.

24. The computer-readable storage medium according to claim 15, characterized in that, The fixed time interval is 10 seconds.

25. The computer-readable storage medium according to claim 15, characterized in that, The method further includes: S37: calculating the correlation between the duration and the subject's oxidative stress level and blood-brain barrier permeability to determine the neuroprotective effect of individualized blood pressure management.

26. The computer-readable storage medium according to claim 25, characterized in that, The method further includes: S38: Set a fixed target blood pressure range, calculate the duration for which blood pressure is above and / or below the individualized blood pressure range within a fixed time after vascular recanalization treatment for a single patient, compare the correlation differences between the two types of durations and neurological function and imaging outcomes, and determine the advantages of individualized blood pressure management relative to fixed target blood pressure.

27. The computer-readable storage medium according to claim 26, characterized in that, The fixed target blood pressure range is 120-140 mmHg.

28. The computer-readable storage medium according to claim 26, characterized in that, The neurological and imaging outcomes include any one or more of the following: functional independence, ischemic stroke tissue volume progression, and symptomatic hemorrhagic transformation.

29. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the following method, the method comprising: S1: Acquire data on the subject's blood pressure, electroencephalogram (EEG) signals, cerebral blood flow velocity, and arterial blood pressure; S2: The neurovascular coupling index was obtained by using phase-amplitude cross-frequency coupling and Granger causality analysis based on the subjects' electroencephalogram signals and cerebral blood flow velocity data; S3: Obtain an individualized blood pressure range based on the subject's neurovascular coupling index and arterial blood pressure data; this includes: S31: Dividing the time period into one window, calculating the average value of the modulation coefficients of each conduction of the EEG signal and / or the average value of the Granger causality index for each window, wherein the average value of the modulation coefficients of each conduction of the EEG signal is the first neural coupling index, and the average value of the Granger causality index is the second neural coupling index, and calculating the average blood pressure corresponding to each window; S32: Moving one window at fixed time intervals, recalculating the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure; S33: Plotting the first neural coupling index, the second neural coupling index, and the corresponding average blood pressure obtained throughout the monitoring process, and averaging... Using blood pressure as the x-axis and the first and / or second neural coupling indices as the y-axis, a quadratic function curve is fitted to obtain the neural coupling index-mean blood pressure curve; S34: The average ordinate of the peak of the neural coupling index-mean blood pressure curve of all subjects included in the cohort study is used as the first tangent point for diagnosing neurovascular coupling damage; The average ordinate of the peak of the neural coupling index-mean blood pressure curve of subjects who developed large-area cerebral infarction and / or symptomatic hemorrhage after vascular recanalization treatment is used as the second tangent point for diagnosing neurovascular coupling damage; S35: Determine the two intersection points of the neural coupling index-mean blood pressure curve of S33 with the ordinate of the first or second tangent point of S34, and the average blood pressure range corresponding to the two intersection points is the individualized blood pressure range of the subject; S4: Determine whether the subject's blood pressure is within the individualized blood pressure range. If the subject's blood pressure is not within the individualized blood pressure range, output a prompt to control blood pressure. S36: Calculate the duration during which the subject's blood pressure was above and / or below the individualized blood pressure range within a fixed time period after vascular recanalization treatment, statistically analyze the correlation between the duration and the classic neurovascular coupling function assessment, and determine the neuroprotective effect of the individualized blood pressure range.

30. The computer program product according to claim 29, characterized in that, The phase-amplitude cross-frequency coupling is used to describe the relationship between the phase of the low-frequency component in the cerebral blood flow velocity waveform and the amplitude modulation of the high-frequency component of the EEG signal.

31. The computer program product according to claim 30, characterized in that, The amplitude modulation relationship is quantified by modulation coefficients, and the calculation steps of the modulation coefficients include: S21: Select the low-frequency components of the cerebral blood flow velocity waveform and the high-frequency components of the EEG signal to obtain the filtered cerebral blood flow velocity and the filtered EEG signal. S22: Based on the filtered cerebral blood flow velocity and the filtered EEG signal, perform Hilbert transform to obtain the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal; S23: Construct the first complex-valued signal based on the phase sequence of cerebral blood flow velocity and the amplitude sequence of EEG signal; S24: The first composite complex-valued signal is given a random time delay to obtain a second composite complex-valued signal whose amplitude sequence undergoes random displacement; S25: The uniformization difference between the first composite complex-valued signal and the second composite complex-valued signal is the modulation coefficient.

32. The computer program product according to claim 29, characterized in that, The low-frequency component of the cerebral blood flow velocity waveform is 0-0.10Hz, and the high-frequency component of the electroencephalogram (EEG) signal is 13-30Hz.

33. The computer program product according to claim 29, characterized in that, The Granger causality analysis yields the Granger causality index, which reflects changes in cerebral blood flow velocity caused by electroencephalogram (EEG) signals; the calculation steps for the Granger causality index include: S26: Establish the first autoregressive model to describe the intrinsic relationship of cerebral blood flow velocity; S27: Establish a second autoregressive model to describe changes in cerebral blood flow velocity caused by changes in EEG signals; S28: The F-test is used to compare the goodness of fit of the first autoregressive model and the second autoregressive model. The calculated F-value is the Granger causality index.

34. The computer program product according to claim 33, characterized in that, The first autoregressive model is: Where CBFV is cerebral blood flow velocity, m is the model order, and a i ε is the first parameter. t The residuals are those of the first autoregressive model.

35. The computer program product according to claim 33, characterized in that, The second autoregressive model is: Where CBFV is cerebral blood flow velocity, EEG is electroencephalogram (EEG) signal, m is the model order, and a i b is the first parameter i ε' is the second parameter. t The residuals are from the second autoregressive model.

36. The computer program product according to claim 33, characterized in that, The formula for calculating the F value is: Where RSSx is the sum of squared residuals of the first autoregressive model, RSS'x is the sum of squared residuals of the second autoregressive model, m is the model order, and T is the number of observations in the second autoregressive model.

37. The computer program product according to claim 29, characterized in that, The fixed time period is 300 seconds.

38. The computer program product according to claim 29, characterized in that, The fixed time interval is 10 seconds.

39. The computer program product according to claim 29, characterized in that, The method further includes: S37: calculating the correlation between the duration and the subject's oxidative stress level and blood-brain barrier permeability to determine the neuroprotective effect of individualized blood pressure management.

40. The computer program product according to claim 39, characterized in that, The method further includes: S38: Set a fixed target blood pressure range, calculate the duration for which blood pressure is above and / or below the individualized blood pressure range within a fixed time after vascular recanalization treatment for a single patient, compare the correlation differences between the two types of durations and neurological function and imaging outcomes, and determine the advantages of individualized blood pressure management relative to fixed target blood pressure.

41. The computer program product according to claim 40, characterized in that, The fixed target blood pressure range is 120-140 mmHg.

42. The computer program product according to claim 40, characterized in that, The neurological and imaging outcomes include any one or more of the following: functional independence, ischemic stroke tissue volume progression, and symptomatic hemorrhagic transformation.

Images