Closed-loop transcranial electrical stimulation system and method based on cerebrospinal fluid and vascular resonance regulation

Abstract

The invention belongs to the technical field of crossing of neural engineering and sleep medicine, and provides a closed-loop transcranial electrical stimulation system and method based on cerebrospinal fluid and vascular resonance regulation, and the method comprises the steps: firstly collecting related data of the diameter of a blood vessel and related data of the flow velocity of the cerebrospinal fluid; then, according to the related data of the blood vessel diameter and the related data of the cerebrospinal fluid flow velocity, the phase difference between the blood vessel and the cerebrospinal fluid is obtained by means of a maximum independent variable point set function; determining an electrical stimulation frequency according to the phase difference between the blood vessel and the cerebrospinal fluid and a preset relationship between the phase difference and the stimulation frequency; and finally, performing transcranial electrical stimulation according to the determined electrical stimulation frequency. According to the invention, cerebrospinal fluid flow and blood vessel factors are considered, and real-time dynamic determination and adjustment of the electrical stimulation frequency are realized through the determined phase difference between the blood vessel and the cerebrospinal fluid, so that the electrical stimulation effect is improved, and the sleep quality under electrical stimulation is ensured.

Description

Technical Field

[0001] The present invention belongs to the interdisciplinary field of neuroengineering and sleep medicine, and in particular relates to a closed-loop transcranial electrical stimulation system and method based on cerebrospinal fluid and vascular resonance regulation. Background Art

[0002] The transcranial alternating current stimulation (tACS) system is a non-invasive transcranial electrical stimulation system. With the help of transcranial alternating current stimulation, it can directly and effectively stimulate most areas of the brain, as well as all deep brain nerve nuclei, change neurotransmitter levels, affect EEG rhythms, and improve connections between brain areas, thereby achieving the goals of enhancing brain function and improving sleep quality.

[0003] Currently, when using transcranial alternating current stimulation systems to improve sleep quality, they only focus on tracking the frequency of brain waves (Electroencephalogram, EEG), ignoring the regulatory effects of cerebrospinal fluid (CSF) flow and blood vessels on sleep, resulting in poor effect of electrical stimulation in improving sleep quality. Summary of the Invention

[0004] To address the above-mentioned issues, the present invention proposes a closed-loop transcranial electrical stimulation system and method based on cerebrospinal fluid and blood vessel resonance regulation. The system first collects data related to blood vessel diameter and cerebrospinal fluid flow rate. Then, based on the data, the system uses a maximum independent variable point set function to determine the phase difference between the blood vessels and the cerebrospinal fluid. The system then determines the electrical stimulation frequency based on the phase difference between the blood vessels and the cerebrospinal fluid and the preset relationship between the phase difference and the stimulation frequency. Finally, transcranial electrical stimulation is performed based on the determined electrical stimulation frequency. This system not only takes into account cerebrospinal fluid flow and blood vessel factors, but also, by determining the phase difference between the blood vessels and the cerebrospinal fluid, enables real-time dynamic determination and adjustment of the electrical stimulation frequency, improving the electrical stimulation effect and ensuring sleep quality under electrical stimulation.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation, comprising: The data acquisition module is configured to: acquire data related to blood vessel diameter and data related to cerebrospinal fluid flow rate; The calculation module is configured to: obtain a phase difference between the blood vessel and the cerebrospinal fluid based on the data related to the blood vessel diameter and the cerebrospinal fluid flow rate using a maximum independent variable point set function; and determine an electrical stimulation frequency based on the phase difference between the blood vessel and the cerebrospinal fluid and a preset relationship between the phase difference and the stimulation frequency; The electrical stimulation control module is configured to perform transcranial electrical stimulation according to a determined electrical stimulation frequency.

[0006] Furthermore, in the calculation module, the phase difference between blood vessels and cerebrospinal fluid Calculated as: ; in, is the maximum independent variable point set function; is a function used to calculate the cross-correlation coefficient between two signals; Data related to blood vessel diameter The data are related to cerebrospinal fluid flow rate.

[0007] Furthermore, in the calculation module, a correction coefficient is introduced , then the corrected phase difference for: ; ; in, 、 and is the preset coefficient; is the local impedance of the skull; is the instantaneous rate of change of arterial diameter, is the change in arterial diameter; is the artery diameter before the change.

[0008] Furthermore, in the calculation module, when the brain oxygen saturation is less than the preset value, the correction coefficient to a preset percentage.

[0009] Furthermore, in the calculation module, when the heart rate variation coefficient is greater than the preset variation value, the correction coefficient Freeze the current value for a preset duration.

[0010] Furthermore, in the calculation module, the relationship between the phase difference and the stimulation frequency is: ; in, is the stimulation frequency; are preset parameters.

[0011] Furthermore, in the data acquisition module, the blood vessel diameter collected is the pulsation signal of the superficial temporal artery.

[0012] Furthermore, in the data acquisition module, a 16-electrode ring array is used to reconstruct the skull impedance distribution in real time.

[0013] Furthermore, the electrode pair combination is optimized based on a genetic algorithm. Specifically, within a preset time, 256 electrode configurations are evaluated and the scheme with the maximum field intensity focusing is selected.

[0014] In a second aspect, the present invention also provides a closed-loop transcranial electrical stimulation method based on cerebrospinal fluid and blood vessel resonance regulation, using the closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and blood vessel resonance regulation as described in the first aspect, including: collecting data related to blood vessel diameter and data related to cerebrospinal fluid flow rate; obtaining the phase difference between the blood vessels and the cerebrospinal fluid based on the data related to blood vessel diameter and the data related to cerebrospinal fluid flow rate with the help of the maximum independent variable point set function; determining the electrical stimulation frequency based on the phase difference between the blood vessels and the cerebrospinal fluid, and the preset relationship between the phase difference and the stimulation frequency; and performing transcranial electrical stimulation according to the determined electrical stimulation frequency.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, first, data related to blood vessel diameter and cerebrospinal fluid flow rate are collected; then, based on the data related to blood vessel diameter and cerebrospinal fluid flow rate, the phase difference between the blood vessels and the cerebrospinal fluid is obtained with the help of the maximum independent variable point set function; based on the phase difference and the preset relationship between the phase difference and the stimulation frequency, the electrical stimulation frequency is determined; finally, transcranial electrical stimulation is performed according to the determined electrical stimulation frequency; the cerebrospinal fluid is responsible for clearing metabolic waste from the brain during sleep, and its flow rate is positively correlated with the depth of slow-wave sleep. At the same time, blood vessel pulsation drives the flow of cerebrospinal fluid through the periodic expansion and contraction of the arterial wall. When the two phases are synchronized, a resonance effect can be formed, thereby improving the efficiency of cerebrospinal fluid circulation; the present invention not only takes into account the factors of cerebrospinal fluid flow and blood vessels, but also realizes the real-time dynamic determination and adjustment of the electrical stimulation frequency by determining the phase difference between the blood vessels and the cerebrospinal fluid, thereby improving the electrical stimulation effect and ensuring the sleep quality under electrical stimulation.

[0016] 2. In this invention, the phase difference is corrected using local skull impedance, the instantaneous rate of change of arterial diameter, and cerebrospinal fluid flow rate. This ensures that the determined electrical stimulation frequency is more consistent with the local skull impedance and changes in arterial diameter, further improving the accuracy of electrical stimulation control. Specifically, a correction factor is introduced to dynamically correct the stimulation frequency based on the skull impedance, the instantaneous rate of change of arterial diameter, and cerebrospinal fluid flow rate, ensuring that the stimulation frequency accurately matches the current physiological state, ultimately generating an optimal stimulation frequency that simultaneously enhances the synergistic effect of vascular pulsation and cerebrospinal fluid flow, promoting the maintenance of deep sleep. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0018] Figure 1 This is a system framework diagram of Example 1 of the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0021] Cerebrospinal fluid (CSF) is a colorless, transparent liquid present in the ventricular system (such as the lateral ventricles, third ventricle, and fourth ventricle) and the subarachnoid space. Its functions include buffering mechanical impact on brain tissue, transporting nutrients, and removing metabolic waste (such as β-amyloid protein). During sleep, CSF forms a directional flow through the perivascular space, promoting the removal of metabolic waste in the brain.

[0022] The blood vessels in the present invention mainly refer to cerebral arteries, especially small and medium-sized arteries in the brain parenchyma (such as branches of the middle cerebral artery) and blood vessels on the surface of the brain (such as the superficial temporal artery). The pulsation of these blood vessels can drive the flow of CSF in the spaces around the blood vessels through the periodic expansion and contraction of the arterial wall (i.e., changes in vascular tension).

[0023] Example 1: To address the problem that current transcranial alternating current stimulation systems only focus on tracking brainwave frequency and ignore the regulatory effects of cerebrospinal fluid (CSF) flow and blood vessels on sleep, this embodiment provides a closed-loop transcranial electrical stimulation system based on CSF and vascular resonance regulation. By real-time monitoring of the vascular pulsation-CSF phase relationship, it dynamically adjusts electrical stimulation parameters. The system includes: The data acquisition module is configured to: acquire data related to blood vessel diameter and data related to cerebrospinal fluid flow rate; The calculation module is configured to: obtain a phase difference between the blood vessel and the cerebrospinal fluid based on the data related to the blood vessel diameter and the cerebrospinal fluid flow rate using a maximum independent variable point set function; and determine an electrical stimulation frequency based on the phase difference between the blood vessel and the cerebrospinal fluid and a preset relationship between the phase difference and the stimulation frequency; The electrical stimulation control module is configured to perform transcranial electrical stimulation according to a determined electrical stimulation frequency.

[0024] Specifically, this embodiment first collects data related to blood vessel diameter and cerebrospinal fluid flow rate. Then, based on the blood vessel diameter and cerebrospinal fluid flow rate data, the phase difference between the blood vessels and the cerebrospinal fluid is determined using a maximum independent variable point set function. The electrical stimulation frequency is determined based on the phase difference and a preset relationship between the phase difference and the stimulation frequency. Finally, transcranial electrical stimulation is performed based on the determined electrical stimulation frequency. This embodiment not only takes into account cerebrospinal fluid flow and blood vessel factors, but also, by determining the phase difference between the blood vessels and the cerebrospinal fluid, enables real-time dynamic determination and adjustment of the electrical stimulation frequency, improving the electrical stimulation effect and ensuring sleep quality under electrical stimulation.

[0025] In this embodiment, the data acquisition module, for example, utilizes a multimodal sensor array and a micro-Doppler unit. Optionally, a 16-electrode annular array (8 cm diameter) is used to reconstruct skull impedance distribution in real time, and a 60 MHz ultrasound probe is used to monitor superficial temporal artery pulsation (with an accuracy of ±5 μm). Optionally, vessel diameter monitoring primarily targets the superficial temporal artery (a surface vessel of the brain) and the branches of the middle cerebral artery (small and medium-sized arteries within the brain parenchyma). Due to its superficial location (under the temporal skin), the dynamic changes in the diameter of the superficial temporal artery can be measured in real time with an 60 MHz ultrasound probe (with an accuracy of ±5 μm). Diameter fluctuations of the branches of the middle cerebral artery are indirectly assessed using a multimodal sensor array, reflecting the pulsation characteristics of the vessels within the brain parenchyma. Cerebrospinal fluid (CSF) flow velocity is monitored in perivascular spaces (such as the Virchow-Robin space) and the fourth ventricle outlet region, where the mechanical coupling between CSF flow and vascular pulsation is most pronounced. Phase synchronization algorithms can be used to quantify the resonance efficiency (e.g., the ΔD / D0 ratio) between the two.

[0026] In this embodiment, in the calculation module, the phase difference between blood vessels and cerebrospinal fluid is Calculated as: ; in, is the maximum independent variable point set function; is a function used to calculate the cross-correlation coefficient between two signals; Data related to blood vessel diameter, such as blood vessel diameter pulsation waveform data; The data is related to cerebrospinal fluid flow rate, such as cerebrospinal fluid flow rate waveform data.

[0027] Optionally, the phase difference between the blood vessels and the cerebrospinal fluid is calculated based on the blood vessel diameter pulsation waveform D(t) and the cerebrospinal fluid flow velocity waveform V(t). Optionally, the blood vessel diameter waveform originates from the mechanical pulsation of the superficial temporal artery (frequency 0.8-1.2 Hz), recorded with a 60 MHz ultrasound probe at 10 ms resolution, and exhibits quasi-sinusoidal fluctuations (amplitude 50-200 μm); the cerebrospinal fluid flow velocity waveform reflects CSF flow in the outlet area of ​​the fourth ventricle (frequency 0.1-0.3 Hz), measured by Doppler ultrasound or MRI, and exhibits low-frequency oscillations (amplitude 2-10 mm / s). Cross-correlation calculation is performed on D(t) and V(t): CrossCorr(τ)=∫D(t)·V(t+τ)dt, and the time offset τ_max when the two signals have maximum correlation is determined; τ_max is extracted through the argmax function and converted into a phase difference Δφ=2π·τ_max / T (T is the least common multiple of the signal period). For example, when τ_max=0.3s and T=5s, Δφ≈21.6°; combined with the skull impedance (Z) and the rate of change of arterial diameter (ΔD / D), the phase difference is dynamically adjusted using the correction coefficient k=1+α(Z / 100)+β(ΔD / D)+γV, and finally mapped to the electrical stimulation frequency f=θ(1-ΔP_corrected / 2π).

[0028] Specifically, first, a 60MHz ultrasound probe (accuracy ±5μm) was used to collect the blood vessel diameter pulsation signal D(t) (frequency 0.8-1.2Hz, amplitude 50-200μm) in real time, and the cerebrospinal fluid flow velocity oscillation signal V(t) (frequency 0.1-0.3Hz, amplitude 2-10mm / s) was recorded by a Doppler unit. Then, the two signals were cross-correlated: CrossCorr(D, V) = ΣD(t)·V(t+τ), and the time offset τ_max corresponding to the cross-correlation peak was determined by the maximum independent variable point set function (argmax). Subsequently, the time offset was converted into a phase difference based on the least common multiple T of the main frequency periods of the two signals (for example, 1Hz of D and 0.2Hz of V correspond to T = 5 seconds). Finally, combined with the local impedance of the skull , instantaneous rate of change of arterial diameter and cerebrospinal fluid flow rate , through the correction factor Dynamic correction of phase difference: , improving the spatiotemporal accuracy of electrical stimulation frequency control to within ±15°.

[0029] In the calculation module, a correction coefficient is introduced , then the corrected phase difference for: ; ; in, 、 and is a preset coefficient, which can be achieved through experiments and other means. =0.15, is the impedance adjustment weight, reflecting the amplification effect of the skull conductivity on the phase difference, =0.3, which is the sensitivity of vascular dilation and controls the contribution of instantaneous arterial deformation to the phase difference; =0.2, velocity coupling factor, representing the nonlinear weight of the influence of cerebrospinal fluid dynamics; is the local impedance of the skull; is the reference impedance, which can be selected as 100Ωm; is the instantaneous change rate of arterial diameter measured by ultrasound (%), is the change in arterial diameter; is the arterial diameter before the change; To preset the cerebrospinal fluid flow rate, the optional value is 10 mm / s.

[0030] Specifically, the phase difference is corrected using the local impedance of the skull, the instantaneous rate of change of the arterial diameter, and the cerebrospinal fluid flow rate, so that the determined electrical stimulation frequency is more consistent with the local impedance of the skull and more in line with the changes in the arterial diameter, further improving the accuracy of electrical stimulation control.

[0031] In the calculation module, the relationship between the phase difference and the stimulation frequency is: ; in, is the stimulation frequency; It is a preset parameter, and the optional value is 10.

[0032] In another embodiment, the electrode pair combination is optimized based on a genetic algorithm. Specifically, 256 electrode configurations are evaluated within 100 ms, and the maximum field intensity focusing scheme is selected.

[0033] In another embodiment, in the calculation module, when the brain oxygen saturation is less than a preset value, the correction coefficient In the calculation module, when the heart rate variation coefficient is greater than the preset variation value, the correction coefficient Freeze the current value for a preset time. In the data acquisition module, the blood vessel diameter collected is the pulsation signal of the superficial temporal artery. 、 as well as , the specific can be determined by multivariate regression of animal experimental data. Set the dynamic boundary constraint mechanism and set the correction coefficient safety range to When brain oxygen saturation hour, Automatically reduce by 30%, when the heart rate variation coefficient hour, Freeze the current value for 60 seconds.

[0034] In another embodiment, a dual closed-loop system is constructed by combining genetic algorithms with metabolic monitoring technologies: Fast loop (10Hz update): Dynamic adjustment based on electrode configuration coefficients; specifically, the fast loop (10Hz update) refers to a high-frequency parameter adjustment mechanism based on the dynamic changes in electrode impedance. This mechanism scans the local impedance data of the skull through a 16-electrode ring array every 100ms (i.e., 10Hz), and uses a genetic algorithm to evaluate 256 electrode pair combinations (such as anode-cathode pairing) within 100ms, selecting the configuration with the highest field intensity focus (FOCUS) (calculation formula: Maximum field strength in the target brain area The dynamic adjustment mainly includes: Real-time correction coefficient: , The dimension of , defined as the current real-time value of the local skull impedance With reference impedance The difference, The default difference is 20. .exist The stimulation is automatically switched to 0.75Hz sine wave. The hardware is realized by FPGA to achieve sub-millisecond response (delay) of electrode switching. ), the dynamic range of stimulation current is 0.1-5mA (resolution 10 ), ensuring target field strength stability error of less than ±5% during impedance fluctuations. This fast loop works in conjunction with the slow loop (0.1Hz update), improving phase synchronization stability from 78.9% to 82.3%.

[0035] In another embodiment, an effect evaluation system is established: Phase Tracking Accuracy (PTA): Calculation Correlation coefficient between measured value and model value; Energy regulation efficiency (ERE) = ( -1)× ×f, where is the metabolic cooperation index (MCI), which is the ratio of β-amyloid clearance efficiency per unit stimulation energy; The metabolic synergy index (MCI) is the ratio of the β-amyloid protein clearance efficiency under unit stimulation energy; the metabolic synergy index (MCI) = β-amyloid protein clearance rate / ( k × stimulus energy), where k The metabolic energy conversion coefficient reflects the efficiency of converting stimulus energy into metabolic clearance. By dynamically configuring 256 electrode topologies (with a response time <50ms) and monitoring multiple parameters (with a temporal resolution of 10ms), phase synchronization stability was improved to 82.3% (a 4.3 percentage point increase from the original 78.9%). Simulation data showed that in REM sleep, the modified system increased spindle density by an additional 15% (p=0.013) while maintaining constant stimulus energy consumption.

[0036] In another embodiment, dynamic impedance matching is performed; optionally, initial scan: applying a 0.1mA@1kHz test current to construct a three-dimensional conductivity model of the skull; real-time adjustment: updating the electrode pair selection strategy every 5 minutes; stimulation protocol: NREM (Non-Rapid Eye Movement) period: 0.75Hz sine wave (peak 2mA), synchronized with the negative phase of the slow wave; REM period: 40Hz pulse train (pulse width 100μs, duty cycle 30%); effect verification (n=15): vascular-CSF phase synchronization increased from 32.4% to 78.9% (p<0.001); the number of awakenings decreased from 5.2±1.1 times / night to 1.7±0.6 times.

[0037] In another embodiment, a new stimulation target was added: electrode location: posterior border of the sternocleidomastoid muscle (jugular bulb projection area); parameters: 1 mA cross-frequency stimulation (0.1 Hz carrier + 5 Hz modulation). Synergistic effects: cerebral lactate clearance increased by 2.3 times (as measured by MRS) and sleep spindle density increased by 41% (p = 0.005). In another embodiment, near-infrared spectroscopy (690-850 nm) was used to monitor cerebral oxygen saturation in real time, and auxiliary jugular vein stimulation was activated when StO2 < 60%.

[0038] In this embodiment, polysomnography showed that the N3 stage was extended by 42±11 minutes; the level of β-amyloid protein in the morning was reduced by 29% (ELISA test); the system field strength focusing degree reached 78.3%, which is 2.1 times higher than that of traditional tDCS; this embodiment can achieve a dual improvement in sleep quality and brain metabolic clearance by revealing the vascular-CSF coupling dynamics.

[0039] Cerebrospinal fluid-vascular resonance regulates sleep quality. During non-rapid eye movement (NREM) sleep, cerebral blood vessels spontaneously generate ultraslow oscillations (infraslow oscillations) of approximately 0.1 Hz, manifesting as rhythmic dilation and constriction of the blood vessels. This vascular dynamics enhance sleep quality through two mechanisms: mechanical coupling: Vascular dilation increases the volume of the perivascular space, creating a negative pressure gradient that propels CSF flow along arteries into the brain parenchyma; vascular constriction promotes CSF return through the interstitial spaces of the brain to the venous terminals. Phase synchronization optimization: When vascular pulsation and CSF flow achieve phase synchronization (i.e., ΔD ≈ 0), a resonance effect occurs, increasing CSF flow by 40%-60%, significantly enhancing the clearance of waste products such as beta-amyloid. The system dynamically adjusts the transcranial electrical stimulation frequency to maintain an optimal phase difference between vascular oscillations and CSF flow (typically controlled within ±15°), thereby prolonging deep sleep duration and improving sleep architecture.

[0040] Example 2: This embodiment provides a closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation. Based on Example 1, a multidimensional regulation function is constructed, and a regulation coefficient matrix is ​​introduced into the frequency formula:

[0041] in, is an integer, =1, 2, 3, or 4; =1+0.15·(FOCUS-0.75), where FOCUS is the field intensity focusing degree; =exp(-| |); =1+0.4·tanh(V / ), where V is the cerebrospinal fluid velocity; The optional speed is 7mm / s; =0.9+0.2·Sigmoid(Z / ), where Z is the four-dimensional impedance tomography value (dynamic three-dimensional impedance distribution), , defined as a dynamic dataset consisting of the dynamic three-dimensional impedance distribution (XYZ spatial coordinates) of the skull region reconstructed in real time by a 16-electrode ring array and superimposed with the time dimension (updated every 100ms, corresponding to a 10Hz sampling rate); Optional is 50Ω·m; weight The results were determined through principal component analysis of animal experimental data. Field focus (FOCUS) is a key indicator for evaluating the spatial positioning accuracy of transcranial electrical stimulation and is defined as the ratio of the maximum electric field intensity in the target brain area to the root mean square value of the field intensity in the non-target area.

[0042] In some embodiments, Z is normalized (Z / 50) and input into the Sigmoid function to calculate the K coefficient: K=0.9+0.2·Sigmoid(Z / 50). When the Z value fluctuates by ±50Ω·m, the K adjustment amplitude is ±0.2 (range 0.7-1.1), which matches the system hard boundary constraints (0.6≤K≤1.8), ensuring that the stimulation parameters are dynamically optimized within a safe range.

[0043] In some embodiments, a genetic algorithm and metabolic feedback method are combined to update the electrode impedance every 20ms. 、 ; every 5 seconds adjusted by β-amyloid clearance Weight; the regulatory function was reconstructed using ELISA test data every 10 minutes.

[0044] In some embodiments, dynamic security constraints are implemented and a dual protection mechanism is set, including hard boundaries and soft constraints; under hard boundaries, 0.6≤ ≤1.8; Under soft restraint, when brain oxygen saturation When , the coefficient matrix is ​​scaled by 0.7 times as a whole. When the CSF pulsation entropy value is greater than 2.5, the freezing Parameter 30 seconds.

[0045] In some embodiments, verification indicator expansion is performed, specifically: ; ; ; in, is the actual coupling oscillation frequency, the instantaneous frequency of vascular-CSF coupling oscillation monitored in real time by the 256-electrode topology configuration; The target frequency can be adjusted from 1.0 to 0.3 Hz. is the dynamic control coefficient, , reflecting the field intensity focusing degree and the gain of energy conversion, .

[0046] Through a 256-electrode dynamic topology configuration (response time <30ms) and multimodal monitoring (temporal resolution 5ms), the stimulation frequency can be synchronized with vascular-CSF coupled oscillations (0.1-0.3Hz) with an accuracy of 92.4%. Experimental verification showed that during deep sleep, the improved system increased delta wave power density by 27% (p=0.008) while reducing energy consumption by 19%.

[0047] Example 3: This embodiment provides a closed-loop transcranial electrical stimulation method based on cerebrospinal fluid and blood vessel resonance regulation, using a closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and blood vessel resonance regulation as described in Example 1 or Example 2, including: collecting data related to blood vessel diameter and data related to cerebrospinal fluid flow rate; obtaining the phase difference between the blood vessels and the cerebrospinal fluid based on the data related to blood vessel diameter and the data related to cerebrospinal fluid flow rate with the help of a maximum independent variable point set function; determining the electrical stimulation frequency based on the phase difference between the blood vessels and the cerebrospinal fluid and the preset relationship between the phase difference and the stimulation frequency; and performing transcranial electrical stimulation according to the determined electrical stimulation frequency.

[0048] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation, characterized in that: include: The data acquisition module is configured to: acquire data related to blood vessel diameter and data related to cerebrospinal fluid flow rate; The calculation module is configured to: obtain a phase difference between the blood vessel and the cerebrospinal fluid based on the blood vessel diameter data and the cerebrospinal fluid flow rate data using a maximum independent variable point set function; and determine an electrical stimulation frequency based on the phase difference between the blood vessel and the cerebrospinal fluid and a preset relationship between the phase difference and the stimulation frequency; The electrical stimulation control module is configured to perform transcranial electrical stimulation according to a determined electrical stimulation frequency.

2. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 1, characterized in that: In the calculation module, the phase difference between blood vessels and cerebrospinal fluid Calculated as: ； in, is the maximum independent variable point set function; is a function used to calculate the cross-correlation coefficient between two signals; Data related to blood vessel diameter; The data are related to cerebrospinal fluid flow rate.

3. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 2, characterized in that: In the calculation module, a correction coefficient is introduced , then the corrected phase difference for: ； ； in, 、 and is the preset coefficient; is the local impedance of the skull; is the preset impedance; is the instantaneous rate of change of arterial diameter, is the change in arterial diameter; is the arterial diameter before the change; The preset cerebrospinal fluid flow rate.

4. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 3, characterized in that: In the calculation module, when the brain oxygen saturation is less than the preset value, the correction coefficient Decrease by a preset percentage.

5. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 4, characterized in that: In the calculation module, when the heart rate variation coefficient is greater than the preset variation value, the correction coefficient Freeze the current value for a preset duration.

6. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 5, characterized in that: In the calculation module, the relationship between the phase difference and the stimulation frequency is: ； in, is the stimulation frequency; are preset parameters.

7. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 1, characterized in that: In the data acquisition module, the collected blood vessel diameter is the pulsation signal of the superficial temporal artery.

8. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 1, characterized in that: In the data acquisition module, a 16-electrode ring array is used to reconstruct the skull impedance distribution in real time.

9. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance regulation according to claim 8, characterized in that: The electrode pair combination is optimized based on a genetic algorithm. Specifically, within a preset time, 256 electrode configurations are evaluated and the maximum field intensity focusing solution is selected.

10. A closed-loop transcranial electrical stimulation method based on cerebrospinal fluid and vascular resonance regulation, characterized in that: A closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and blood vessel resonance regulation as described in any one of claims 1 to 9 is used, comprising: collecting data related to blood vessel diameter and data related to cerebrospinal fluid flow rate; obtaining the phase difference between the blood vessels and the cerebrospinal fluid based on the data related to blood vessel diameter and the data related to cerebrospinal fluid flow rate with the help of a maximum independent variable point set function; determining the electrical stimulation frequency based on the phase difference between the blood vessels and the cerebrospinal fluid and the preset relationship between the phase difference and the stimulation frequency; and performing transcranial electrical stimulation according to the determined electrical stimulation frequency.

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