Metabolic rhythm synchronization closed-loop neural rhythm signal regulation and control device and system

Abstract

The invention belongs to the technical field of human neuron signal processing and regulation and control, and provides a metabolic rhythm synchronization closed-loop neural rhythm signal regulation and control device and system, and the technical scheme is that the metabolic rhythm synchronization closed-loop neural rhythm signal regulation and control device comprises a sensor probe and a signal processing module, and the sensor probe is connected with the signal processing module; the signal processing module is configured to collect a multi-modal metabolic rhythm signal, introduce a multi-modal metabolic rhythm signal adjusting factor, perform coupling degree analysis on the multi-modal metabolic rhythm signal, and determine the coupling degree of the multi-modal metabolic rhythm signal based on the multi-modal metabolic rhythm signal adjusting factor and the coupling degree analysis result. And optimizing the biological signal intensity signal in combination with the constructed multi-objective optimization framework and the constraint condition to obtain the optimized biological signal intensity. And the regulation and control signal can be generated more accurately.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of human neuron signal processing and regulation, and particularly relates to a metabolic rhythm synchronization closed-loop neural rhythm signal regulation device and system. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] In the treatment of sleep fragmentation related to aging, the traditional neural rhythm signal regulation system uses single mode stimulation, which cannot coordinate the metabolic coupling rhythm of neurons-astrocytes, for example, only targeting neurons such as GABA drugs, ignoring the support of glial cells on synaptic plasticity and metabolism, and long-term use may lead to tolerance; only adjusting metabolism, without synchronizing the rhythm of neuronal electrical activity, the effect is limited.

[0004] At the same time, the traditional deep brain stimulation (DBS) electrode implantation may interfere with local metabolism through mechanical damage or electrical stimulation, leading to accumulation of lactic acid, and further damaging the metabolic coupling of neurons-astrocytes; In addition, the existing neural rhythm signal regulation system mainly targets neuronal electrical activity, and lacks a phase locking mechanism for sleep cerebrospinal fluid pulse waves, but sleep cerebrospinal fluid dynamics disorder will exacerbate sleep fragmentation and metabolic waste accumulation, and such limitations are particularly prominent in aging or neurodegenerative diseases, because sleep cerebrospinal fluid dynamics disorder will exacerbate sleep fragmentation and metabolic waste accumulation, and the generated rhythm regulation signal does not meet the actual demand. SUMMARY

[0005] In order to solve at least one of the technical problems existing in the background art, the present application provides a metabolic rhythm synchronization closed-loop neural rhythm signal regulation system and method, which considers multi-modal stimulation and realizes the regulation of cerebrospinal fluid pulse through a multi-rhythm regulation system.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions: The first aspect of the present application provides a metabolic rhythm synchronization closed-loop neural rhythm signal regulation device, comprising a sensor probe and a signal processing module, the sensor probe and the signal processing module being connected; The signal processing module is configured to: collect multi-modal metabolic rhythm signals, introduce multi-modal metabolic rhythm signal regulation factors, analyze the coupling degree of the multi-modal metabolic rhythm signals, and based on the multi-modal metabolic rhythm signal regulation factors and the coupling degree analysis results, combine the constructed multi-objective optimization framework and the constraint conditions to optimize the biological signal intensity signal to obtain the optimized biological signal intensity.

[0007] Further, in the signal processing module, the calculation formula of the coupling degree analysis of the multi-modal metabolic rhythm signal is: , Wherein, is the signal coupling degree coefficient, is the covariance of lactate concentration and cerebrospinal fluid pulse, unit: μM, is the standard deviation of cerebrospinal fluid pulse amplitude, unit: mmHg; represents the covariance of lactate concentration and cerebrospinal fluid pulse amplitude.

[0008] Further, in the signal processing module, when the signal coupling degree is greater than the set threshold value, the weight coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted through fuzzy logic, including: Divide the fuzzy data set of the multi-modal metabolic rhythm signal into different fuzzy states; Determine all trigger rules according to the fuzzy state of the input variable, calculate the rule trigger intensity under each trigger rule, combine the rule trigger intensity, and determine the initial weight coefficient corresponding in the rule base, combine the rule trigger intensity and the initial weight coefficient to calculate the weight coefficient update amount corresponding to the trigger rule, and then weight average the weight coefficient update amount of all trigger rules to obtain the updated weight value.

[0009] Further, in the signal processing module, the calculation formula of the updated weight value is: , , , Wherein, represents the weight coefficient corresponding to the updated lactate correction function, represents the weight coefficient corresponding to the lactate correction function before updating, represents the weight coefficient corresponding to the updated cerebrospinal fluid pulse amplitude correction function, represents the weight coefficient corresponding to the cerebrospinal fluid pulse amplitude correction function before updating, and respectively represent the weighted average value of the weight coefficient updated by the rule trigger intensity; and represent the weight coefficient based on the lactate concentration C lac The trigger intensity of the fuzzy rule and the weight coefficient based on the cerebrospinal fluid pulse amplitude A CSF The trigger intensity of the fuzzy rule, is the number of activated rules, which is calculated by dividing the fuzzy set of the input variable, represents the initial weight coefficient corresponding to the lactate concentration correction function, The initial weight coefficient corresponding to the cerebrospinal fluid pulse amplitude correction function is represented.

[0010] Further, in the signal processing module, the multi-objective optimization function established is: , Wherein, The square norm of the derivative torque of the neural signal reflects the synchrony of the neural rhythm; specifically, Calculated from the second derivative of the local field potential, The stimulation energy consumption estimation function is in quadratic relationship with stimulation parameters such as amplitude, frequency and pulse width; The tissue pH value, Wherein, The current tissue pH value, The baseline; The adjustment weight of the energy consumption target, wherein, Calculated dynamically from the lactic acid concentration And the cerebrospinal fluid pulse amplitude ; , And The empirical value, The adjustment weight of pH stability.

[0011] Further, in the signal processing module, the optimized biological signal intensity is: , Wherein, The lactic acid correction function is expressed as: , The cerebrospinal fluid pulse gain factor is expressed as: , Wherein, , The weight coefficient corresponding to the lactic acid correction function and the cerebrospinal fluid pulse gain factor, The lactic acid concentration, μM, The cerebrospinal fluid pulse amplitude, mmHg.

[0012] The second aspect of the present application provides a metabolic rhythm synchronization closed-loop neural rhythm signal regulation system, comprising the metabolic rhythm synchronization closed-loop neural rhythm signal regulation device of the first aspect.

[0013] The third aspect of the present application provides a computer readable storage medium.

[0014] A computer readable storage medium having a computer program stored thereon, the program being executed by a processor to perform the following steps: acquiring a multi-modal metabolic rhythm signal; introducing a multi-modal metabolic rhythm signal regulating factor, and performing coupling degree analysis on the multi-modal metabolic rhythm signal; based on the multi-modal metabolic rhythm signal regulating factor and the coupling degree analysis result, combining a constructed multi-target optimization framework and constraint conditions to optimize the biological signal intensity signal to obtain an optimized biological signal intensity.

[0015] A fourth aspect of the application provides a computer device.

[0016] A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the following steps: acquiring a multi-modal metabolic rhythm signal; introducing a multi-modal metabolic rhythm signal regulating factor, and performing coupling degree analysis on the multi-modal metabolic rhythm signal; based on the multi-modal metabolic rhythm signal regulating factor and the coupling degree analysis result, combining a constructed multi-target optimization framework and constraint conditions to optimize the biological signal intensity signal to obtain an optimized biological signal intensity.

[0017] A fifth aspect of the application provides a program product.

[0018] A program product, which is a computer program product, comprising a computer program, wherein the computer program is executed by a processor to perform the following steps: acquiring a multi-modal metabolic rhythm signal; introducing a multi-modal metabolic rhythm signal regulating factor, and performing coupling degree analysis on the multi-modal metabolic rhythm signal; based on the multi-modal metabolic rhythm signal regulating factor and the coupling degree analysis result, combining a constructed multi-target optimization framework and constraint conditions to optimize the biological signal intensity signal to obtain an optimized biological signal intensity.

[0019] Compared with the prior art, the application has the following beneficial effects: Based on the acquired multi-modal metabolic rhythm signal, the multi-modal metabolic rhythm signal regulating factor is introduced, the coupling degree analysis is performed on the multi-modal metabolic rhythm signal, the control parameters in the dynamic regulation equation are corrected based on the multi-modal metabolic rhythm signal regulating factor and the coupling degree analysis result, and the regulation signal can be generated more accurately.

[0020] The advantages of the additional aspects of the application will be partially given in the following description, partially will become obvious from the following description, or will be understood by the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application and are incorporated by reference in their entirety. The embodiments depicted herein are provided by way of example and are not meant to limit the application.

[0022] Figure 1 is a block diagram of a multi-modal coupled closed-loop electrical stimulation sleep co-signal modulation device provided by an embodiment of the application; Figure 2 is a flowchart of a multi-modal coupled closed-loop electrical stimulation sleep co-signal modulation method provided by an embodiment of the application. DETAILED DESCRIPTION

[0023] The application will be further described with reference to the drawings and embodiments.

[0024] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, 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 belongs.

[0025] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0026] Embodiment One As shown in Figure 1 and Figure 2 , the present embodiment provides a metabolic rhythm synchronization closed-loop neural rhythm signal modulation device, comprising: a sensor probe and a signal processing module, the sensor probe and the signal processing module are connected; In the present embodiment, the sensor probe specifically comprises an electrochemical sensor, a pressure sensor, a pH response sensor and a microelectrode probe; Specifically, the electrochemical sensor adopts a Pt-Sn / LDH sensor for collecting real-time lactic acid concentration data ; The pressure sensor adopts an epidural pressure sensor for collecting cerebrospinal fluid pulse amplitude; The pH response sensor adopts an IrOx micro sensor for collecting tissue pH value; The microelectrode probe is used to collect gamma oscillation power, such as 30-80 Hz LFP energy, reflecting neural cluster synchronicity.

[0027] The signal processing module comprises a signal acquisition module, a signal preprocessing module, a dynamic parameter adjustment module and a multi-target optimization module. The signal acquisition module is configured to acquire the multi-modal metabolic rhythm signals. In this embodiment, the multi-modal metabolic rhythm signals comprise real-time lactic acid concentration data , cerebrospinal fluid pulse amplitude data , tissue pH value and gamma oscillation power. Specifically, when the real-time lactic acid concentration is acquired, it is acquired by a Pt-Sn / LDH sensor, and the principle of acquisition is as follows: The Pt-Sn / LDH is an electrochemical catalytic material, which generates a current signal related to the concentration by oxidizing lactic acid; based on the LDH substrate, the Pt-Sn nanoparticles provide high specific surface area and stability, and enhance the dispersibility of the Pt-Sn nanoparticles, which act as catalytic active centers, efficiently oxidize lactic acid, and the generated electrons form a current signal, the strength of which is proportional to the lactic acid concentration.

[0028] Specifically, when the cerebrospinal fluid pulse amplitude is acquired, it is acquired by an epidural pressure sensor, and the specific principle is as follows: The pressure sensor fixed in the epidural space performs time domain or frequency domain analysis based on the acquired pressure data to obtain the amplitude of the signal; If time domain analysis is used, the pressure waveform in a single cardiac cycle is identified, the amplitude of each cycle is calculated, and the average value of a plurality of cycles such as 10-20 times is taken as the final amplitude; If frequency domain analysis is used, the amplitude of the main frequency component (usually corresponding to the heart rate) is extracted by Fourier transform (FFT) analysis of the pressure signal spectrum.

[0029] The signal preprocessing module is configured to preprocess the acquired multi-modal metabolic rhythm signals to obtain preprocessed multi-modal metabolic rhythm signals. Specifically, the signal preprocessing module is configured to eliminate physiological noise interference signals by time domain analysis or frequency domain analysis. In this embodiment, the multi-cycle amplitude averaging method can be used for time domain analysis, and the Fourier transform method can be used for frequency domain analysis.

[0030] Further, the lactic acid concentration acquired by the Pt-Sn / LDH sensor is converted into an electrochemical signal; after the tissue pH value is acquired by the IrOx sensor, it is processed by wavelet denoising and sliding window correction to convert it into a standardized pH value, which can be calculated by the formula: pH = 3.21V + 7.0; the gamma oscillation power is calculated by 30-80Hz band-pass filtering and Hilbert transform to integrate the instantaneous amplitude, and finally output as a logarithmic compressed power value.

[0031] a dynamic parameter adjustment module, which is used to introduce a multi-modal metabolic rhythm signal adjustment factor, to perform coupling degree analysis on the multi-modal metabolic rhythm signal, to correct a control parameter in a dynamic adjustment equation in combination with the multi-modal metabolic rhythm signal adjustment factor and the coupling degree analysis result, and to obtain a biological signal intensity dynamic adjustment equation; The original biological signal intensity adjustment equation directly obtains the final biological signal intensity by weighted summation according to the weight proportion of each modal signal, and cannot dynamically adjust according to the correlation between the signals, and the expression is as follows: (1), wherein, is the corrected biological signal intensity, is a current biological signal intensity reference value, is a stimulation intensity safety constraint parameter, is a target metabolic rhythm synchronization degree, is an actual metabolic rhythm synchronization degree, The embodiment comprehensively considers the collected multi-modal metabolic rhythm signals, introduces a lactic acid concentration correction function and a cerebrospinal fluid pulse gain factor, corrects the control parameter in the dynamic adjustment equation, and the corrected dynamic adjustment equation is as follows: (2), wherein, is the lactic acid correction function, and the expression is as follows: (3), is the cerebrospinal fluid pulse gain factor, and the expression is as follows: (4), wherein, , is a weight coefficient corresponding to the lactic acid correction function and the cerebrospinal fluid pulse gain factor, is the lactic acid concentration, μM, is the cerebrospinal fluid pulse amplitude, mmHg.

[0032] In the dynamic parameter adjustment module, when the obtained lactic acid concentration data and cerebrospinal fluid pulse data are subjected to coupling degree analysis, the specific signal coupling degree calculation formula is as follows: (5), wherein, is the covariance of the lactic acid concentration and the cerebrospinal fluid pulse, The calculation formula of is , the unit is μM, is the standard deviation of the cerebrospinal fluid pulse amplitude, The calculation formula of is , the unit is mmHg; represents the covariance of lactate concentration and CSF pulse amplitude; is the signal coupling degree coefficient, ranging from 0 to 1, representing the synergy of lactate concentration and CSF pulse, represents no correlation; is the mean value of lactate concentration, is the mean value of CSF pulse amplitude. When the signal coupling degree is greater than a set threshold value, such as 0.6, the weight of the introduced multi-modal metabolic rhythm signal adjustment factor , is adjusted. The specific adaptive parameter adjustment method adjusts the weight , through a fuzzy logic controller. The specific adjustment process includes the following steps: Step 1: divide the fuzzy data set of the multi-modal metabolic rhythm signal into different fuzzy states; specifically, respectively divide the signal coupling degree , lactate concentration and CSF pulse amplitude into fuzzy sets; Among them, the signal coupling degree is divided into weak coupling set, medium coupling set and strong coupling set by using triangular membership function. Specifically, in this embodiment, the signal coupling degree is defined as a weak coupling set with a value range of (0-0.4), the signal coupling degree is defined as a trapezoidal membership function with a value range of (0.4-0.7), and the signal coupling degree is defined as a strong coupling set with a value range of (0.7-1); Among them, the lactate concentration is divided into low, normal and high three fuzzy sets by using trapezoidal membership function; Specifically, in this embodiment, the lactate concentration is defined as a low fuzzy set with a value range of less than 2 μM, the lactate concentration is defined as a normal fuzzy set with a value range of (2-4 μM), and the lactate concentration is defined as a high fuzzy set with a value range of greater than 4 μM; Among them, the CSF pulse amplitude is divided into weak pulse, medium pulse and strong pulse by using Gaussian membership function; Specifically, in this embodiment, the CSF pulse amplitude is defined as a weak pulse with a value less than 15 mmHg, the CSF pulse amplitude is defined as a medium pulse with a value range of (15-30 μM), and the CSF pulse amplitude is defined as a strong pulse with a value range greater than 30 mmHg.

[0033] For example, when When the corresponding fuzzy set is the signal coupling degree For medium coupling, lactate concentration is a high fuzzy set, the cerebrospinal fluid pulse amplitude For medium pulse; The pH value is divided into three fuzzy sets: acidic (<7.0), normal (7.0-7.4), and alkaline (>7.4); the gamma power is divided into three fuzzy sets: low (<6dB), medium (6-12dB), and high (>12dB); Step 2: Determine the rule to be activated based on the fuzzy state of the input variable, and update the weight value based on the trigger strength corresponding to the rule to be activated and the weight value corresponding to the trigger strength in the rule base, combining the weight value and the trigger strength to obtain an updated weight value; In this embodiment, all triggering rules are determined based on the fuzzy state of the input variables, the rule triggering strength under each triggering rule is calculated, the rule triggering strength is combined, and the corresponding initial weight coefficient in the rule base is determined. The weight coefficient update amount corresponding to the triggering rule is calculated by combining the rule triggering strength and the initial weight coefficient. The updated weight coefficient amounts of all triggering rules are then weighted averaged to obtain the updated weight value. It should be noted that the fuzzy state of the input variable and the triggering rules are preset according to the requirements. For example, when (15-30mmHg) when the "Rule, in this embodiment, not all presets are described one by one, and can be set according to actual needs; Among them, when calculating the rule triggering strength, the corresponding rule triggering strength is calculated according to the real-time measured input variables under each fuzzy set and the corresponding membership function; for example, when When , it belongs to the “medium pulse” fuzzy set , which can be calculated by Gaussian membership function; Take a trigger rule as an example to illustrate the updating process of weight coefficient; For example, when (15-30mmHg) when the "rule, find the trigger strength of the rule at this time, if the trigger strength is , and the rule base , then the rule base is The contribution of .

[0034] Further, and The value of is updated through the rule-triggered intensity weighting. The specific update formula is: (6), (7), (8), in, Represents the weight coefficient corresponding to the updated lactate correction function, Indicates the weight coefficient corresponding to the lactate correction function before updating, represents the weight coefficient corresponding to the updated cerebrospinal fluid pulse amplitude correction function, represents the weight coefficient corresponding to the cerebrospinal fluid pulse amplitude correction function before updating, and They represent the weighted average values ​​of the weight coefficients after the rule trigger strength is updated; and Indicates the lactate concentration C lac The trigger intensity of fuzzy rules and the amplitude of cerebrospinal fluid pulse A CSF The triggering strength of fuzzy rules, The number of activation rules is determined by input variables (signal coupling r, lactate concentration C lac , CSF pulse amplitude A CSF ) is calculated by fuzzy set partitioning, represents the initial weight coefficient corresponding to the lactate concentration correction function, represents the initial weight coefficient corresponding to the cerebrospinal fluid pulse amplitude correction function; In this embodiment, when the signal coupling degree When it is greater than 0.8, The adjustment amplitude is attenuated by 50%, when the cerebrospinal fluid pulse amplitude When it is greater than 30 mmHg, The gain factor is fixed at 2.3; Step 3: Constrain the adjusted weight values; In this embodiment, The value range is [0.1, 0, 6]. If it exceeds this range, it will trigger an alarm log record; The value range is [0.05, 0, 4]. If the same direction adjustment is made three times in a row, the step size increases by 20%.

[0035] A multi-objective optimization module is used to optimize the bio-signal intensity signal by combining the bio-signal intensity dynamic adjustment equation and the constructed multi-objective optimization framework and constraint conditions to obtain the optimized bio-signal intensity; In this embodiment, the established multi-objective optimization function is: (9), in, is the square norm of the neural signal derivative torque, reflecting the synchronization of neural rhythm; specifically, calculated from the second derivative of the local field potential, as a quadratic function of stimulation parameters such as amplitude, frequency and pulse width; as the tissue pH, wherein, as the current tissue pH, as the baseline; as the adjustment weight of energy expenditure target, wherein, calculated from the lactate concentration and the cerebrospinal fluid pulse amplitude dynamically; wherein, and as empirical values, preferably, , , adjusted by the fuzzy logic controller to adjust , ; ; When adjusting , first calculate the membership of each fuzzy set according to the real-time measured pH and gamma power values (e.g. the membership of normal set is 0.8 when pH = 7.1), take the minimum value of the membership of the intersection condition as the rule trigger strength, then weight average the adjustment amount of all triggered rules: , for example, when , (and ) and , (and ) are triggered at the same time, the final ; In this embodiment, the value range of constraint condition parameters include: gamma oscillation power constraint: This constraint ensures that the gamma oscillation power maintains the minimum threshold required for cognitive function, and below the threshold, associative memory declines; tissue pH constraint: This constraint is a physiological safety range, and exceeding this range will activate an emergency compensation mechanism; stimulation intensity physiological safety range constraint: The upper limit of the stimulation intensity of this constraint can be determined based on the cortical heat loss threshold experiment.

[0036] ​MOEA / D algorithm (population size 50, iteration 200 times) is used to solve the Pareto optimal solution set, while meeting physiological constraints such as gamma oscillation >= 8dB, pH 7.0-7.6, stimulation intensity <= 2.3mA; finally, the optimal solution is modified again through the feedback compensation layer, for example, when pH < 7.2, the stimulation intensity is automatically reduced by 15% and the alkaline buffer is triggered, or when the gamma power continues to exceed the standard, the frequency is increased by 2Hz to suppress excessive synchronization, forming a complete closed-loop control chain of "dynamic pre-adjustment-multi-objective optimization-physiological compensation".

[0037] Based on the obtained multi-modal metabolic rhythm signal, a multi-modal metabolic rhythm signal regulating factor is introduced, the coupling degree of the multi-modal metabolic rhythm signal is analyzed, and the control parameters in the dynamic adjustment equation are modified according to the multi-modal metabolic rhythm signal regulating factor and the coupling degree analysis result, so that the regulation signal can be generated more accurately.

[0038] Embodiment two The embodiment provides a metabolic rhythm synchronization closed-loop neural rhythm signal regulation system, which comprises the metabolic rhythm synchronization closed-loop neural rhythm signal regulation device as described in embodiment one.

[0039] It should be noted that the specific implementation mode of the metabolic rhythm synchronization closed-loop neural rhythm signal regulation device system of the embodiment of the present application is similar to that of the metabolic rhythm synchronization closed-loop neural rhythm signal regulation device of the embodiment of the present application, and specific reference can be made to the description of the device part. In order to reduce redundancy, this part will not be repeated here.

[0040] Embodiment three The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the following steps: Collecting multi-modal metabolic rhythm signals; Introducing a multi-modal metabolic rhythm signal regulating factor to analyze the coupling degree of the multi-modal metabolic rhythm signal; Based on the multi-modal metabolic rhythm signal regulating factor and the coupling degree analysis result, the biological signal intensity signal is optimized to obtain an optimized biological signal intensity signal by combining the constructed multi-objective optimization framework and the constraint condition.

[0041] Embodiment four The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the following steps when executing the program: Collecting multi-modal metabolic rhythm signals; Introducing a multi-modal metabolic rhythm signal regulating factor to analyze the coupling degree of the multi-modal metabolic rhythm signal; Based on the multi-modal metabolic rhythm signal regulation factor and the coupling degree analysis result, the biological signal intensity signal is optimized based on a multi-objective optimization framework and constraint conditions to obtain an optimized biological signal intensity.

[0042] Embodiment five The embodiment provides a program product, which is a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the following steps: Collecting a multi-modal metabolic rhythm signal; Introducing a multi-modal metabolic rhythm signal regulation factor, and performing coupling degree analysis on the multi-modal metabolic rhythm signal; Based on the multi-modal metabolic rhythm signal regulation factor and the coupling degree analysis result, the biological signal intensity signal is optimized based on a multi-objective optimization framework and constraint conditions to obtain an optimized biological signal intensity.

[0043] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.

[0044] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0045] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0046] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operational steps are performed on the computer or other programmable data processing device to generate a computer-implemented process, thus the instructions executed on the computer or other programmable data processing device provide the function of implementing the processes specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 one block or multiple blocks.

[0047] Those of ordinary skill in the art can understand that all or part of the flow of the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the flow of the above-mentioned embodiment of each method. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), and the like.

[0048] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A closed-loop neural rhythm signal control device for synchronizing metabolic rhythms, characterized in that: It includes a sensor probe and a signal processing module, wherein the sensor probe and the signal processing module are connected; The signal processing module is configured to: collect multimodal metabolic rhythm signals, introduce multimodal metabolic rhythm signal regulation factors, perform coupling analysis on the multimodal metabolic rhythm signals, and optimize the biological signal intensity signal based on the multimodal metabolic rhythm signal regulation factors and coupling analysis results in combination with the constructed multi-objective optimization framework and constraint conditions to obtain the optimized biological signal intensity.

2. The metabolic rhythm synchronized closed-loop neural rhythm signal control device according to claim 1, characterized in that: In the signal processing module, the calculation formula for coupling analysis of multimodal metabolic rhythm signals is: ， in, is the signal coupling coefficient, is the covariance between lactate concentration and cerebrospinal fluid pulse, in μM, is the standard deviation of the cerebrospinal fluid pulse amplitude, in mmHg; represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude.

3. The metabolic rhythm synchronization closed-loop neural rhythm signal control device according to claim 1, characterized in that: In the signal processing module, when the signal coupling degree is greater than the set threshold, the weight coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted through fuzzy logic, including: The fuzzy dataset of multimodal metabolic rhythm signals is divided into different fuzzy states; Determine all trigger rules based on the fuzzy state of the input variables, calculate the rule trigger strength under each trigger rule, combine the rule trigger strength, and determine the corresponding initial weight coefficient in the rule base, combine the rule trigger strength and the initial weight coefficient to calculate the weight coefficient update amount corresponding to the trigger rule, and then take the weighted average of the weight coefficient update amounts of all trigger rules to obtain the updated weight value.

4. The metabolic rhythm synchronized closed-loop neural rhythm signal control device according to claim 3, characterized in that: In the signal processing module, the calculation formula for the updated weight value is: ， ， ， in, Represents the weight coefficient corresponding to the updated lactate correction function, Indicates the weight coefficient corresponding to the lactate correction function before updating, represents the weight coefficient corresponding to the updated cerebrospinal fluid pulse amplitude correction function, represents the weight coefficient corresponding to the cerebrospinal fluid pulse amplitude correction function before updating, and They represent the weighted average values ​​of the weight coefficients after the rule trigger strength is updated; and Indicates the lactate concentration C lac The trigger intensity of fuzzy rules and the amplitude of cerebrospinal fluid pulse A CSF The triggering strength of fuzzy rules, is the number of activated rules, which is calculated by fuzzy set partitioning of input variables. represents the initial weight coefficient corresponding to the lactate concentration correction function, Represents the initial weight coefficient corresponding to the cerebrospinal fluid pulse amplitude correction function.

5. The metabolic rhythm synchronized closed-loop neural rhythm signal control device according to claim 3, characterized in that: In the signal processing module, the multi-objective optimization function is established as follows: ， in, is the square norm of the neural signal derivative torque, reflecting the synchronization of neural rhythm; specifically, Calculated from the second derivative of the local field potential, is the stimulation energy consumption estimation function, which has a quadratic relationship with stimulation parameters such as amplitude, frequency and pulse width; is the tissue pH, ,in, is the current tissue pH, is the baseline; is the adjustment weight of the energy consumption target, where By lactate concentration and CSF pulse amplitude Dynamic calculation; , and is the experience value, is the adjustment weight for pH stability.

6. The metabolic rhythm synchronized closed-loop neural rhythm signal control device according to claim 3, characterized in that: In the signal processing module, the optimized biological signal strength is: ， in, is the lactate correction function, expressed as: , is the cerebrospinal fluid pulse gain factor, expressed as: ， in, 、 is the weight coefficient corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor, is the lactate concentration, μM, is the cerebrospinal fluid pulse amplitude, mmHg.

7. A metabolic rhythm synchronized closed-loop neural rhythm signal control system, characterized in that: It comprises the metabolic rhythm synchronized closed-loop neural rhythm signal control device as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the following steps are implemented: Collect multimodal metabolic rhythm signals; Introducing multimodal metabolic rhythm signal regulatory factors and conducting coupling analysis on multimodal metabolic rhythm signals; Based on the results of multimodal metabolic rhythm signal regulation factors and coupling analysis, the biological signal intensity signal is optimized in combination with the constructed multi-objective optimization framework and constraint conditions to obtain the optimized biological signal intensity.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the following steps are implemented: Collect multimodal metabolic rhythm signals; Introducing multimodal metabolic rhythm signal regulatory factors and conducting coupling analysis on multimodal metabolic rhythm signals; Based on the results of multimodal metabolic rhythm signal regulation factors and coupling analysis, the biological signal intensity signal is optimized in combination with the constructed multi-objective optimization framework and constraint conditions to obtain the optimized biological signal intensity.

10. A program product, wherein the program product is a computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, the following steps are implemented: Collect multimodal metabolic rhythm signals; Introducing multimodal metabolic rhythm signal regulatory factors and conducting coupling analysis on multimodal metabolic rhythm signals; Based on the results of multimodal metabolic rhythm signal regulation factors and coupling analysis, the biological signal intensity signal is optimized in combination with the constructed multi-objective optimization framework and constraint conditions to obtain the optimized biological signal intensity.

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