A method and system for delay feedback adaptive intervention regulation of alzheimer's disease

Abstract

The application provides an Alzheimer's disease time-delay feedback adaptive intervention regulation method and system, which comprises the following steps: establishing a thalamocortical system model for simulating electroencephalogram signals in an Alzheimer's disease state; adding adaptive stimulation according to dynamic changes of the nervous system itself in a closed-loop control strategy of the thalamocortical system model; and simulating electroencephalogram signals generated by the thalamocortical system model when adaptive stimulation parameters change by changing the adaptive stimulation parameters. The method further comprises displaying oscillation behavior of the simulated electroencephalogram signals in a preset frequency band by using a frequency spectrum. Based on the method, an Alzheimer's disease time-delay feedback adaptive intervention regulation method is further provided. The closed-loop control system designed by the application is more accurate, and is particularly helpful for assisting in solving the problem of early intervention of Alzheimer's disease, intuitively displays the progress and intervention, and optimally analyzes the visualization, so that the application can be more widely used for health management.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of neural computing, and particularly relates to an Alzheimer's disease delay feedback adaptive intervention regulation method and system. BACKGROUND

[0002] Alzheimer's disease (AD) is a common degenerative neurological disease that seriously threatens human health. Therefore, the pathogenesis of Alzheimer's disease and related recovery strategies have attracted widespread attention. In order to better understand the neuron correlation of Alzheimer's disease and intervene in early cognitive impairment, much attention has been gradually shifted to computational models related to neurological or mental diseases. The abnormal brain oscillation phenomenon of Alzheimer's disease patients is manifested as a decrease in cognitive function and a slowing down of alpha rhythm in electroencephalogram during brain oscillation. In clinical practice, drug treatment of different neurotransmitter systems can delay the development of AD, but the effect of drug treatment is poor and the cost is huge. Deep brain stimulation (DBS) as a kind of neuromodulation technology is an important auxiliary rehabilitation method for most neurological diseases.

[0003] However, due to the inherent open-loop characteristics of DBS, it cannot be adaptively adjusted according to the dynamic changes of the nervous system itself, and the DBS stimulation waveform is always fixed and unchangeable, which requires a large amount of energy. SUMMARY

[0004] In order to solve the above technical problems, the application provides an Alzheimer's disease delay feedback adaptive intervention regulation method and system, which helps to solve the problem of early intervention of Alzheimer's disease, intuitively displays the progress and intervention thereof, and optimizes the visual analysis, so that it can be more widely used for health management.

[0005] To achieve the above purpose, the application adopts the following technical solutions:

[0006] An Alzheimer's disease delay feedback adaptive intervention regulation method comprises the following steps:

[0007] Establishing a thalamocortical system model for simulating electroencephalogram signals in an Alzheimer's disease state;

[0008] Adding adaptive stimulation according to the dynamic changes of the nervous system itself in the closed-loop control strategy of the thalamocortical system model;

[0009] By changing the adaptive stimulation parameters, the electroencephalogram signals generated by the thalamocortical system model when the adaptive stimulation parameters are changed are simulated.

[0010] Further, the method further comprises using frequency spectrum to show the oscillation behavior of the simulated electroencephalogram signals in a preset frequency band.

[0011] Further, the process of establishing the thalamocortical system model for simulating the electroencephalogram under the Alzheimer's disease state comprises: adopting a difference equation or a differential equation to describe the thalamocortical system model; and the thalamocortical system model comprises state variables of a thalamic module and state variables of a cortical module.

[0012] Further, the state variables of the thalamic module comprise a state scalar x1 of a retinal neuron group, a state variable x2 of a thalamic relay neuron group, and a state variable x3 of a thalamic reticular neuron group.

[0013] x4 represents a first-order differential of x1; x5 represents a first-order differential of x2; and x6 represents a first-order differential of x3; that is:

[0014]

[0015] The first-order differentials of x4, x5 and x6 are respectively calculated; that is:

[0016] wherein A t is an excitatory synaptic parameter in the thalamic module; a t is an inverse of an excitatory time constant 1 / a t in the thalamic module; b t is an inverse of an inhibitory time constant 1 / b t in the thalamic module; p2(t) represents a Gaussian white noise of an external input of the cortical module; S1 converts an average membrane potential of a post-synaptic neuron group of the thalamus into a spike density; C t1 represents a synaptic connection coefficient from TCR to TRN; C t2 represents a synaptic connection coefficient from TRN to TCR; C t3 represents a synaptic connection coefficient from Ret to TCR; K ct represents a coupling coefficient from the cortical module to the thalamic module; u(t) is a stimulus signal adaptively applied to the thalamic relay nucleus in the thalamocortical coupling model according to an output signal x t (t) of the thalamic module itself; B t represents an inhibitory synaptic parameter in the thalamic module; and x7 is a pyramidal neuron group in the cortical module.

[0017] Further, the state variables of the cortical module comprise a pyramidal neuron group x7 in the cortical module, an external input x8, a slow inhibitory neuron group x9, a fast inhibitory neuron group x 10 , and a state variable x 11 of an excitatory interneuron group.

[0018] x 12 represents a first-order differential of x7; and x 13The first derivative of expression x8; using x 14 The first derivative of expression x9; using x 15 The first derivative of expression x 10 ; using x 16 The first derivative of expression x 11 ; namely:

[0019] The first derivative of expression x 12 , x 13 , x 14 , x 15 , x 16 ; namely:

[0020]

[0021]

[0022]

[0023] A c represents the excitatory synaptic parameter in the cortical module; a c is the derivative of the excitatory time constant in the cortical module; S2 is the average membrane potential of the post-synaptic neuron group in the cortical module converted into the action potential pulse density; K tc represents the coupling coefficient from the thalamic module to the cortical module; p1(t) represents the Gaussian white noise input from the outside world of the thalamic module; C1 represents the first excitatory synaptic connection coefficient; C2 represents the first excitatory synaptic connection coefficient; C3 represents the first slow inhibitory synaptic connection coefficient; C4 represents the second slow inhibitory synaptic connection coefficient; C5 represents the first fast inhibitory synaptic connection coefficient; C6 represents the second fast inhibitory synaptic connection coefficient; C7 represents the slow and fast inhibitory synaptic connection coefficient; S2 represents the average membrane potential of the post-synaptic neuron group in the cortical module converted into the action potential pulse density; G c represents the fast inhibitory synaptic parameter in the cortical module; g c is the derivative of the fast inhibitory time constant 1 / g c in the cortical module; B c represents the slow inhibitory synaptic parameter in the cortical module; b c is the derivative of the slow inhibitory time constant 1 / b c in the cortical module.

[0024] Further, the process of adding adaptive stimulation according to the dynamic changes of the nervous system itself in the closed-loop control strategy of the thalamocortical system model comprises:

[0025] Establish a time delay feedback adaptive stimulation closed-loop control strategy function: u(t) = K(x t(t-t)-x t (t)); wherein, K is a feedback gain, τ is a time delay, u(t) is an output signal of the thalamic module itself according to the nervous system, and x t (t) is adaptively applied to the stimulation signal of the thalamic relay nucleus in the thalamocortical coupling model.

[0026] Further, the process of simulating the electroencephalogram generated by the thalamocortical system model when the adaptive stimulation parameter is changed includes: changing the feedback gain K and the time delay τ to generate adaptive stimulation regulation intervention for the thalamocortical system, so that the system output electroencephalogram appears alpha rhythm.

[0027] Further, the process of using frequency spectrum to show the oscillation behavior of the simulated electroencephalogram in the preset frequency band includes: performing power spectrum analysis on the simulated membrane potential output by the thalamic module and the cortical module in the thalamocortical system model.

[0028] The application further provides an Alzheimer's disease time-delay feedback adaptive intervention regulation system, which comprises an establishing model module, an adding module and a simulation module.

[0029] The establishing model module is used to establish a thalamocortical system model for simulating electroencephalogram in an Alzheimer's disease state.

[0030] The adding module is used to add adaptive stimulation according to the dynamic change of the nervous system in the closed-loop control strategy of the thalamocortical system model.

[0031] The simulation module is used to simulate the electroencephalogram generated by the thalamocortical system model when the adaptive stimulation parameter is changed by changing the adaptive stimulation parameter.

[0032] Further, the system further comprises a display module.

[0033] The display module is used to use frequency spectrum to show the oscillation behavior of the simulated electroencephalogram in the preset frequency band.

[0034] The effects provided in the summary are only the effects of the embodiments, not all the effects of the application. One of the above technical solutions has the following advantages or beneficial effects:

[0035] The application provides an Alzheimer's disease time-delay feedback adaptive intervention regulation method and system, which comprises the following steps: a thalamus cortex system model for simulating electroencephalogram signals in an Alzheimer's disease state is established; adaptive stimulation according to dynamic changes of a nervous system itself is added in a closed-loop control strategy of the thalamus cortex system model; electroencephalogram signals generated by the thalamus cortex system model when adaptive stimulation parameters change are simulated by changing the adaptive stimulation parameters; the method further comprises displaying the simulated electroencephalogram signals in a preset frequency band by using a frequency spectrum. Based on the Alzheimer's disease time-delay feedback adaptive intervention regulation method, an Alzheimer's disease time-delay feedback adaptive intervention regulation method is further provided. The application creatively introduces a closed-loop control strategy into a thalamus cortex coupling system, firstly establishes a thalamus cortex coupling system model to simulate electroencephalogram signals in an Alzheimer's disease state, then designs a closed-loop control strategy to apply adaptive stimulation to the system, and finally analyzes the influence of stimulation parameter changes in the control strategy on the spectrum of the alpha band in the system output signal. The designed closed-loop control system is more accurate, more reliable in performance, simpler and more convenient to use, especially helps to solve the problem of early intervention of Alzheimer's disease, intuitively displays the progress and intervention, and optimally analyzes and visualizes, so that the system can be more widely used to guide health management. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 A flow chart of an Alzheimer's disease time-delay feedback adaptive intervention regulation method of the embodiment 1 of the application;

[0037] Figure 2 A structural schematic diagram of the Alzheimer's disease time-delay feedback adaptive intervention regulation method of the embodiment 1 of the application;

[0038] Figure 3 The thalamus module output electroencephalogram signals and a frequency spectrum diagram of the embodiment 1 of the application before and after stimulation at 1s;

[0039] Figure 4 The cortex module output electroencephalogram signals and a frequency spectrum diagram of the embodiment 1 of the application before and after stimulation at 1s;

[0040] Figure 5 A schematic diagram of the adaptive delay feedback stimulation signal in the embodiment 1 of the application;

[0041] Figure 6 A schematic diagram of the influence of the parameters K and i in the closed-loop stimulation strategy on the main frequency of the thalamus output signal in the embodiment 1 of the application;

[0042] Figure 7 A schematic diagram of the influence of the parameters K and i in the closed-loop stimulation strategy on the main frequency of the cortex output signal in the embodiment 1 of the application;

[0043] Figure 8 Figure 2 is a schematic diagram of an Alzheimer's disease time-delay feedback adaptive intervention regulation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to clearly illustrate the technical features of the present application, the following will describe the present application in detail with specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing the various structures of the present application. In order to simplify the disclosure of the present application, the components and settings of specific examples are described below. In addition, the present application can repeatedly refer to the same numbers and / or letters in different examples. Such repetition is for the purpose of simplification and clarity, and does not in itself indicate a relationship between the various embodiments and / or settings being discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. The present application omits the description of well-known components and processing techniques and processes to avoid unnecessarily limiting the present application.

[0045] Embodiment 1

[0046] The present application embodiment 1 proposes an Alzheimer's disease time-delay feedback adaptive intervention regulation method, which creatively introduces a closed-loop control strategy into the thalamocortical coupling system. First, a thalamocortical coupling system model is established to simulate the electroencephalogram signal under Alzheimer's disease state, then a closed-loop control strategy is designed to apply adaptive stimulation to the system, and finally the effect of the change of the stimulation parameter in the control strategy on the α band spectrum in the system output signal is analyzed.

[0047] The specific steps of the method include: establishing a thalamocortical system model for simulating electroencephalogram signals under Alzheimer's disease state;

[0048] Adding adaptive stimulation according to the dynamic changes of the nervous system itself in the closed-loop control strategy of the thalamocortical system model;

[0049] By changing the adaptive stimulation parameters, the electroencephalogram signals generated by the thalamocortical system model when the adaptive stimulation parameters change are simulated.

[0050] The method further includes using a frequency spectrum to show the oscillation behavior of the simulated electroencephalogram signals in the preset frequency band.

[0051] As Figure 1 Figure 1 is a flowchart of an Alzheimer's disease time-delay feedback adaptive intervention regulation method according to an embodiment of the present application; a closed-loop control strategy is introduced to adjust the stimulation parameters in the control strategy, and then the effect of the stimulation parameters on the oscillation behavior of the simulated electroencephalogram α (8Hz-13Hz) band of the model output is studied and compared based on frequency spectrum analysis. As Figure 2 Figure 2 is a schematic diagram of an Alzheimer's disease time-delay feedback adaptive intervention regulation system according to an embodiment of the present application.

[0052] The process for establishing the thalamocortical system model for simulating the electroencephalogram in Alzheimer's disease state comprises: using differential equations or differential equations to describe the thalamocortical system model; the thalamocortical system model comprises state variables of a thalamic module and state variables of a cortical module.

[0053] The state variables of the thalamic module comprise a state scalar x1 of a retinal neuron group, a state variable x2 of a thalamic relay neuron group, and a state variable x3 of a thalamic reticular neuron group;

[0054] The first-order differential of x1 is expressed by x4; the first-order differential of x2 is expressed by x5; and the first-order differential of x3 is expressed by x6; that is:

[0055]

[0056] The first-order differentials of x4, x5 and x6 are respectively calculated; that is:

[0057] Wherein, A t is an excitatory synaptic parameter in the thalamic module; a t is the inverse of an excitatory time constant 1 / a t in the thalamic module; b t is the inverse of an inhibitory time constant 1 / b t in the thalamic module; p2(t) represents a Gaussian white noise of an external input of the cortical module; S1 converts an average membrane potential of a post-synaptic neuron group in the thalamus into an action potential pulse density; C t1 represents a synaptic connection coefficient from TCR to TRN; C t2 represents a synaptic connection coefficient from TRN to TCR; C t3 represents a synaptic connection coefficient from Ret to TCR; K ct represents a coupling coefficient from the cortical module to the thalamic module; u(t) is a stimulus signal adaptively applied to the thalamic relay nucleus in the thalamocortical coupling model according to an output signal x t (t) of the thalamic module itself; B t represents an inhibitory synaptic parameter in the thalamic module; x7 is a pyramidal neuron group in the cortical module.

[0058] The state variables of the cortical module comprise a pyramidal neuron group x7 in the cortical module, an external input x8, a slow inhibitory neuron group x9, a fast inhibitory neuron group x 10 , and a state variable x 11 of an excitatory intermediate neuron group;

[0059] The first-order differential of x7 is expressed by x 12 ; the first-order differential of x8 is expressed by x 13 ; and the first-order differential of x9 is expressed by x 14The first-order differential of expression x9; adopt x 15 The first-order differential of expression x 10 ; adopt x 16 The first-order differential of expression x 11 ; that is:

[0060] The first-order differential of expression x 12 , x 13 , x 14 , x 15 , x 16 ; that is:

[0061]

[0062]

[0063]

[0064] A c represents the excitatory synapse parameter in the cortical module; a c is the derivative of the excitatory time constant in the cortical module; S2 is the average membrane potential of the post-synaptic neuron group in the cortical module converted into the action potential pulse density; K tc represents the coupling coefficient of the thalamic module to the cortical module; p1(t) represents the Gaussian white noise of the external input of the thalamic module; C1 represents the first excitatory synapse connection coefficient; C2 represents the first excitatory synapse connection coefficient; C3 represents the first slow inhibitory synapse connection coefficient; C4 represents the second slow inhibitory synapse connection coefficient; C5 represents the first fast inhibitory synapse connection coefficient; C6 represents the second fast inhibitory synapse connection coefficient; C7 represents the slow and fast inhibitory synapse connection coefficient; S2 represents the average membrane potential of the post-synaptic neuron group in the cortical module converted into the action potential pulse density; G c represents the fast inhibitory synapse parameter in the cortical module; g c is the derivative of the fast inhibitory time constant 1 / g c in the cortical module; B c represents the slow inhibitory synapse parameter in the cortical module; b c is the derivative of the slow inhibitory time constant 1 / b c in the cortical module.

[0065] As Figure 5 is a schematic diagram of an adaptive delay feedback stimulation signal applied in the system of embodiment 1 of the present application; the process of adding adaptive stimulation according to the dynamic changes of the nervous system in the closed-loop control strategy of the thalamocortical system model includes:

[0066] Establish a delay feedback adaptive stimulation closed-loop control strategy function: u(t) = K(xt (t-t)-x t (t)); wherein, K is a feedback gain, τ is a time delay, u(t) is a signal outputted by the thalamus module itself according to the neural system thalamus module, and x(t) is a signal outputted by the cortex module. t (t) is adaptively applied to the stimulation signal of the thalamic relay nucleus in the thalamus-cortex coupling model.

[0067] As Figure 3 is the thalamus module outputted by the system of embodiment 1 of the present application before and after applying stimulation at 1s, and is the frequency spectrum diagram; as Figure 4 is the cortex module outputted by the system of embodiment 1 of the present application before and after applying stimulation at 1s, and is the frequency spectrum diagram; by changing the adaptive stimulation parameters, the process of the brain electrical signals generated by the thalamus-cortex system model when the adaptive stimulation parameters are changed includes: by changing the feedback gain K and the time delay τ, the adaptive stimulation regulation intervention is generated by the thalamus-cortex system, so that the alpha rhythm appears in the system output brain electrical signal.

[0068] The related equations defined in the model are solved by using the Euler method in Matlab, and the total simulation time is 3s. The membrane potential of the thalamic relay nucleus neuron group is repeatedly simulated for 10 times independently for each group of parameter values to obtain the average value, so as to ensure the accuracy of the statistics. In order to obtain the power spectral density of the alpha (8-13Hz) frequency band outputted by the thalamus in the model, the power spectrum analysis is performed on the simulated membrane potential outputted by the thalamus module and the cortex module in the model. As Figure 6 is the influence diagram of the parameters K and i in the closed-loop stimulation strategy of embodiment 1 of the present application on the main frequency of the thalamus output signal; as Figure 7 is the influence diagram of the parameters K and i in the closed-loop stimulation strategy of embodiment 1 of the present application on the main frequency of the cortex output signal;

[0069] The obtained membrane potential is sampled at 1000Hz and band-pass filtered by using a Butterworth filter with an order of 10, and the lower and upper limit cutoff frequencies are 3Hz and 30Hz respectively.

[0070] The power spectral density is obtained by using the Welch periodogram method with a Hamming window, and the segment length of the Hamming window is 1 / 2 of the sampling frequency, and the overlap is 50%.

[0071] The closed-loop control system designed in the adaptive intervention regulation method for Alzheimer's disease delay feedback of embodiment 1 of the present application is more accurate, the performance is more reliable, the use is more simple and convenient, and it is especially helpful for assisting to solve the early intervention problem of Alzheimer's disease, and the progress and intervention are intuitively displayed and more optimally visualized, so that the method can be more widely used for guiding health management.

[0072] Embodiment 2

[0073] Based on the Alzheimer's disease delay feedback adaptive intervention regulation method proposed in embodiment 1 of the present application, an Alzheimer's disease delay feedback adaptive intervention regulation system is proposed in embodiment 2 of the present application. Figure 8 The system includes a model establishing module, an adding module and a simulation module.

[0074] The model establishing module is configured to establish a thalamocortical system model simulating electroencephalogram signals in an Alzheimer's disease state.

[0075] The adding module is configured to add adaptive stimulation according to dynamic changes of the nervous system in a closed-loop control strategy of the thalamocortical system model.

[0076] The simulation module is configured to simulate electroencephalogram signals generated by the thalamocortical system model when adaptive stimulation parameters are changed by changing the adaptive stimulation parameters.

[0077] The system further includes a display module configured to display the simulated electroencephalogram signals in a preset frequency band using a spectrum.

[0078] The process implemented by the establishing module includes a process of establishing a thalamocortical system model simulating electroencephalogram signals in an Alzheimer's disease state, which includes using a difference equation or a differential equation to describe the thalamocortical system model, and the thalamocortical system model including state variables of a thalamic module and state variables of a cortical module.

[0079] The state variables of the thalamic module include a state scalar x1 of a retinal neuron group, a state variable x2 of a thalamic relay neuron group and a state variable x3 of a thalamic reticular neuron group.

[0080] x4 is used to express the first-order differential of x1, x5 is used to express the first-order differential of x2, and x6 is used to express the first-order differential of x3, i.e.

[0081]

[0082] The first-order differentials of x4, x5 and x6 are calculated respectively, i.e.

[0083] wherein, A t is an excitatory synapse parameter in the thalamic module; a t is the inverse of an excitatory time constant 1 / a t in the thalamic module; b t is an inhibitory time constant 1 / b tthe inverse of the reciprocal of the cortical module's input; p2(t) represents the Gaussian white noise of the cortical module's input; S1 is the average membrane potential of the thalamic postsynaptic neuron group converted into the action potential pulse density; C t1 represents the TCR to TRN synaptic connection coefficient; C t2 represents the TRN to TCR synaptic connection coefficient; C t3 represents the Ret to TCR synaptic connection coefficient; K ct represents the coupling coefficient of the cortical module to the thalamic module; u(t) is the stimulus signal applied to the thalamic relay nucleus in the thalamocortical coupling model according to the thalamic module's own output signal x t (t); B t represents the inhibitory synaptic parameter in the thalamic module; x7 is the pyramidal neuron group in the cortical module.

[0084] The state variables of the cortical module include the pyramidal neuron group x7 in the cortical module, the external input x8, the slow inhibitory neuron group x9, the fast inhibitory neuron group x 10 , and the state variable x 11 of the excitatory interneuron group;

[0085] The first-order differential of x 12 is expressed as x 13 ; the first-order differential of x 14 is expressed as x 15 ; the first-order differential of x 10 is expressed as x 16 ; and the first-order differential of x 11 is expressed as x

[0086] The first-order differentials of x 12 , x 13 , x 14 , x 15 , and x 16 are calculated respectively; that is,

[0087]

[0088] A c represents the excitatory synaptic parameter in the cortical module; a c is the derivative of the excitatory time constant in the cortical module; S2 is the average membrane potential of the postsynaptic neuron group in the cortical module converted into the action potential pulse density; K tcrepresents the coupling coefficient from the thalamic module to the cortical module; p1(t) represents the external input Gaussian white noise to the thalamic module; C1 represents the first excitatory synaptic connection coefficient; C2 represents the first excitatory synaptic connection coefficient; C3 represents the first slow inhibitory synaptic connection coefficient; C4 represents the second slow inhibitory synaptic connection coefficient; C5 represents the first fast inhibitory synaptic connection coefficient; C6 represents the second fast inhibitory synaptic connection coefficient; C7 represents the slow and fast inhibitory synaptic connection coefficient; S2 represents the average membrane potential of the post-synaptic neuron group in the cortical module converted into the action potential pulse density; G c represents the fast inhibitory synaptic parameter in the cortical module; g c is the derivative of the fast inhibitory time constant 1 / g in the cortical module c ; B c represents the slow inhibitory synaptic parameter in the cortical module; b c is the derivative of the slow inhibitory time constant 1 / b in the cortical module c .

[0089] The process implemented by the adding module includes: establishing a time-delay feedback adaptive stimulation closed-loop control strategy function: u(t) = K(x t (t-τ)-x t (t)); wherein K is the feedback gain, τ is the time delay, and u(t) is the stimulation signal adaptively applied to the thalamic relay nucleus in the thalamocortical coupling model according to the output signal x t (t) of the thalamic module itself.

[0090] The process implemented by the simulation module is: by changing the feedback gain K and the time delay τ, an adaptive stimulation regulation intervention is generated to the thalamocortical system, so that the alpha rhythm appears in the output brain electrical signal of the system.

[0091] The process implemented by the display module is: the related equations defined in the model are solved using the Euler method in Matlab, and the total simulation time is 3s. The membrane potential of the thalamic relay nucleus neuron group is averaged by repeating 10 independent simulations for each set of parameter values to ensure the accuracy of the statistics. In order to obtain the power spectral density of the alpha (8-13Hz) frequency band output by the thalamus in the model, power spectrum analysis is performed on the simulated membrane potential output by the thalamic module and the cortical module in the model.

[0092] The obtained membrane potential is sampled at 1000Hz and band-pass filtered using a Butterworth filter with an order of 10, with lower and upper cutoff frequencies of 3Hz and 30Hz, respectively.

[0093] The power spectral density is obtained using the Welch periodogram method with a Hamming window, with a segment length of 1 / 2 of the sampling frequency and an overlap of 50%.

[0094] The closed-loop control system designed in the Alzheimer's disease delay feedback adaptive intervention regulation system of the embodiment 2 is more accurate, the performance is more reliable, the use is more simple and convenient, and the system is especially helpful for assisting in solving the early intervention problem of Alzheimer's disease, directly displays the progress and intervention, and optimally visually analyzes the progress and intervention, so that the system can be more widely used for guiding health management.

[0095] It should be noted that, in this article, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device inherent in the series of elements. Without more limitations, the element defined by the statement "includes one" does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, the above technical solutions provided by the embodiments of the present application are not described in detail, so as not to be too repetitive.

[0096] The above describes the specific embodiments of the present application in combination with the accompanying drawings, but is not a limitation on the protection scope of the present application. Based on the above description, those skilled in the art can make other different forms of modifications or changes. Here, it is not necessary and impossible to exhaust all the embodiments. Various modifications or changes made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A method for Alzheimer's disease delay feedback adaptive intervention regulation, characterized in that, The method comprises the following steps: establishing a thalamocortical system model for simulating electroencephalogram signals in Alzheimer's disease state; the process of establishing the thalamocortical system model for simulating electroencephalogram signals in Alzheimer's disease state comprises: adopting a difference equation or a differential equation to describe the thalamocortical system model; the thalamocortical system model comprises state variables of a thalamic module and state variables of a cortical module; the state variables of the thalamic module comprise a state scalar x1 of a retinal neuron group, state variables x2 of a thalamic relay nucleus neuron group, and state variables x3 of a thalamic reticular nucleus neuron group; x4 represents the first derivative of x1 ; x5 represents the first derivative of x2; x6 represents the first derivative of x3; i.e.: First order differentials of x4, x5, and x6 are calculated, respectively; that is: Among them, A t These are excitatory synaptic parameters in the thalamic module; a t The excitatory time constant 1 / a in the thalamic module t The reciprocal of b; t The inhibitory time constant 1 / b in the thalamic module t The reciprocal of ; p2(t) represents the Gaussian white noise input to the cortical module; S1 converts the average membrane potential of the thalamic postsynaptic neuron group into action potential pulse density; C t1 Indicates the TCR to TRN synaptic connection coefficient; C t2 Indicates the TRN-to-TCR synaptic connectivity coefficient; C t3 K represents the Ret-TCR synaptic connection coefficient; ct This represents the coupling coefficient from the cortical module to the thalamic module; u(t) is based on the output signal x of the thalamic module itself. t (t) Adaptive stimulation signals applied to the thalamic relay nucleus in the thalamic-cortical coupling model; B t x7 represents the inhibitory synaptic parameters in the thalamic module; x8 represents the pyramidal neuron group in the cortical module; the state variables of the cortical module include the pyramidal neuron group x7 in the cortical module, external input x8, slow inhibitory neuron group x9, and fast inhibitory neuron group x1. 10 The state variable x of the excitatory interneuron group 11 ; x 12 x7 13 x8 14 x9 15 x 10 x 16 x 11 x The first order differential of x 12 , x 13 , x 14 , x 15 , x 16 is calculated respectively; that is: A c denotes the excitatory synaptic parameter in the cortical module; a c is the derivative of the excitatory time constant in the cortical module; S2 is the average membrane potential of the post-synaptic neuron population in the cortical module converted to action potential spike density; K tc denotes the coupling coefficient from the thalamic module to the cortical module; p1(t) represents the external input to the thalamic module of Gaussian white noise; C1 denotes the first excitatory synaptic connection coefficient; C2 denotes the first inhibitory synaptic connection coefficient; C3 denotes the first slow inhibitory synaptic connection coefficient; C4 denotes the second slow inhibitory synaptic connection coefficient; C5 denotes the first fast inhibitory synaptic connection coefficient; C6 denotes the second fast inhibitory synaptic connection coefficient; C7 denotes the slow and fast inhibitory synaptic connection coefficient; S2 denotes the average membrane potential of the post-synaptic neuron population in the cortical module converted to action potential spike density; G c denotes the fast inhibitory synaptic parameter in the cortical module; g c is the derivative of the fast inhibitory time constant 1 / g c in the cortical module; B c denotes the slow inhibitory synaptic parameter in the cortical module; b c is the derivative of the slow inhibitory time constant 1 / b c in the cortical module; adding adaptive stimulation according to dynamic changes of the nervous system itself in a closed-loop control strategy of the thalamocortical system model; simulating electroencephalogram signals generated by the thalamocortical system model when adaptive stimulation parameters change by changing the adaptive stimulation parameters.

2. The Alzheimer's disease delay feedback adaptive intervention regulation method according to claim 1, characterized in that, The method further comprises using a frequency spectrum to show oscillation behavior of the simulated electroencephalogram signals in a preset frequency band.

3. The Alzheimer's disease delay feedback adaptive intervention regulation method according to claim 1, characterized in that, The process of adding adaptive stimulation according to dynamic changes of the nervous system itself in a closed-loop control strategy of the thalamocortical system model comprises: A time-delayed feedback adaptive stimulation closed-loop control strategy function is established: u(t) = K(x t (t - τ) - x t (t)); where K is the feedback gain, τ is the time delay, u(t) is the stimulation signal applied to the thalamic relay nucleus in the thalamocortical coupling model according to the output signal x t (t) of the thalamic module of the nervous system itself.

4. The Alzheimer's disease delay feedback adaptive intervention regulation method according to claim 1, characterized in that, The process of simulating electroencephalogram signals generated by the thalamocortical system model when adaptive stimulation parameters change by changing the adaptive stimulation parameters comprises: changing feedback gain K and time delay τ to generate adaptive stimulation regulation intervention for the thalamocortical system, so that alpha rhythm appears in system output electroencephalogram signals.

5. The Alzheimer's disease delay feedback adaptive intervention regulation method according to claim 2, characterized in that, The process of using a frequency spectrum to show oscillation behavior of the simulated electroencephalogram signals in a preset frequency band comprises: performing power spectrum analysis on simulated membrane potentials output by the thalamic module and the cortical module in the thalamocortical system model.

6. An Alzheimer's disease time-delayed feedback adaptive intervention regulation system for performing the Alzheimer's disease time-delayed feedback adaptive intervention regulation method of any one of claims 1 to 5, characterized in that, The system comprises an establishing model module, an adding module, and a simulation module; The establishing model module is configured to establish a thalamocortical system model for simulating electroencephalogram signals in Alzheimer's disease state; The adding module is configured to add adaptive stimulation according to dynamic changes of the nervous system itself in a closed-loop control strategy of the thalamocortical system model; The simulation module is configured to simulate electroencephalogram signals generated by the thalamocortical system model when adaptive stimulation parameters change by changing the adaptive stimulation parameters.

7. The Alzheimer's disease time-delayed feedback adaptive intervention regulatory system according to claim 6, wherein, The system further comprises a display module; The display module is configured to use a frequency spectrum to show oscillation behavior of the simulated electroencephalogram signals in a preset frequency band.

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