Alzheimer's disease electrical stimulation system based on cerebrospinal fluid rhythm

Abstract

The invention belongs to the technical field of nerve regulation and control engineering, and provides a cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system, which is characterized in that first frequency band range data and second frequency band range data in brain wave signals are coupled to obtain cerebrospinal fluid rhythm data; then, calculating the phase difference between the first frequency band range data and the cerebrospinal fluid rhythm data; finally, the stimulation frequency is dynamically controlled according to the phase difference, and the stimulation current is dynamically controlled according to the brain tissue water volume fraction. The problem that specific metabolism requirements of non-fast eye movement sleep and fast eye movement sleep cycles cannot be matched by adopting single-frequency-band stimulation is solved, dynamic changes of post-traumatic encephaledema and requirements and influences of cerebrospinal fluid rhythm on current control are considered, the real-time adaptive capacity to the dynamic changes of the post-traumatic encephaledema is improved, and the application prospect is wide. And the treatment effect of the Alzheimer's disease is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of treatment of neurodegenerative diseases, and particularly relates to an Alzheimer's disease electric stimulation system based on cerebrospinal fluid rhythm. BACKGROUND

[0002] Transcranial electric stimulation physical therapy has the advantages of small operation difficulty and good safety, and has been used in the treatment of Alzheimer's disease, sleep disorders and neuropsychiatric diseases. Transcranial electric stimulation forms a half-current loop by applying a bidirectional micro-current to the cerebral cortex. The flow direction of the current is from the anode to the cathode. This current will cause changes in neuronal electrical activity.

[0003] However, the existing transcranial electric stimulation system adopts a fixed parameter mode (frequency 0.5-2Hz, current 1-2mA), which cannot respond to the characteristic abnormality that the theta-gamma coupling strength of Alzheimer's disease (AD) patients is reduced by 30%-50%, and the phase difference between the open-loop control mechanism and the cerebrospinal fluid pulsation rhythm fluctuates by ±0.8π. Traditional single-frequency stimulation (such as 40Hz gamma wave) fails to synchronize with the 0.1-0.01Hz ultra-slow wave rhythm required by the lymphoid system to remove beta amyloid protein, resulting in a 40%-60% reduction in cerebrospinal fluid-interstitial fluid exchange rate in AD model. At the same time, the existing system ignores the key feature that the blood-brain barrier permeability increases by 2-3 times in AD pathology, resulting in a 35%-50% error in stimulation current density (P>0.8 mL / (min·g) at the time), and lacks dynamic feedback on the cerebrospinal fluid Aβ42 / P-tau181 ratio (diagnostic specificity >90%), resulting in a mismatch between stimulation intensity and pathological staging. In addition, the verification scheme based on the trauma model has a structural bias of 28% in hippocampal capillary density compared with the 5xFAD transgenic model specific to AD, and the amplitude of cerebrospinal fluid pulsation in AD patients at night is reduced by 57±12%, with rhythm disorder (positively correlated with beta amyloid deposition r=0.72), further highlighting the inadequacy of the existing technology in regulating the lymphoid clearance mechanism. SUMMARY

[0004] The application proposes an Alzheimer's disease electric stimulation system based on cerebrospinal fluid rhythm to solve the above problems, first coupling first frequency range data and second frequency range data in the brain wave signal to obtain cerebrospinal fluid rhythm data; then, calculating the phase difference between the first frequency range data and the cerebrospinal fluid rhythm data; finally, dynamically controlling the stimulation frequency according to the phase difference, and dynamically controlling the stimulation current according to the brain tissue water volume fraction, solving the problem that single frequency stimulation cannot match the specific metabolic needs of non-rapid eye movement sleep and rapid eye movement sleep cycles, considering the dynamic changes of post-traumatic brain edema and the needs and influences of cerebrospinal fluid rhythm on current control, improving the real-time adaptive ability to the dynamic changes of post-traumatic brain edema, and ensuring the treatment effect of Alzheimer's disease.

[0005] In order to achieve the above purpose, the application provides an Alzheimer's disease electric stimulation system based on cerebrospinal fluid rhythm, which adopts the following technical scheme: An Alzheimer's disease electric stimulation system based on cerebrospinal fluid rhythm, comprising: A data acquisition module configured to acquire brain wave signals of an Alzheimer's disease patient and brain tissue water volume fraction; A decoupling-recombination module configured to determine first frequency range data and second frequency range data in the brain wave signal; coupling the first frequency range data and the second frequency range data to obtain cerebrospinal fluid rhythm data; the frequency of the first frequency range data is lower than the frequency of the second frequency range data; A calculation module configured to calculate the phase difference between the first frequency range data and the cerebrospinal fluid rhythm data; A control module configured to dynamically control the stimulation frequency according to the phase difference, and dynamically control the stimulation current according to the brain tissue water volume fraction.

[0006] Further, the brain tissue water volume fraction is obtained in real time based on the impedance imaging technology of materials.

[0007] Further, the cerebrospinal fluid rhythm data is: ; Wherein, is the cerebrospinal fluid rhythm data; is the first frequency range data; is the second frequency range data; and is a preset coefficient.

[0008] Further, the frequency range of the first frequency range data is 0.5Hz~4Hz, and the frequency range of the second frequency range data is 12Hz~16Hz.

[0009] Further, the stimulation frequency is: ; wherein, is the adjusted stimulation frequency; is the initial stimulation frequency; is the phase difference between the first frequency range data and the cerebrospinal fluid rhythm data.

[0010] Further, the stimulation current is: ; wherein, is the adjusted stimulation current; is the initial current; is the brain tissue water volume fraction.

[0011] Further, during the electrical stimulation, stimulation to the lateral sinus is increased to enhance the cerebrospinal fluid reflux.

[0012] Further, during the electrical stimulation, stimulation to the reticular nucleus of the thalamus is increased to promote sleep spindle.

[0013] Further, the first frequency range data is separated by using an empirical mode decomposition algorithm.

[0014] Further, the second frequency range data is separated by using a multi-scale wavelet packet framework.

[0015] Compared with the prior art, the present application has the following beneficial effects: The present application first couples the first frequency range data and the second frequency range data in the brain wave signal to obtain cerebrospinal fluid rhythm data; then, calculates the phase difference between the first frequency range data and the cerebrospinal fluid rhythm data; finally, dynamically controls the stimulation frequency according to the phase difference, and dynamically controls the stimulation current according to the brain tissue water volume fraction; the present application solves the problem that single-frequency stimulation cannot match the specific metabolic needs of non-rapid eye movement sleep and rapid eye movement sleep, considers the dynamic changes of post-traumatic brain edema and the needs and influences of cerebrospinal fluid rhythm on current control, improves the real-time adaptive ability to the dynamic changes of post-traumatic brain edema, and guarantees the treatment effect of Alzheimer's disease. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings constituting a part of this embodiment are used to provide a further understanding of the embodiment, and the schematic embodiment and its description are used to explain the embodiment, and do not constitute an improper limitation on the embodiment.

[0017] Figure 1 is the system framework diagram of embodiment 1 of the present application. DETAILED DESCRIPTION

[0018] The present application will be further described below in combination with the drawings and embodiments.

[0019] 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.

[0020] Example 1: Currently, post-Alzheimer's electrical stimulation systems suffer from several problems, including single-band stimulation failing to match the specific metabolic needs of the non-rapid eye movement-rapid eye movement (NREM-REM) sleep cycle, fluctuations in the interface impedance of traditional electrodes leading to a decrease in stimulation efficiency of >40%, and a lack of real-time adaptability to the dynamic changes in post-traumatic brain edema.

[0021] To address at least one of the aforementioned problems, this embodiment provides an Alzheimer's disease electrical stimulation system based on cerebrospinal fluid rhythm; such as Figure 1 As shown, the system includes a data acquisition module, a decoupling-reassembly module, a computing module, and a control module; specifically: The data acquisition module is configured to acquire the electroencephalogram (EEG) signals of Alzheimer's patients and the brain water content (BWC). The decoupling-recombination module is configured to: determine a first frequency band range data and a second frequency band range data in the electroencephalogram signal; couple the first frequency band range data and the second frequency band range data to obtain cerebrospinal fluid rhythm data; wherein the frequency of the first frequency band range data is lower than the frequency of the second frequency band range data; The calculation module is configured to calculate the phase difference between the data in the first frequency band range and the cerebrospinal fluid rhythm data; The control module is configured to dynamically control the stimulation frequency based on the phase difference and dynamically control the stimulation current based on the brain tissue water volume ratio.

[0022] This embodiment takes into account the dynamic changes of post-traumatic cerebral edema and the needs and influences of cerebrospinal fluid rhythm on current control, improves the real-time adaptability to the dynamic changes of post-traumatic cerebral edema, ensures the treatment effect of Alzheimer's disease, sleep disorders and traumatic brain injury, and solves the problem that single-band stimulation cannot match the specific metabolic needs of non-rapid eye movement sleep and rapid eye movement sleep cycles.

[0023] In some embodiments, data from a first frequency band range in the electroencephalogram (EEG) signal are separated in real time. δ (0.5-4Hz), Second frequency band range data σ (12-16Hz) and third frequency band range data γ (30-80Hz). Cerebrospinal fluid rhythm data are as follows: ; wherein, is cerebrospinal fluid rhythm data; is first frequency band range data; is second frequency band range data; and are preset coefficients, which are 0.7 and 0.3, respectively.

[0024] In some embodiments, the material-based impedance imaging acquires brain tissue water volume fraction in real time. A specific impedance imaging method is to apply a micro-current to the brain tissue through a conductive hydrogel electrode (impedance self-adjusting range 50-5000 ), and detect the difference in current impedance of the brain tissue with different water content. The selection of electrode material and the optimization of contact pressure (using a genetic algorithm with a step size of 0.1N to adjust in real time) ensure the stability of signal acquisition. Multi-band signal processing: 0.5-4Hz( δ ), 12-16Hz( σ ) and other specific frequency band electroencephalogram signals are used to realize signal separation through empirical mode decomposition and multi-scale wavelet packet architecture. The coupling formula of the cerebrospinal fluid rhythm data F = 0.7δ + 0.3σ establishes a correlation model between brain electrical activity and body fluid circulation. Dynamic modeling calculation: based on the exponential relationship, the brain tissue water volume fraction is inverted through the impedance change. The model establishes the impedance-water content mapping relationship through real-time calculation of the phase difference ΔΦ = δ - F . Closed-loop feedback regulation: impedance data is updated every 200ms, and dynamic adjustment is realized in combination with the stimulation frequency formula to ensure sub-second response capability to changes in brain edema.

[0025] In some embodiments, an adaptive interface module is provided, which includes a conductive hydrogel electrode (impedance self-adjusting range 50-5000 ), and a genetic algorithm based on materials to optimize the contact pressure in real time (step size 0.1N). Specifically, the core purpose of the adaptive interface module is to ensure the stability and accuracy of impedance imaging data by optimizing the electrode-tissue interface characteristics in real time. Specifically, it contains two key functions: impedance dynamic matching: using a conductive hydrogel electrode (di impedance self-adjusting range 50-5000 ), the impedance fluctuation caused by tissue edema is compensated by the change of the conductivity of the material itself, and the contact impedance error is controlled within ±15%. Contact pressure optimization: based on the genetic algorithm, the electrode pressure is adjusted in real time with a step size of 0.1N, solving the problem of poor contact caused by skull deformation or patient movement. Experimental data shows that this algorithm can improve the uniformity of pressure distribution by 62%, significantly reducing motion artifacts. This module optimizes the electrical characteristics and mechanical contact at the same time, so that the impedance imaging system can still maintain a signal fidelity of more than 0.95 in a dynamic physiological environment.

[0026] In some embodiments, the stimulation frequency is: ; in, The adjusted stimulation frequency; This is the initial stimulation frequency; This represents the phase difference between the data in the first frequency band and the cerebrospinal fluid rhythm data.

[0027] The stimulation current is: ; in, The adjusted stimulation current; The initial current is 2mA; The brain tissue water volume is used; optionally, the brain tissue water volume is detected independently by impedance imaging, directly correlated with the tissue conductivity, to determine the intensity of the stimulation current.

[0028] In conclusion, when δ Waves and F Increased rhythmic phase difference indicates intensified neuronal cluster desynchronization. Reducing the stimulation frequency (down to a minimum of 0.8f0) can promote slow-wave oscillation renormalization, and experimental data show that it can increase the power of sleep spindle waves by 43%. Current regulation: For every 10% increase in brain tissue water volume, conductivity increases by 2.3 times. An exponential decay model is used to compensate for changes in conductivity, ensuring that the stimulation current density remains stable at 0.2-0.5. Within a safe range, animal experiments have verified that it can reduce edema volume by 58%.

[0029] In one embodiment, when determining the magnitude of the stimulation current, the regulation principle of the brain tissue water volume fraction E is based on its nonlinear relationship with conductivity: for every 10% increase in water content, conductivity increases by 2.3 times, leading to an abnormally large increase in current density at the same voltage. Therefore, exponential decay is used for dynamic compensation: when... E When the current is 0.3 (30% moisture content), it decays to 74% of its initial value, precisely matching the conductivity change curve. =0.98), ensuring the stimulation current density remains stable between 0.2 and 0.5. Safety range. Adaptive adjustment is achieved through impedance-water content mapping (real-time error <5%). Animal experiments show that it can reduce edema volume by 58% while avoiding the risk of current overload under high conductivity conditions. The deep coupling between biophysical characteristics and circuit model breaks through the safety bottleneck of traditional fixed parameter stimulators.

[0030] Effectiveness verification (TBI rat model): cerebral edema volume decreased by 58% (T2-weighted MRI); the area surrounding the traumatic lesion... AβDeposition decreased by 72% (immunofluorescence).

[0031] In some embodiments, increase stimulation of the lateral sinus to enhance cerebrospinal fluid reflow, and increase stimulation of the reticular nucleus of the thalamus to promote sleep spindles. Optionally, use 1 Hz square wave (30% duty cycle) for lateral sinus stimulation to enhance cerebrospinal fluid reflow, and use 40 Hz gamma burst (200 μs pulse width) for reticular nucleus of the thalamus stimulation to promote sleep spindles.

[0032] In some embodiments, perform dynamic matching, in NREM stage: use 0.8-1.2 Hz sweep stimulation (sweep speed 0.05 Hz / s) to lock δ wave negative phase; in REM stage, use 5 Hz carrier + 80 Hz envelope modulation to enhance θ - γ cross-frequency coupling; optionally, θ waves are 4 Hz~8 Hz brain waves.

[0033] In some embodiments, detect calcium signal indicating neuronal activity (GCaMP6s, sampling rate 30 Hz), and detect endogenous NADH fluorescence response metabolic state (excitation wavelength 340 nm). Trigger cooling stimulation when metabolic stress index ; mitochondrial membrane potential stability is increased by 3.8 times (JC-1 flow detection).

[0034] Embodiment 2: This embodiment provides an Alzheimer's disease electrical stimulation system based on cerebrospinal fluid rhythm, which is based on embodiment 1: Use independent component analysis (ICA) combined with prior lead constraints to supplement existing frequency domain decomposition methods. By constructing a lead topology matrix, muscle artifact and γ oscillation components can be effectively separated. This algorithm can be integrated into the preprocessing stage of the multi-dimensional feature extraction unit 521.

[0035] Introduce empirical mode decomposition (EMD) algorithm to separate δ waves, solve δ the non-stationary characteristics of the wave; optionally, initialize the IMF list, use a while loop for decomposition until the residual energy is below threshold 1, in each iteration, extract a new IMF15 by the sift function, use an adaptive stop condition (0.3 times the residual standard deviation) to control the number of sifts13, add the newly extracted IMF to the list and update the residual signal; this algorithm can complement the existing power spectral density analysis, and is particularly suitable for abnormal δ wave separation in Alzheimer's patients. Design an 8-layer wavelet packet decomposition tree to optimize α (12-16 Hz) and γBand-pass separation in 30-80Hz band, replacing existing fixed-bandwidth filters to improve alpha band resolution to ±0.5Hz. Construct a dual-flow CNN-LSTM hybrid network to replace traditional spectral analysis, automatically learning the nonlinear combination of time-frequency features, and forming a cascade structure with existing hierarchical neural networks.

[0036] Introduce the weighted phase-lag index (wPLI) to improve the existing synchronization index, which is not sensitive to volume conduction effects and can more accurately assess cross-band coupling strength, supporting α Calculation of the coherence square term.

[0037] Through original signal → blind source separation → time-frequency decomposition → deep feature extraction, the final output parameters are used by the stimulation encoding unit. Modular design is required when implemented in FPGA, and the delay of each stage is controlled within 20ms to meet the real-time requirements.

[0038] In some embodiments, multi-dimensional parameter optimization is achieved through triple gene encoding: the chromosome structure contains three dimensions of pressure (0.1N step / 0-10N range), frequency (0.1Hz precision / 0.5-100Hz range), and phase shift (5° step / -180~180° range), and 30-bit Gray code encoding is used to improve local search efficiency by 23%. The fitness function integrates impedance matching (weight 0.4), cerebrospinal fluid flow gain (weight 0.5), and patient comfort (weight 0.1) into three objectives, where F_discomfort is a 0-10 subjective discomfort score. In the dynamic adjustment mechanism, the cerebrospinal fluid flow equation The metabolic pressure index MPI ( and edema index are used to achieve two-factor coupling adjustment: when MPI>2.5 α 0.2·tanh(MPI-2.5) is added to enhance δ wave (0.5-4Hz) CSF clearance function, while the edema index increases by 0.1 β 0.01 is added to offset the change in conductivity. Phase synchronization adjustment is achieved by α _adj=0.7·[1+(wPLI( δ - γ )-0.4) / 0.5] when δ wPLI>0.4 γ wave (30-80Hz) to improve cross-band coupling strength, increasing slow wave sleep spindle density by 45%. This innovative solution combines metabolic state and tissue electrical property changes, increasing CSF clearance rate by 38% and impedance matching error by less than 7% in animal experiments.

[0039] The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system achieves precise monitoring through a multimodal data acquisition module, employing conductive hydrogel electrodes (impedance self-adjusting range 50-5000Ω). The contact pressure was optimized using a genetic algorithm (step size 0.1N) to ensure that the electrode-tissue interface impedance error was controlled within ±15%. The implantable microelectrode array simultaneously detected the Aβ42 / P-tau181 ratio in cerebrospinal fluid (sensitivity 0.1 pg / mL) and... θ - γ Phase amplitude coupling strength (5kHz sampling rate). The system uses empirical mode decomposition to extract the θ wave (4-8Hz) and wavelet packet transform to analyze the γ wave (30-100Hz), constructing an θ-γ coupling model: F_AD(t)=0.6θ(t)+0.4γ(t), and dynamically optimizes the weight coefficients through an LSTM network. The dynamic control algorithm adjusts the parameters in real time according to the phase difference Δφ=θ_phase-F_phase (accuracy ±0.1π): when the θ-γ coupling strength <0.35, the γ enhancement mode is activated: f_γ=40Hz+5·(0.5-PAC_θγ), and during N3 sleep, a 0.05Hz ultra-slow wave (pulse width 200ms, 1.2mA) is switched to increase the cerebrospinal fluid-in-tissue fluid (CSF-ISF) exchange rate to (8.7±1.2)×10. -4 min -1 The brain tissue water volume fraction E was inverted by multi-band impedance imaging (0.5-100Hz sweep) and dynamically compensated according to I_adj=I_0·e^{-2.3(E-0.2)}. =0.98), combined with Doppler ultrasound tracking of the carotid artery pulsation phase (Δt_stim=T_cardiac·arctan(φ_CSF / φ_artery) / 2π) to achieve synchronous optimization of fluid dynamics. The closed-loop validation system updates parameters every 200ms, increasing the frequency by 0.8Hz when Δφ>0.5π, and automatically increasing the γ stimulation intensity (I_hotspot=0.8+0.1·SUV_PiB) when the Aβ clearance rate is monitored by miniature PET-CT as <15% / week. The safety mechanism establishes a conductivity-temperature coupling model: T_max=39.5-2.3·log 10 (σ / 0.5), switching to safety mode (1Hz@0.2mA) when local temperature >38.5℃ or impedance mutation >20%. Validated in a 5xFAD animal model, this system reduced hippocampal Aβ plaque density by 55.3% (p<0.01), increased θ-γ coupling strength by 75%, and restored nocturnal CSF pulsation amplitude to 82±7% of normal levels, forming a closed-loop regulatory system targeting Alzheimer's pathological characteristics.

[0040] The embodiment is based on the cerebrospinal fluid rhythm of Alzheimer's disease electric stimulation system on the basis of primary traumatic brain injury, and is subjected to multidimensional pathological adaptation modification; the system adds an electrochemical sensor to detect the cerebrospinal fluid Aβ42 / P-tau181 ratio in real time (sensitivity 0.1 pg / mL), and adopts 5 kHz sampling Magnetoencephalography (MEG) to monitor the θ-γ phase amplitude coupling strength, and reconstructs the original δ / β rhythm into a θ / γ coupling model, and introduces 0.05 Hz ultra-slow wave stimulation (I_glymphatic=1.2 mA·sin(2πf_slow t)) to enhance the lymphoid clearance function. In terms of dynamic regulation algorithm, when the θ-γ coupling strength is less than 0.35, the γ band enhancement mode is automatically activated, and the blood brain barrier permeability Ktrans measured by DCE-MRI is dynamically adjusted (I_adjusted=I_0·[1+2.5(Ktrans-0.15)]^-1). The system innovatively realizes sleep cycle synchronous stimulation: 0.05 Hz ultra-slow wave is applied in N3 period to make the CSF-ISF exchange rate increase by 58%; 40 Hz γ wave stimulation is switched in REM period to enhance the phagocytic activity of microglia cells by 37%, and the cerebrospinal fluid dynamics is optimized through ultrasonic Doppler synchronization of carotid artery pulsation phase (Δt_stim=T_cardiac·(φ_CSF-φ_artery) / 2π). Clinical verification shows that the system makes the hippocampal Aβ plaque density decrease by 55.3%, the θ-γ coupling strength increase by 75%, and the night CSF pulsation amplitude recover to 82±7% of the normal level. The system adds Aβ hotspot positioning stimulation (I_hotspot=0.8+0.1·SUV_PiB) of PiB-PET fusion and APOEε4 genotype adaptation algorithm (γ_weight=0.4+0.1·ε4 allele number), and finally forms a closed-loop regulation system for the pathological characteristics of Alzheimer's disease.

[0041] The above only describes the preferred embodiments of the present embodiment and is not intended to limit the present embodiment. Those skilled in the art can make various modifications and changes to the present embodiment. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. An Alzheimer's disease electrical stimulation system based on cerebrospinal fluid rhythms, characterized in that, The method comprises the following steps: A data acquisition module is configured to acquire the brain wave signal of an Alzheimer's patient and the brain tissue water volume fraction; A decoupling-recombination module is configured to determine the first frequency range data and the second frequency range data in the brain wave signal, and to couple the first frequency range data and the second frequency range data to obtain cerebrospinal fluid rhythm data; the frequency of the first frequency range data is lower than the frequency of the second frequency range data; A calculation module is configured to calculate the phase difference between the first frequency range data and the cerebrospinal fluid rhythm data; A control module is configured to dynamically control the stimulation frequency according to the phase difference and to dynamically control the stimulation current according to the brain tissue water volume fraction.

2. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system as described in claim 1, wherein, The brain tissue water volume fraction is obtained in real time based on the impedance imaging technology of materials.

3. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system as described in claim 1, wherein, The cerebrospinal fluid rhythm data is: ； wherein, is cerebrospinal fluid rhythm data; is first frequency band range data; second frequency band range data; and is a preset coefficient.

4. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system of claim 3, wherein, The frequency range of the first frequency range data is 0.5Hz-4Hz, and the frequency range of the second frequency range data is 12Hz-16Hz.

5. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system as described in claim 1, wherein, The stimulation frequency is: ； wherein, is the adjusted stimulation frequency; is the initial stimulation frequency; is the phase difference between the first frequency band range data and the cerebrospinal fluid rhythm data.

6. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system as described in claim 1, wherein, The stimulation current is: ； wherein, is the adjusted stimulation current; is the initial current; is the brain tissue water volume fraction.

7. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system of claim 1, wherein, During the electrical stimulation process, the stimulation on the lateral sinus is increased to enhance the cerebrospinal fluid reflux.

8. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system of claim 7, wherein, During the electrical stimulation process, the stimulation on the thalamic reticular nucleus is increased to promote sleep spindles.

9. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system of claim 1, wherein, The first frequency range data is separated by using an empirical mode decomposition algorithm.

10. The cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system of claim 1, wherein, The second frequency range data is separated by using a multi-scale wavelet packet framework.

Images