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

Abstract

This invention belongs to the interdisciplinary field of neuroengineering and sleep medicine, and provides a closed-loop transcranial electrical stimulation system and method based on the resonance regulation of cerebrospinal fluid and blood vessels. First, data related to blood vessel diameter and cerebrospinal fluid flow velocity are collected. Then, based on these data, the phase difference between the blood vessels and cerebrospinal fluid is obtained using a maximum independent variable point set function. The electrical stimulation frequency is determined based on this phase difference and a preset relationship between the phase difference and the stimulation frequency. Finally, transcranial electrical stimulation is performed according to the determined frequency. This method not only considers cerebrospinal fluid flow and vascular factors, but also achieves real-time dynamic determination and adjustment of the electrical stimulation frequency through the determined phase difference between the blood vessels and cerebrospinal fluid, improving the effectiveness of electrical stimulation and ensuring sleep quality under electrical stimulation.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of neuroengineering and sleep medicine, and particularly relates to a closed-loop transcranial electrical stimulation system and method based on the resonance regulation of cerebrospinal fluid and blood vessels. Background Technology

[0002] Transcranial alternating current stimulation (tACS) 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 nuclei, change neurotransmitter levels, affect brain electrical rhythms, and improve interbrain communication, thereby achieving the purpose of enhancing brain function and improving sleep quality.

[0003] Currently, when using transcranial alternating current stimulation systems to improve sleep quality, only the frequency of electroencephalograms (EEGs) is tracked, while the regulatory effects of cerebrospinal fluid (CSF) flow and blood vessels on sleep are ignored, resulting in poor effectiveness of electrical stimulation in improving sleep quality. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a closed-loop transcranial electrical stimulation system and method based on the resonance regulation of cerebrospinal fluid and blood vessels. The invention first collects data related to blood vessel diameter and cerebrospinal fluid flow velocity. Then, based on these data, and using a maximum value independent variable point set function, the phase difference between the blood vessels and the cerebrospinal fluid is obtained. The electrical stimulation frequency is determined based on this phase difference and a preset relationship between the phase difference and the stimulation frequency. Finally, transcranial electrical stimulation is performed according to the determined frequency. This method not only considers cerebrospinal fluid flow and vascular factors but also achieves real-time dynamic determination and adjustment of the electrical stimulation frequency through the determined phase difference between the blood vessels and the cerebrospinal fluid, improving the effectiveness of electrical stimulation and ensuring sleep quality under electrical stimulation.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0006] In a first aspect, the present invention provides a closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation, comprising:

[0007] The data acquisition module is configured to collect data related to blood vessel diameter and cerebrospinal fluid flow velocity.

[0008] The calculation module is configured to: obtain the phase difference between blood vessels and cerebrospinal fluid based on blood vessel diameter data and cerebrospinal fluid flow velocity data, using a maximum value independent variable point set function; and determine the electrical stimulation frequency based on the phase difference between blood vessels and cerebrospinal fluid, as well as the preset relationship between the phase difference and the stimulation frequency.

[0009] The electrical stimulation control module is configured to perform transcranial electrical stimulation according to a determined electrical stimulation frequency.

[0010] Furthermore, in the calculation module, the phase difference between blood vessels and cerebrospinal fluid... The calculation is as follows:

[0011] ;

[0012] in, The function is a point set function with maximum value for the independent variable; This is a function used to calculate the cross-correlation coefficient between two signals; Data related to blood vessel diameter Data related to cerebrospinal fluid flow velocity.

[0013] Furthermore, a correction coefficient is introduced into the calculation module. The corrected phase difference for:

[0014] ;

[0015] ;

[0016] in, , and These are preset coefficients; Local impedance of the skull; The instantaneous rate of change of arterial diameter. This represents the change in arterial diameter. The diameter of the artery before the change.

[0017] Furthermore, in the calculation module, when the brain oxygen saturation is less than a preset value, the correction coefficient... Reduce to the preset percentage.

[0018] Furthermore, in the calculation module, when the heart rate coefficient of variation is greater than a preset value, a correction coefficient is applied. Freeze the current value for a preset duration.

[0019] Furthermore, in the calculation module, the relationship between the phase difference and the stimulation frequency is as follows:

[0020] ;

[0021] in, The frequency of stimulation; These are preset parameters.

[0022] Furthermore, in the data acquisition module, the acquired blood vessel diameter is the superficial temporal artery pulsation signal.

[0023] Furthermore, the data acquisition module employs a 16-electrode ring array to reconstruct the skull impedance distribution in real time.

[0024] Furthermore, the electrode pair combination is optimized based on a genetic algorithm. Specifically, within a preset time period, 256 electrode configurations are evaluated, and the scheme with the maximum field strength focusing power is selected.

[0025] Secondly, the present invention also provides a closed-loop transcranial electrical stimulation method based on the resonance modulation of cerebrospinal fluid and blood vessels, using the closed-loop transcranial electrical stimulation system based on the resonance modulation of cerebrospinal fluid and blood vessels as described in the first aspect, comprising: collecting data related to blood vessel diameter and data related to cerebrospinal fluid flow velocity; obtaining the phase difference between blood vessels and cerebrospinal fluid based on the data related to blood vessel diameter and cerebrospinal fluid flow velocity, using a maximum value independent variable point set function; determining the electrical stimulation frequency based on the phase difference between blood vessels and cerebrospinal fluid, and a preset relationship between the phase difference and the stimulation frequency; and performing transcranial electrical stimulation according to the determined electrical stimulation frequency.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] 1. In this invention, firstly, data related to blood vessel diameter and cerebrospinal fluid (CSF) flow velocity are collected. Then, based on these data, the phase difference between blood vessels and CSF is obtained using a maximum value independent variable point set function. The electrical stimulation frequency is determined based on this phase difference and a preset relationship between the phase difference and the stimulation frequency. Finally, transcranial electrical stimulation is performed according to the determined frequency. During sleep, CSF is responsible for clearing metabolic waste from the brain, and its flow velocity is positively correlated with the depth of slow-wave sleep. Simultaneously, vascular pulsation drives CSF flow through the periodic expansion and contraction of the arterial walls. When the two are in phase synchronization, a resonance effect is formed, improving CSF circulation efficiency. This invention not only considers CSF flow and vascular factors but also achieves real-time dynamic determination and adjustment of the electrical stimulation frequency through the determined phase difference between blood vessels and CSF, improving the electrical stimulation effect and ensuring sleep quality under electrical stimulation.

[0028] 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 velocity. This makes the determined electrical stimulation frequency more consistent with the local skull impedance and the changes in arterial diameter, further improving the accuracy of electrical stimulation control. Specifically, a correction coefficient is introduced to dynamically correct the stimulation frequency by comprehensively considering skull impedance, the instantaneous rate of change of arterial diameter, and cerebrospinal fluid flow velocity. This ensures that the stimulation frequency accurately matches the current physiological state, ultimately generating the optimal stimulation frequency. This frequency can simultaneously enhance the synergistic effect of vascular pulsation and cerebrospinal fluid flow, promoting the maintenance of deep sleep. Attached Figure Description

[0029] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0030] Figure 1 This is a system framework diagram of Embodiment 1 of the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0033] Cerebrospinal fluid (CSF) is a colorless, transparent fluid found in the ventricular system (such as the lateral ventricles, third ventricle, and fourth ventricle) and subarachnoid space. Its functions include buffering mechanical shocks to brain tissue, transporting nutrients, and removing metabolic waste (such as β-amyloid protein). During sleep, CSF flows in a directed manner through the perivascular space to promote the removal of metabolic waste from the brain.

[0034] The blood vessels in this invention mainly refer to cerebral arteries, especially small and medium-sized arteries within 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, through the periodic expansion and contraction of the arterial walls (i.e., changes in vascular tension), can drive the flow of CSF in the spaces surrounding the blood vessels.

[0035] Example 1:

[0036] To address the issue that current transcranial alternating current (TCD) stimulation systems only focus on tracking brainwave frequencies, neglecting the regulatory effects of cerebrospinal fluid (CSF) flow and blood vessels on sleep, this embodiment provides a closed-loop TCD stimulation system based on CSF and vascular resonance modulation. This system dynamically adjusts stimulation parameters by real-time monitoring of the vascular pulsation-CSF phase relationship. The system includes:

[0037] The data acquisition module is configured to collect data related to blood vessel diameter and cerebrospinal fluid flow velocity.

[0038] The calculation module is configured to: obtain the phase difference between blood vessels and cerebrospinal fluid based on blood vessel diameter data and cerebrospinal fluid flow velocity data, using a maximum value independent variable point set function; and determine the electrical stimulation frequency based on the phase difference between blood vessels and cerebrospinal fluid, as well as the preset relationship between the phase difference and the stimulation frequency.

[0039] The electrical stimulation control module is configured to perform transcranial electrical stimulation according to a determined electrical stimulation frequency.

[0040] Specifically, this embodiment first collects data related to blood vessel diameter and cerebrospinal fluid flow velocity. Then, based on the blood vessel diameter and cerebrospinal fluid flow velocity data, the phase difference between the blood vessels and cerebrospinal fluid is obtained 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 according to the determined electrical stimulation frequency. This embodiment not only considers cerebrospinal fluid flow and vascular factors, but also achieves real-time dynamic determination and adjustment of the electrical stimulation frequency through the determined phase difference between the blood vessels and cerebrospinal fluid, improving the electrical stimulation effect and ensuring sleep quality under electrical stimulation.

[0041] In this embodiment, the data acquisition module employs, for example, a multimodal sensor array and a miniature Doppler unit; optionally, a 16-electrode ring array (8cm in diameter) is used to reconstruct the skull impedance distribution in real time, and a 60MHz ultrasound probe is used to monitor the superficial temporal artery pulsation (accuracy ±5μm). Optionally, vessel diameter monitoring primarily targets the superficial temporal artery (a vessel on the brain surface) and branches of the middle cerebral artery (small and medium-sized arteries within the brain parenchyma). The superficial temporal artery, due to its superficial location (located subcutaneously in the temporal region), can have its diameter dynamics measured in real time using a 60MHz ultrasound probe (accuracy ±5μm); while the diameter fluctuations of the branches of the middle cerebral artery are indirectly assessed using a multimodal sensor array, reflecting the pulsation characteristics of vessels within the brain parenchyma. Cerebrospinal fluid (CSF) flow velocity monitoring focuses on perivascular spaces (such as the Virchow-Robin space) and the fourth ventricle outlet region, where the mechanical coupling effect between CSF flow and vascular pulsation is most significant. The resonance efficiency (e.g., the ΔD / D0 ratio) can be quantified using a phase synchronization algorithm.

[0042] In this embodiment, the phase difference between blood vessels and cerebrospinal fluid is calculated in the calculation module. The calculation is as follows:

[0043] ;

[0044] in, The function is a point set function with maximum value for the independent variable; This is a function used to calculate the cross-correlation coefficient between two signals; This includes data related to blood vessel diameter, such as pulsation waveform data related to blood vessel diameter. This includes data related to cerebrospinal fluid flow velocity, such as cerebrospinal fluid flow velocity waveform data.

[0045] Optionally, the phase difference between blood vessels and cerebrospinal fluid (CSF) is calculated based on the pulsation waveform D(t) of the blood vessel diameter and the CSF 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 a resolution of 10 ms, exhibiting a sinusoidal fluctuation (amplitude 50-200 μm); the CSF flow velocity waveform reflects CSF flow in the fourth ventricle outlet region (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, to determine the time offset τ_max when the two signals have the maximum correlation; τ_max is extracted by 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).

[0046] Specifically, firstly, a 60MHz ultrasound probe (accuracy ±5μm) is used to acquire the pulsation signal D(t) of the blood vessel diameter in real time (frequency 0.8-1.2Hz, amplitude 50-200μm), while simultaneously recording the oscillation signal V(t) of the cerebrospinal fluid flow velocity (frequency 0.1-0.3Hz, amplitude 2-10mm / s) using a Doppler unit. Next, cross-correlation analysis is performed on the two signals: CrossCorr(D, V) = ΣD(t)·V(t+τ), and the time offset τ_max corresponding to the cross-correlation peak is determined using the maximum value independent variable point set function (argmax). Subsequently, based on the least common multiple T of the dominant frequency periods of the two signals (e.g., 1Hz of D and 0.2Hz of V correspond to T=5 seconds), the time offset is converted into a phase difference. Finally, combined with local skull impedance Instantaneous change rate of arterial diameter and cerebrospinal fluid flow rate By correction coefficient Dynamic correction of phase difference: This improves the spatiotemporal accuracy of electrical stimulation frequency control to within ±15°.

[0047] In the calculation module, a correction coefficient is introduced. The corrected phase difference for:

[0048] ;

[0049] ;

[0050] in, , and These are preset coefficients, which can be achieved through experiments or other means; they are optional. =0.15, which is the impedance adjustment weight, reflecting the amplification effect of the skull's conductivity on the phase difference. =0.3, which is the vasodilation sensitivity, controlling the contribution of transient arterial deformation to the phase difference; =0.2, velocity coupling factor, a nonlinear weight characterizing the influence of cerebrospinal fluid dynamics; Local impedance of the skull; The reference impedance can be set to 100 Ωm. The instantaneous change rate (%) of arterial diameter as measured by ultrasound. This represents the change in arterial diameter. The diameter of the artery before the change; The preset cerebrospinal fluid flow rate can be set to 10 mm / s.

[0051] Specifically, the phase difference is corrected by using local skull impedance, instantaneous rate of change of arterial diameter, and cerebrospinal fluid flow velocity, so that the determined electrical stimulation frequency is more consistent with the local skull impedance and the rate of change of arterial diameter, thereby further improving the accuracy of electrical stimulation control.

[0052] In the calculation module, the relationship between phase difference and stimulation frequency is as follows:

[0053] ;

[0054] in, The frequency of stimulation; This is a preset parameter; the optional value is 10.

[0055] In another embodiment, the electrode pair combination is optimized based on a genetic algorithm. Specifically, 256 electrode configurations are evaluated within 100ms to select the scheme with the maximum field strength focusing power.

[0056] In another embodiment, in the calculation module, when the brain oxygen saturation is less than a preset value, a correction coefficient is applied. The heart rate is reduced to a preset percentage. In the calculation module, when the heart rate coefficient of variation is greater than a preset value, a correction coefficient is applied. The current value is frozen for a preset duration. In the data acquisition module, the acquired vessel diameter is the superficial temporal artery pulsation signal. Coefficient , as well as Specifically, this can be determined through multiple regression analysis using animal experimental data. A dynamic boundary constraint mechanism is set, and the safety range for the correction coefficient is set to... When brain oxygen saturation hour, Automatic reduction of 30%, when heart rate variability coefficient is... hour, Freeze the current value for 60 seconds.

[0057] In another embodiment, a dual closed-loop system is constructed by combining genetic algorithms and metabolic monitoring techniques: a fast loop (10Hz update) dynamically adjusted according to the electrode configuration. Coefficients; specifically, the fast loop (10Hz update) refers to a high-frequency parameter adjustment mechanism based on dynamic changes in electrode impedance. This mechanism scans local skull impedance data every 100ms (i.e., 10Hz) using a 16-electrode ring array and employs a genetic algorithm to evaluate 256 electrode pair combinations (such as anode-cathode pairing) within 100ms, selecting the configuration with the highest field strength focusing (FOCUS) (calculation formula: Maximum field strength of the target brain region The root mean square of the electric field strength in the non-target region. Dynamic adjustment mainly includes: based on impedance deviation. Real-time correction coefficient: , The dimensions are Defined as the real-time value of the current local impedance of the skull. With reference impedance The difference, The preset difference value is 20. .exist The stimulation automatically switches to a 0.75Hz sine wave. The frequency is then switched to a 40Hz pulse train mode. On the hardware side, sub-millisecond response (delay) for electrode switching is achieved through an FPGA. The dynamic range of the stimulation current is 0.1-5mA (resolution 10). This ensures that the target field strength stability error is <±5% during impedance fluctuations. The fast loop works in conjunction with the slow loop (0.1Hz update) to improve phase synchronization stability from 78.9% to 82.3%.

[0058] In another embodiment, an effectiveness evaluation system is established: Phase Tracking Accuracy (PTA): calculated. The correlation coefficient between measured values ​​and model values; Energy regulation efficiency (ERE) = ( -1)× ×f, where Metabolic Co-existence Index (MCI) is the ratio of β-amyloid clearance efficiency per unit of stimulus energy. Metabolic Co-existence Index (MCI) is the ratio of β-amyloid clearance efficiency per unit stimulus energy; Metabolic Co-existence Index (MCI) = β-amyloid clearance rate / ( k (×stimulation energy), among which... k The metabolic energy conversion coefficient reflects the proportion of stimulus energy converted into metabolic clearance efficiency. Through dynamic configuration of 256 electrode topologies (response time < 50 ms) and multi-parameter monitoring (time resolution 10 ms), phase synchronization stability can be improved to 82.3% (4.3 percentage points higher than the original 78.9%). Simulation data shows that in the REM phase scenario, the corrected system can increase the spindle wave density by an additional 15% (p = 0.013) while maintaining constant stimulus energy consumption.

[0059] In another embodiment, dynamic impedance matching is performed; optionally, initial scan: a 0.1mA@1kHz test current is applied to construct a three-dimensional conductivity model of the skull; real-time adjustment: the electrode pair selection strategy is updated every 5 minutes; stimulation protocol: NREM (Non-Rapid Eye Movement) phase: 0.75Hz sine wave (peak 2mA), synchronized with the slow wave in negative phase; REM phase: 40Hz pulse train (pulse width 100μs, duty cycle 30%); effect verification (n=15): vascular-CSF phase synchronization improved 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.

[0060] In another embodiment, a new stimulation target was added: Electrode location: posterior border of the sternocleidomastoid muscle (jugular bulb projection area); Parameters: 1mA crossover frequency stimulation (0.1Hz carrier + 5Hz modulation). Synergistic effect: brain lactate clearance rate increased by 2.3 times (MRS detection), sleep spindle density increased by 41% (p=0.005). In another embodiment, near-infrared spectroscopy (690-850nm) was used to detect brain oxygen saturation in real time, and jugular vein auxiliary stimulation was activated when StO2 < 60%.

[0061] In this embodiment, polysomnography showed that the N3 phase was prolonged by 42±11 minutes; the morning β-amyloid protein level decreased by 29% (ELISA detection); the system field intensity 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.

[0062] The regulatory mechanism of cerebrospinal fluid-vascular resonance on sleep quality involves the spontaneous generation of ultraslow oscillations (CSF) of approximately 0.1 Hz during non-rapid eye movement (NREM) sleep, manifested as rhythmic dilation and contraction of blood vessels. This vascular dynamics enhances sleep quality through two mechanisms: mechanical coupling effect: vasodilation increases the volume of the perivascular space, creating a negative pressure gradient that propels CSF flow along the arteries into the brain parenchyma; contraction promotes CSF return through the interstitial spaces to the venous end. Phase synchronization optimization: when vascular pulsation and CSF flow are phase synchronized (i.e., ΔD≈0), a resonance effect is generated, increasing CSF flow velocity by 40%-60% and significantly enhancing the clearance efficiency of waste products such as β-amyloid. The system dynamically adjusts the transcranial electrical stimulation frequency to maintain the optimal phase difference between vascular oscillations and CSF flow (typically controlled within ±15°), thereby prolonging the duration of deep sleep and improving sleep structure.

[0063] Example 2:

[0064] This embodiment provides a closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation. Building upon Embodiment 1, a multidimensional modulation function is constructed, and a modulation coefficient matrix is ​​introduced into the frequency formula:

[0065]

[0066] in, It is an integer. =1, 2, 3, or 4; =1+0.15·(FOCUS-0.75), where FOCUS is the field intensity focusing power; =exp(-| |); =1+0.4·tanh(V / ), where V is the cerebrospinal fluid flow velocity; The speed is 7mm / s; =0.9+0.2·Sigmoid(Z / ), where Z is the four-dimensional impedance tomography value (dynamic three-dimensional impedance distribution). , is 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); The optional value is 50 Ω·m; weight Principal component analysis of animal experimental data was used to determine the field intensity focusing (FOCUS). FOCUS is a key indicator for evaluating the spatial localization accuracy of transcranial electrical stimulation, defined as the ratio of the maximum electric field intensity in the target brain region to the root mean square value of the field intensity in the non-target region.

[0067] In some embodiments, Z is normalized (Z / 50) and input to 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 adjustment range of K is ±0.2 (range 0.7-1.1), which matches the system hard boundary constraint (0.6≤K≤1.8) to ensure that the stimulation parameters are dynamically optimized within a safe range.

[0068] In some embodiments, a combination of genetic algorithms and metabolic feedback methods is used to update the electrode impedance changes every 20ms. , Adjusted every 5 seconds by β-amyloid clearance rate Weights; the regulatory function is reconstructed every 10 minutes using ELISA test data.

[0069] In some embodiments, dynamic safety constraints are applied, and a dual protection mechanism is set, including hard boundaries and soft constraints; under hard boundaries, 0.6 ≤ ≤1.8; Under soft constraints, when brain oxygen saturation When the coefficient matrix is ​​scaled by a factor of 0.7, and when the CSF pulsation entropy value is greater than 2.5, it is frozen. Parameter 30 seconds.

[0070] In some embodiments, the verification metrics are expanded, specifically:

[0071] ;

[0072] ;

[0073] ;

[0074] in, The actual coupling oscillation frequency is the instantaneous frequency of blood vessel-CSF coupling oscillation monitored in real time through a 256-electrode topology configuration. The target frequency can be selected from 1.0 to 0.3 Hz; This is a dynamic control coefficient. This reflects the focusing intensity of the field and the gain in energy conversion. .

[0075] By employing a dynamic topology configuration with 256 electrodes (response time <30ms) and multimodal monitoring (time resolution 5ms), the synchronization accuracy between the stimulation frequency and the blood vessel-CSF coupled oscillation (0.1-0.3Hz) can reach 92.4%. Experimental verification shows that in deep sleep scenarios, the improved system increases delta wave power density by 27% (p=0.008) while reducing energy consumption by 19%.

[0076] Example 3:

[0077] This embodiment provides a closed-loop transcranial electrical stimulation method based on the resonance modulation of cerebrospinal fluid and blood vessels. It uses a closed-loop transcranial electrical stimulation system based on the resonance modulation of cerebrospinal fluid and blood vessels as described in Embodiment 1 or Embodiment 2, comprising: acquiring data related to blood vessel diameter and cerebrospinal fluid flow velocity; obtaining the phase difference between blood vessels and cerebrospinal fluid using the blood vessel diameter and cerebrospinal fluid flow velocity data and a maximum value independent variable point set function; determining the electrical stimulation frequency based on the phase difference between blood vessels and cerebrospinal fluid, and a preset relationship between the phase difference and the stimulation frequency; and performing transcranial electrical stimulation according to the determined electrical stimulation frequency.

[0078] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation, characterized in that, include: The data acquisition module is configured to collect data related to blood vessel diameter and cerebrospinal fluid flow velocity. The calculation module is configured to: obtain the phase difference between blood vessels and cerebrospinal fluid based on blood vessel diameter data and cerebrospinal fluid flow velocity data, using a maximum value independent variable point set function; and determine the electrical stimulation frequency based on the phase difference between blood vessels and cerebrospinal fluid, as well as the 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; In the calculation module, the phase difference between blood vessels and cerebrospinal fluid... The calculation is as follows: ； in, The function is a point set function with maximum value for the independent variable; This is a function used to calculate the cross-correlation coefficient between two signals; Data related to blood vessel diameter; Data related to cerebrospinal fluid flow velocity; In the calculation module, a correction coefficient is introduced. The corrected phase difference for: ； ； in, , and These are preset coefficients; Local impedance of the skull; Preset impedance; The instantaneous rate of change of arterial diameter. This represents the change in arterial diameter. The diameter of the artery before the change; To preset the cerebrospinal fluid flow rate; In the calculation module, the relationship between phase difference and stimulation frequency is as follows: ； in, The frequency of stimulation; These are preset parameters.

2. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation as described in claim 1, characterized in that, In the calculation module, when the brain oxygen saturation is less than a preset value, the correction coefficient... The preset percentage for the reduction.

3. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation as described in claim 2, characterized in that, In the calculation module, when the heart rate coefficient of variation is greater than a preset value, a correction coefficient is applied. Freeze the current value for a preset duration.

4. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation as described in claim 1, characterized in that, The data acquisition module collects the blood vessel diameter as the pulsation signal of the superficial temporal artery.

5. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation as described in claim 1, characterized in that, The data acquisition module uses a 16-electrode ring array to reconstruct the skull impedance distribution in real time.

6. The closed-loop transcranial electrical stimulation system based on cerebrospinal fluid and vascular resonance modulation as described in claim 5, 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 scheme with the maximum field strength focusing power is selected.

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