Neurovascular coupling-based spinal cord electrical stimulation prognosis prediction system for patients with disturbance of consciousness

Abstract

The invention discloses a spinal cord electrical stimulation prognosis prediction system for a patient with disturbance of consciousness based on neurovascular coupling, and belongs to the technical field of brain-computer interfaces. The system synchronously acquires electroencephalogram and cerebral blood flow signals of the patient through electroencephalogram acquisition equipment and transcranial Doppler ultrasound; brain network connections are calculated by using weighted pairwise phase congruency indexes, neurovascular coupling parameters are calculated based on a phase-amplitude coupling method, and finally results are presented through a display module to predict prognosis. The system can solve the problem of clinical lack of prognosis evaluation indexes of SCS treatment consciousness disorder patients, has the advantages of simple acquisition equipment, reusability and the like, and can assist doctors in providing personalized clinical schemes and reducing the treatment cycle; the system relates to a data acquisition, processing and result display module, a key parameter calculation process is clear, and the system is suitable for prognosis evaluation after SCS implantation of a consciousness disorder patient.

Description

Technical Field

[0001] The present invention relates to the technical field of brain-computer interface (BCI), and in particular to a spinal cord electrical stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling. Background Art

[0002] Impairment of consciousness refers to varying degrees of changes in an individual's state of consciousness, usually manifested as a weakened or lost ability to respond to external stimuli. Impairment of consciousness can be caused by a variety of reasons, including neurological diseases, metabolic disorders, drug poisoning, infection, trauma, etc. Among them, traumatic brain injury (TBI) and non-traumatic brain injury (cerebrovascular disease, hypoxic encephalopathy) are the main causes of the disease. There are approximately 50 million to 60 million new TBI patients worldwide each year, and about 0.3% of patients will develop impaired consciousness. In addition, more than 12.2 million new cases of stroke each year will also lead to many patients with impaired consciousness.

[0003] The treatment and prognosis of patients with impaired consciousness place considerable pressure and burden on physicians and their families. First, the treatment time for patients with impaired consciousness varies greatly. Acute impaired consciousness, if promptly diagnosed and effectively treated, may resolve within days or weeks. Chronic or severe impaired consciousness, such as those caused by cerebrovascular events or degenerative diseases, may require months or even longer of treatment and rehabilitation. Neuromodulation therapy, a commonly used clinical approach for treating patients with impaired consciousness, offers personalized treatment options tailored to the patient's condition, with the advantages of minimal side effects and repeatability. Currently, neuromodulation therapies used in the clinical treatment of patients with DOC include transcranial magnetic stimulation, deep brain stimulation, transcranial direct current stimulation, spinal cord stimulation, vagus nerve stimulation, and functional electrical stimulation. Implantable spinal cord stimulation (SCS) involves implanting stimulating electrodes along the epidural space at the C2-C4 level, modulating stimulation at a frequency of 70 Hz to increase neuronal activity. However, clinical observations suggest that there are no clear predictors of the prognosis of patients undergoing SCS implantation. This not only makes it impossible for doctors to make personalized adjustments to patients' medical plans in a timely manner, but also means that patients' families are unable to understand the patient's treatment efficacy and status in a timely manner, causing a greater psychological burden.

[0004] BCI refers to an artificially constructed pathway between the brain and computers and external devices that is different from traditional brain information transmission. It can replace, rebuild, strengthen, supplement or improve the normal output of the central nervous system. According to the characteristics of EEG signals, it can be divided into active, reactive and passive. Passive BCI refers to inferring the user's state, emotion or cognitive load by monitoring brain electrical activity or physiological signals (such as EEG, EOG, etc.) without actively controlling brain waves or generating specific intentions. Brain network connections calculated based on EEG data and neurovascular coupling parameters calculated based on EEG and TCD data are both passive BCI. They reflect the functional synergy of various brain regions and the dynamic coupling between brain activity and blood supply, helping to infer the brain's health status and cognitive load. This is reflected by analyzing the relationship between EEG signals and cerebral blood flow.

[0005] Resting-State Brain Network Connectivity refers to the network connection formed by the spontaneous activity between brain regions in the absence of external tasks or specific behavioral instructions. The resting-state brain network reflects the collaborative activities of different brain regions by detecting the functional connectivity patterns between different brain regions. The present invention uses the weighted pairwise phase consistency index (WPPC) to calculate the patient's resting-state brain network connectivity, which can more accurately and robustly reflect the functional connectivity between different brain regions. The brain network connection calculated based on EEG has many advantages such as extremely high temporal resolution, relatively low monitoring cost, portable equipment, and simple acquisition.

[0006] Neurovascular coupling (NVC) refers to the interaction between neural activity and local blood flow. In the brain, increased neural activity causes a regulation of blood flow, thereby providing more oxygen and nutrients to meet the energy needs of the activated area. The phase-amplitude coupling method based on EEG and TCD blood flow data is used to calculate neurovascular coupling. It mainly explores the dynamic coupling mechanism of neural activity and blood flow by analyzing the phase and amplitude relationship between neural electrical activity and blood flow. This method not only has high temporal resolution and accuracy, but also can analyze nonlinear and complex neurovascular interactions. It also has the advantages of low cost and reusability.

[0007] Passive BCIs are non-invasive, easy to detect, reusable, and low-cost, making them widely used in various clinical examinations. The proposed method of brain network connectivity and neurovascular coupling has great potential for predicting the prognosis of patients with impaired consciousness after surgery, thereby helping doctors assess their postoperative status. Summary of the Invention

[0008] The purpose of the present invention is to provide a spinal cord electrical stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling, so as to solve the problems in the prior art that there are no clear indicators for observing and evaluating the therapeutic effect of implanted spinal cord nerve stimulators in curing patients with impaired consciousness, and that the prognosis effect of implanted spinal cord nerve stimulators is often difficult to predict.

[0009] To achieve the above objectives, the present invention provides a prognosis prediction system for spinal cord electrical stimulation of patients with impaired consciousness based on neurovascular coupling, comprising:

[0010] The data acquisition module uses EEG acquisition equipment and transcranial Doppler ultrasound to achieve synchronous acquisition of patients' EEG and cerebral blood flow signals;

[0011] The EEG and cerebral blood flow data processing module pre-processes the original EEG signals collected by the data acquisition module and calculates brain network connectivity and neurovascular coupling through the pre-processed data;

[0012] The output display module displays the characteristic parameters processed by the EEG and cerebral blood flow data processing module.

[0013] Preferably, when the data acquisition module is working, the transcranial Doppler ultrasound probe is fixed at the temporal part, that is, about 2-3 cm above the ear, usually at the midpoint of the line connecting the eyebrow and the ear; the sampling rate is set to 125 Hz to collect information such as the cerebral blood flow velocity of the middle cerebral artery (MCA); considering that the TCD probe occupies part of the head, the EEG cap leads need to be reduced as much as possible, so the EEG cap adopts a 19-lead EEG acquisition system designed by the international 10-20 system; standard Ag / Agcl electrodes are used, the sampling rate is set to 512 Hz, the forehead is grounded, and the impedance between the scalp and the electrode is kept below 10KΩ; the collected EEG signals and cerebral blood flow information are saved in EDF and txt files respectively.

[0014] Preferably, the EEG and cerebral blood flow data processing module includes an EEG data preprocessing unit, a cerebral blood flow data processing unit, a brain network connection calculation unit and a neurovascular coupling calculation unit.

[0015] Preferably, the EEG data preprocessing unit uses EEGLAB, an EEG processing toolbox developed based on MATLAB, to preprocess the original EEG signal; the steps include:

[0016] S11, bandpass filtering: The data is filtered using a third-order Butterworth bandpass filter with a filtering range of 0.5-40 Hz to remove extremely low-frequency and extremely high-frequency interference in the EEG signal;

[0017] S12, 50Hz notch filter: removes 50Hz power frequency interference from the acquisition system;

[0018] S13. Interpolation of bad leads: Use the interpolation of bad leads function in the EEGLAB toolkit to remove distorted leads;

[0019] S14, downsampling: When calculating neurovascular coupling, the EEG sampling rate is reduced to 125 Hz to maintain the synchronization of EEG and cerebral blood flow data;

[0020] S15. ICA artifact removal: Use the ICA artifact removal function in the EEGLAB toolkit to remove electrooculogram and electromyography artifacts that appear during the acquisition process;

[0021] S16. Data segmentation: Divide the EEG data into several 5-second data, calculate the brain network connection respectively, and take the average as the average brain network connection state of this period.

[0022] Preferably, the cerebral blood flow data processing module obtains the frequency band of interest in the cerebral blood flow envelope value collected by TCD through filtering, and extracts the corresponding phase information.

[0023] Preferably, the connectivity of the brain network is calculated based on the weighted pairwise phase consistency index, and the calculation steps are as follows:

[0024] S21, phase extraction: perform wavelet transform on the time series of each brain region to extract phase information;

[0025] S22. Calculate the phase difference: For each lead, calculate its phase difference. The formula is:

[0026] Δφ ij (t) = φ i (t)-φ j (t) (1)

[0027] Among them, Δφ ij (t) represents the phase difference between the i-th lead and the j-th lead at time t; φ i (t) represents the phase value of the EEG signal of the i-th lead; φ j (t) represents the phase value of the EEG signal of the jth lead;

[0028] S23. Calculate the weighted pairwise phase consistency index: Calculate the amplitude of the phase difference. The formula is:

[0029]

[0030] Among them, WPPC ij represents the weighted pairwise phase consistency index between leads i and j; T represents the total number of signal time points; exp() belongs to the complex exponential function;

[0031] S24, Normalization: Perform normalization processing, the formula is:

[0032]

[0033] Among them, Normalized WPPC ij Represents the normalized weighted pairwise phase consistency index, with the output value in [0, 1].

[0034] Preferably, the neurovascular coupling calculation unit applies phase-amplitude coupling to calculate neurovascular coupling to obtain the coupling relationship between low-frequency blood flow and high-frequency neuronal activity. The calculation steps are as follows:

[0035] S31. Blood flow phase information extraction: The phase information of the frequency band of interest in the blood flow signal is extracted through Hilbert transform. The calculation formula is:

[0036]

[0037] in, Indicates the instantaneous phase value at time t; arg() indicates the calculation of the phase angle of the blood flow signal; Hilbert() performs Hilbert transform on the input signal to generate an analytical signal; CBFv low (t) low-frequency cerebral blood flow velocity signal at time t;

[0038] S32. EEG amplitude information extraction: The amplitude information of the frequency band of interest in the EEG signal is extracted through Hilbert transform. The calculation formula is:

[0039] AEEG(t)=|Hilbert(EEG high (t))| (5)

[0040] Among them, AEEG(t) represents the instantaneous amplitude of high-frequency EEG at time t; EEG hight (t) represents the high-frequency EEG signal at time t;

[0041] S33. Calculate the average amplitude in the phase. The calculation formula is:

[0042]

[0043] Where Z(t) represents the neurovascular coupling strength value at time t; It is a complex exponential function that combines blood flow phase information and EEG amplitude information; represents the instantaneous blood flow phase at time t;

[0044] S34. Calculate the coupling value: Calculate the average phase and amplitude of the complex exponential function and take the modulus. The calculation formula is:

[0045]

[0046] Wherein, PAC represents the neurovascular coupling strength value; represents the averaging of the complex exponential function, and T represents the total number of time points.

[0047] Preferably, the display module includes three parts: patient information, detailed results, and comparative predictions; the patient information part includes information such as the patient's age, gender, medical history, coma time, Coma Recovery Scale score, and the patient's nursing level; the detailed results part can view the detailed analysis results of the patient's single data of EEG in different frequency bands; the comparative prediction part gives the predicted results of the patient's prognosis by comparing the data collected at different time points.

[0048] Therefore, the present invention adopts the above-mentioned spinal cord electrical stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling, which has the following beneficial effects:

[0049] (1) The clinical prognosis of patients undergoing awakening treatment can be predicted by collecting EEG and cerebral blood flow data;

[0050] (2) The collection device of the present invention is simple, convenient and can be reused;

[0051] (3) Through objective evaluation of the patient's neuronal activity and cerebral blood flow recovery, doctors can be assisted in providing patients with personalized clinical plans, shortening the patient's treatment cycle and avoiding waste of doctors and medical resources.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a module diagram of the brain network and neurovascular coupling brain state observation system of the spinal cord electrical stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling of the present invention;

[0054] Figure 2 This is a schematic diagram showing brain network connection results in different frequency bands according to an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram showing the effect of neurovascular coupling results according to an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the actual display effect of an embodiment of the present invention;

[0057] Figure 5 This is a schematic diagram showing the detailed results interface of an embodiment of the present invention;

[0058] Figure 6This is a schematic diagram showing the interface of the comparison prediction part of an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0060] See also Figure 1 The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling mainly includes a data acquisition module, an EEG and cerebral blood flow data processing module and an output display module.

[0061] The data acquisition module uses an EEG acquisition device and transcranial Doppler ultrasound to synchronously acquire EEG and cerebral blood flow signals. Basic patient information (age, gender, medical history, duration of coma, CRS-R score, nursing level, etc.) is recorded, and confirmation is made that a spinal cord stimulator (SCS) has been implanted and the patient is in the postoperative monitoring period. The patient's scalp is cleaned and conductive cream is applied. A 19-lead EEG cap is placed according to the international 10-20 system, ensuring electrode impedance is less than 10 kΩ (measured using an impedance meter). The forehead electrode is grounded, and the reference electrode is placed on the earlobe to avoid motion artifacts. A TCD probe (2 MHz) is fixed to the patient's temporal window (2-3 cm above the midpoint of the line connecting the eyebrow and ear). After applying coupling gel, it is secured with an elastic bandage to ensure close contact between the probe and the skin. The EEG acquisition device (sampling rate 512 Hz, EDF format) and the TCD device (sampling rate 125 Hz, TXT format) are connected. Synchronous acquisition is initiated via the parallel communication module, and the timestamp alignment error is confirmed to be less than 1 ms. The collected EEG signals and cerebral blood flow information were saved in EDF and txt files for subsequent analysis.

[0062] The main functions of the EEG and cerebral blood flow data processing module include preprocessing the original EEG signal (collected by the data acquisition module); calculating the brain network connection and neurovascular coupling through the preprocessed data, including: EEG data preprocessing unit, cerebral blood flow data processing unit, brain network connection calculation unit, and neurovascular coupling calculation unit.

[0063] 1. EEG data preprocessing unit

[0064] EEG data preprocessing is performed on the raw EEG signals using EEGLAB, an EEG processing toolbox developed based on MATLAB. This includes bandpass filtering, 50Hz notch filtering, interpolation, downsampling, ICA artifact removal, and data segmentation. The steps are as follows:

[0065] Step 1: Bandpass filtering: The data was filtered using a third-order Butterworth bandpass filter with a filtering range of 0.5-40 Hz to remove extremely low-frequency and extremely high-frequency interference in the EEG signal.

[0066] Step 2, 50Hz notch filtering: mainly removes the 50Hz power frequency interference of the acquisition system;

[0067] Step 3. Interpolate bad leads: When the signal collected from individual leads is severely distorted, the interpolate bad leads function in the EEGLAB toolkit can be used to remove the distorted leads.

[0068] Step 4: Downsampling: The sampling rate can be retained for brain network connection calculations. When calculating neurovascular coupling, the EEG sampling rate needs to be reduced to 125 Hz to maintain the synchronization of EEG and cerebral blood flow data.

[0069] Step 5: ICA artifact removal: Use the ICA artifact removal function in the EEGLAB toolkit to remove electrooculogram and electromyography artifacts that may appear during the acquisition process.

[0070] Step 6: Data segmentation: Brain network calculation requires segmenting the EEG data. The brain network connection is calculated by dividing the EEG data into several 5-second data and taking the average as the average brain network connection state for this period.

[0071] 2. Cerebral blood flow data processing unit

[0072] Cerebral blood flow data processing is to obtain the frequency band of interest in the cerebral blood flow envelope value collected by TCD through filtering and extract the corresponding phase information.

[0073] 3. Brain Network Connectivity Computing Unit

[0074] The weighted pairwise phase consistency index is an indicator used to measure the phase synchronization between different brain regions in a brain network. Based on EEG time series data, the degree of phase coupling between different brain regions is calculated to assess the connectivity of the brain network. The calculation steps are as follows:

[0075] Step 1: Phase extraction: Perform wavelet transform on the time series of each brain region to extract phase information.

[0076] Step 2: Calculate the phase difference: For each lead, calculate their phase difference. The formula is:

[0077] Δφ ij (t) = φ i (t)-φ j (t) (1)

[0078] Among them, Δφ ij (t) represents the phase difference between the i-th lead and the j-th lead at time t;

[0079] φ i (t) represents the phase value of the EEG signal of the i-th lead; φ j (t) represents the phase value of the EEG signal of the jth lead;

[0080] Step 3: Calculate the weighted pairwise phase consistency index: Measure the degree of coupling between different leads by calculating the amplitude of the phase difference. The formula for the weighted pairwise phase consistency index is:

[0081]

[0082] Among them, WPPC ij represents the weighted pairwise phase consistency index between leads i and j; T represents the total number of signal time points; exp() belongs to the complex exponential function;

[0083] Step 4: Normalization: Normalize the values ​​so that they are between 0 and 1, where 0 indicates no phase synchronization and 1 indicates complete synchronization. The following formula can be used for normalization:

[0084]

[0085] 4. Neurovascular Coupling Calculation Unit

[0086] Neurovascular coupling calculation is phase-amplitude coupling (PAC), a technique used to analyze the interaction between different frequency bands in neural signals, particularly exploring the coupling relationship between low-frequency signals (such as theta waves and alpha waves) and high-frequency signals (such as gamma waves and beta waves). This paper applies PAC to calculate neurovascular coupling, thereby obtaining the coupling relationship between low-frequency blood flow and high-frequency neuronal activity. The calculation steps are as follows:

[0087] Step 1: Extract blood flow phase information: Extract the phase information of the frequency band of interest in the blood flow signal through Hilbert transform

[0088]

[0089] in, Indicates the instantaneous phase value at time t; arg() indicates the calculation of the phase angle of the blood flow signal; Hilbert() performs Hilbert transform on the input signal to generate an analytical signal; CBFv low (t) low-frequency cerebral blood flow velocity signal at time t;

[0090] Step 2: Extract EEG amplitude information: Extract the amplitude information of the frequency band of interest in the EEG signal through Hilbert transform

[0091] AEEG(t)=|Hilbert(EEGhigh (t))| (5)

[0092] Among them, AEEG(t) represents the instantaneous amplitude of high-frequency EEG at time t; EEG high (t) represents the high-frequency EEG signal at time t;

[0093] Step 3: Calculate the average amplitude in the phase:

[0094]

[0095] Where Z(t) represents the neurovascular coupling strength value at time t; It is a complex exponential function that combines blood flow phase information and EEG amplitude information; represents the instantaneous blood flow phase at time t;

[0096] S34. Calculate the coupling value: Calculate the average phase and amplitude of the complex exponential function and take the modulus. The calculation formula is:

[0097]

[0098] Wherein, PAC represents the neurovascular coupling strength value; represents the averaging of the complex exponential function, and T represents the total number of time points.

[0099] The display module presents the processed characteristic parameters in an intuitive and simple way, making it easy for doctors to receive and guide medical plans.

[0100] The brain network connectivity value is a value ranging from 0 to 1. By calculating the brain network connectivity values ​​of 19 leads in five EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma), five 19×19 matrices will be obtained. Figure 2 The connection status is clearly displayed.

[0101] The connecting lines between the electrodes in the image represent stronger connections. The greener and thicker the lines, the stronger the connection. Doctors can predict a patient's prognosis by comparing brain network connectivity in different frequency bands before and after surgery. Existing data suggests that when brain network connectivity in the frontal lobe of a patient strengthens in the Theta, Alpha, Beta, and Gamma frequency bands, the patient's prognosis improves, and the probability of recovery is higher.

[0102] Neurovascular coupling is a value between 0 and 5. By dividing the brain into regions and different EEG frequency bands, a 7×5 array can be obtained. By adjusting the brain region division, the output can be divided into seven parts: left frontal lobe, right frontal lobe, left temporal lobe, right temporal lobe, central area, parietal lobe, and occipital lobe. It can also be output as frontal lobe, temporal lobe, central area, parietal lobe, and occipital lobe. The output effect of different regions of a single frequency band of single data is as follows: Figure 3 shown.

[0103] By comparing changes in neurovascular coupling before and after surgery, doctors can predict a patient's prognosis. Existing data suggests that when neurovascular coupling strength in the frontal lobe is enhanced, the patient has a good prognosis and a greater chance of recovery.

[0104] The final display effect of the display module is as follows Figure 4 As shown, it is mainly divided into three parts: patient information, detailed results, and comparative prediction (detailed function display see Figure 5 , Figure 6 ). Click on the data of any patient in the patient database to view the postoperative prediction information of the patient. The patient information section includes information such as the patient's age, gender, medical history, coma time, Coma Recovery Scale–Revised (CRS-R) score, and the patient's nursing level. In the detailed results section, you can view the detailed analysis results of the patient's EEG in different frequency bands of a single data. By clicking the switch button on the upper side, you can view the patient's data information at different time points, such as preoperative information and postoperative information. By clicking the switch button on the lower side, you can view the information of different frequency bands of the patient's single data, such as Delta, Theta, Alpha, Beta, and Gamma. The comparative prediction section gives the predicted results of the patient's prognosis by comparing the data collected at different time points. In order to more intuitively reflect the changes in the patient's neurovascular coupling, the preoperative and postoperative results are normalized to highlight the improvement of different brain regions.

[0105] Therefore, the present invention utilizes the aforementioned neurovascular coupling-based spinal cord stimulation prognosis prediction system for patients with impaired consciousness and innovatively proposes a prognosis prediction method for DOC patients undergoing SCS implantation. By collecting EEG and cerebral blood flow data, the patient's clinical prognosis can be accurately predicted. This solves the problem of prognostic uncertainty for DOC patients after neuromodulation therapy, helps doctors provide timely, personalized treatment plans, shortens the patient treatment cycle, and is expected to achieve considerable social and economic benefits.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A prognosis prediction system for spinal cord stimulation in patients with impaired consciousness based on neurovascular coupling, characterized by: include: The data acquisition module uses EEG acquisition equipment and transcranial Doppler ultrasound to achieve synchronous acquisition of patients' EEG and cerebral blood flow signals; The EEG and cerebral blood flow data processing module pre-processes the original EEG signals collected by the data acquisition module and calculates brain network connectivity and neurovascular coupling through the pre-processed data; The output display module displays the characteristic parameters processed by the EEG and cerebral blood flow data processing module.

2. The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling according to claim 1, characterized in that: When the data acquisition module is working, the transcranial Doppler ultrasound probe is fixed to the temporal region, usually at the midpoint of the line connecting the eyebrows and ears; the sampling rate is set to 125Hz; the EEG cap adopts a 19-lead EEG acquisition system designed with the international 10-20 system; standard Ag / Agcl electrodes are used, the sampling rate is set to 512Hz, the forehead is grounded, and the impedance between the scalp and the electrodes is kept below 10KΩ; the collected EEG signals and cerebral blood flow information are saved as EDF and txt files, respectively.

3. The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling according to claim 1, characterized in that: The EEG and cerebral blood flow data processing module includes an EEG data preprocessing unit, a cerebral blood flow data processing unit, a brain network connection calculation unit, and a neurovascular coupling calculation unit.

4. The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling according to claim 3, characterized in that: The EEG data preprocessing unit uses EEGLAB, an EEG processing toolbox developed based on MATLAB, to preprocess the raw EEG signals; The following steps are involved: S11, bandpass filtering: The data is filtered using a third-order Butterworth bandpass filter with a filtering range of 0.5-40 Hz to remove extremely low-frequency and extremely high-frequency interference in the EEG signal; S12, 50Hz notch filter: removes 50Hz power frequency interference from the acquisition system; S13. Interpolation of bad leads: Use the interpolation of bad leads function in the EEGLAB toolkit to remove distorted leads; S14, downsampling: When calculating neurovascular coupling, the EEG sampling rate is reduced to 125 Hz to maintain the synchronization of EEG and cerebral blood flow data; S15. ICA artifact removal: Use the ICA artifact removal function in the EEGLAB toolkit to remove electrooculogram and electromyography artifacts that appear during the acquisition process; S16. Data segmentation: Divide the EEG data into several 5-second data, calculate the brain network connection respectively, and take the average as the average brain network connection state of this period.

5. The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling according to claim 3, characterized in that: The cerebral blood flow data processing module obtains the frequency band of interest in the cerebral blood flow envelope value collected by TCD through filtering and extracts the corresponding phase information.

6. The prognosis prediction system for spinal cord stimulation of patients with impaired consciousness based on neurovascular coupling according to claim 3, characterized in that: The connectivity of the brain network was calculated based on the weighted pairwise phase consistency index. The calculation steps are as follows: S21, phase extraction: perform wavelet transform on the time series of each brain region to extract phase information; S22. Calculate the phase difference: For each lead, calculate its phase difference. The formula is: Df ij (t)=φ i (t)-φ j (t) (1) Among them, Δφ ij (t) represents the phase difference between the i-th lead and the j-th lead at time t; φ i (t) represents the phase value of the EEG signal of the i-th lead; φ j (t) represents the phase value of the EEG signal of the jth lead; S23. Calculate the weighted pairwise phase consistency index: Calculate the amplitude of the phase difference. The formula is: Among them, WPPC ij represents the weighted pairwise phase consistency index between leads i and j; T represents the total number of signal time points; exp() belongs to the complex exponential function; S24, Normalization: Perform normalization processing, the formula is: Among them, Normalized WPPC ij Represents the normalized weighted pairwise phase consistency index, with the output value in [0, 1].

7. The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling according to claim 3, characterized in that: The neurovascular coupling calculation unit applies phase-amplitude coupling to calculate neurovascular coupling and obtains the coupling relationship between low-frequency blood flow and high-frequency neuronal activity. The calculation steps are as follows: S31. Blood flow phase information extraction: The phase information of the frequency band of interest in the blood flow signal is extracted through Hilbert transform. The calculation formula is: in, Indicates the instantaneous phase value at time t; arg() indicates the calculation of the phase angle of the blood flow signal; Hilbert() performs Hilbert transform on the input signal to generate an analytical signal; CBFv low (t) low-frequency cerebral blood flow velocity signal at time t; S32. EEG amplitude information extraction: The amplitude information of the frequency band of interest in the EEG signal is extracted through Hilbert transform. The calculation formula is: AEEG(t)=|Hilbert(EEG high (t))| (5) Among them, AEEG(t) represents the instantaneous amplitude of high-frequency EEG at time t; EEG high (t) represents the high-frequency EEG signal at time t; S33. Calculate the average amplitude in the phase. The calculation formula is: Where Z(t) represents the neurovascular coupling strength value at time t; It is a complex exponential function that combines blood flow phase information and EEG amplitude information; represents the instantaneous blood flow phase at time t; S34. Calculate the coupling value: Calculate the average phase and amplitude of the complex exponential function and take the modulus. The calculation formula is: Wherein, PAC represents the neurovascular coupling strength value; represents the averaging of the complex exponential function, and T represents the total number of time points.

8. The spinal cord stimulation prognosis prediction system for patients with impaired consciousness based on neurovascular coupling according to claim 1, characterized in that: The display module includes three parts: patient information, detailed results, and comparative predictions. The patient information part includes the patient's age, gender, medical history, coma time, Coma Recovery Scale score, and the patient's nursing level. In the detailed results section, you can view the detailed analysis results of the patient's single EEG data in different frequency bands; in the comparative prediction section, the patient's prognosis is predicted by comparing the data collected at different time points.

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