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

Abstract

The present application belongs to the technical field of human neuron signal processing and regulation, and provides a metabolic rhythm synchronization closed-loop nerve rhythm signal regulation device and system, which comprises 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 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 a constructed multi-objective optimization framework and constraint conditions to optimize the biological signal intensity signal to obtain an optimized biological signal intensity. The regulation signal can be generated more accurately.

Description

Technical Field

[0001] This invention belongs to the field of human neuron signal processing and regulation technology, and particularly relates to a closed-loop neural rhythm signal regulation device and system for synchronizing metabolic rhythms. Background Technology

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

[0003] In the treatment of age-related sleep fragmentation, traditional neural rhythm signal modulation systems use single-modality stimulation, which cannot coordinate the metabolic coupling rhythm of neurons and astrocytes. For example, targeting only neurons, such as GABAergic drugs, ignores the support of glial cells for synaptic plasticity and metabolism, and long-term use may lead to tolerance; merely regulating metabolism without synchronizing the rhythm of neuronal electrical activity has limited effect.

[0004] Meanwhile, the use of traditional deep brain stimulation (DBS) electrode implantation may interfere with local metabolism through mechanical damage or electrical stimulation, leading to lactic acid accumulation and thus disrupting the metabolic coupling between neurons and astrocytes.

[0005] In addition, existing neural rhythm signal regulation systems mainly target neuronal electrical activity and lack a phase-locking mechanism for cerebrospinal fluid pulse waves during sleep. However, cerebrospinal fluid dynamic disturbances during sleep can exacerbate sleep fragmentation and the accumulation of metabolic waste. This limitation is particularly prominent in aging or neurodegenerative diseases because cerebrospinal fluid dynamic disturbances during sleep can exacerbate sleep fragmentation and the accumulation of metabolic waste, resulting in rhythm regulation signals that do not meet actual needs. Summary of the Invention

[0006] To address at least one of the technical problems mentioned above, this invention provides a closed-loop neural rhythm signal modulation system and method for synchronizing metabolic rhythms, which considers multimodal stimulation and achieves modulation of cerebrospinal fluid pulses through a multi-rhythm modulation system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides a closed-loop neural rhythm signal modulation device for metabolic rhythm synchronization, comprising a sensor probe and a signal processing module, wherein the sensor probe and the signal processing module are connected together.

[0009] The signal processing module is configured to: acquire multimodal metabolic rhythm signals, introduce multimodal metabolic rhythm signal regulation factors, perform coupling degree analysis on multimodal metabolic rhythm signals, and optimize the biological signal intensity based on the multimodal metabolic rhythm signal regulation factors and coupling degree analysis results, combined with the constructed multi-objective optimization framework and constraints, to obtain the optimized biological signal intensity.

[0010] Furthermore, in the signal processing module, the formula for calculating the coupling degree of multimodal metabolic rhythm signals is as follows:

[0011] ,

[0012] in, The signal coupling coefficient, The value represents the covariance between lactate concentration and cerebrospinal fluid pulses, in μM. The standard deviation of cerebrospinal fluid pulse amplitude is expressed in mmHg. This represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude.

[0013] Furthermore, in the signal processing module, when the signal coupling degree exceeds a set threshold, the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted using fuzzy logic, including:

[0014] The fuzzy dataset of multimodal metabolic rhythm signals is divided into different fuzzy states;

[0015] All triggering rules are determined based on the fuzzy state of the input variables. The triggering intensity of each triggering rule is calculated. The triggering intensity is combined with the initial weight coefficient in the rule base. The weight coefficient update amount corresponding to the triggering rule is calculated by combining the triggering intensity and the initial weight coefficient. The updated weight value is obtained by weighted averaging of the weight coefficient updates of all triggering rules.

[0016] Furthermore, in the signal processing module, the formula for calculating the updated weight values ​​is:

[0017] ,

[0018] ,

[0019] ,

[0020] in, This represents the weighting coefficients corresponding to the updated lactate correction function. This represents the weighting coefficients corresponding to the lactate correction function before the update. This represents the weighting coefficients corresponding to the updated cerebrospinal fluid pulse amplitude correction function. This represents the weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function before the update. and These represent the weighted average of the weight coefficients after the intensity update is triggered by the rule; and Indicated based on lactic acid concentration C lacTriggering strength of fuzzy rules and cerebrospinal fluid pulse amplitude A CSF The trigger strength of fuzzy rules The activation rule number is calculated through fuzzy set partitioning of the input variables. This represents the initial weighting coefficients corresponding to the lactic acid concentration correction function. This represents the initial weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function.

[0021] Furthermore, in the signal processing module, the established multi-objective optimization function is as follows:

[0022] ,

[0023] in, This is the square norm of the derivative torque of the neural signal, reflecting the synchronicity of the neural rhythm; specifically, Calculated from the second derivative of the local field potential, The stimulus energy consumption estimation function is quadratically related to stimulus parameters such as amplitude, frequency, and pulse width. For tissue pH value, ,in, The current tissue pH value, As the baseline; The adjustment weights for energy consumption targets, where, Based on lactic acid concentration and cerebrospinal fluid pulse amplitude Dynamic calculation; , and Based on experience points. This represents the adjustment weight for pH stability.

[0024] Furthermore, in the signal processing module, the optimized biosignal intensity is:

[0025] ,

[0026] in, The lactate correction function is expressed as follows:

[0027] ,

[0028] The cerebrospinal fluid pulse gain factor is expressed as follows:

[0029] ,

[0030] in, , These are the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg.

[0031] A second aspect of the present invention provides a metabolic rhythm synchronization closed-loop neural rhythm signal modulation system, including the metabolic rhythm synchronization closed-loop neural rhythm signal modulation device described in the first aspect.

[0032] A third aspect of the present invention provides a computer-readable storage medium.

[0033] A computer-readable storage medium having a computer program stored thereon, the program being executed by a processor as follows:

[0034] Acquire multimodal metabolic rhythm signals;

[0035] Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals;

[0036] Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0037] A fourth aspect of the present invention provides a computer device.

[0038] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor performs the following steps when executing the program:

[0039] Acquire multimodal metabolic rhythm signals;

[0040] Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals;

[0041] Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0042] A fifth aspect of the present invention provides a program product.

[0043] A program product, which is a computer program product, includes a computer program that is executed by a processor in the following steps:

[0044] Acquire multimodal metabolic rhythm signals;

[0045] Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals;

[0046] Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This invention, based on the acquired multimodal metabolic rhythm signals, introduces multimodal metabolic rhythm signal regulation factors, performs coupling degree analysis on the multimodal metabolic rhythm signals, and combines the multimodal metabolic rhythm signal regulation factors and coupling degree analysis results to correct the control parameters in the dynamic regulation equation, thereby generating regulatory signals more accurately.

[0049] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0050] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0051] Figure 1 This is a block diagram of the multimodal coupled closed-loop electrical stimulation sleep coordination signal modulation device provided in an embodiment of the present invention;

[0052] Figure 2 This is a flowchart of the multimodal coupled closed-loop electrical stimulation sleep synergistic signal modulation method provided in the embodiments of the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. 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 invention pertains.

[0055] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0056] Example 1

[0057] like Figure 1 and Figure 2 As shown, this embodiment provides a closed-loop neural rhythm signal modulation device for metabolic rhythm synchronization, including: a sensor probe and a signal processing module, wherein the sensor probe and the signal processing module are connected.

[0058] In this embodiment, the sensor probe specifically includes an electrochemical sensor, a pressure sensor, a pH response sensor, and a microelectrode probe;

[0059] Specifically, the electrochemical sensor is a Pt-Sn / LDH sensor used to acquire real-time lactic acid concentration data. ;

[0060] The pressure sensor is an epidural pressure sensor used to collect the amplitude of cerebrospinal fluid pulses;

[0061] The pH response sensor uses an IrOx microsensor to collect tissue pH values;

[0062] The microelectrode probe is used to collect gamma oscillation power, such as 30-80 Hz LFP energy, to reflect neural cluster synchronization.

[0063] The signal processing module includes a signal acquisition module, a signal preprocessing module, a dynamic parameter adjustment module, and a multi-objective optimization module;

[0064] Among them, the signal acquisition module is used to collect multimodal metabolic rhythm signals;

[0065] In this embodiment, the multimodal metabolic rhythm signal includes real-time lactate concentration data. Cerebrospinal fluid pulse amplitude data Tissue pH and gamma oscillation power;

[0066] Specifically, the real-time lactic acid concentration is obtained using a Pt-Sn / LDH sensor, and the principle behind this acquisition is as follows:

[0067] Pt-Sn / LDH is an electrochemical catalytic material that generates a concentration-dependent current signal by oxidizing lactic acid. Based on the LDH substrate, it provides a high specific surface area and stability, enhancing the dispersibility of Pt-Sn nanoparticles. Pt-Sn nanoparticles, as catalytic active centers, efficiently oxidize lactic acid, and the generated electrons form a current signal whose intensity is proportional to the lactic acid concentration.

[0068] Specifically, the cerebrospinal fluid pulse amplitude is acquired via an epidural pressure sensor, and the specific principle is as follows:

[0069] The amplitude of the signal is obtained by performing time-domain or frequency-domain analysis on the acquired pressure data using a pressure sensor fixed to the epidural cavity.

[0070] If time-domain analysis is used, the pressure waveform within a single cardiac cycle can be identified, the amplitude of each cycle can be calculated, and the average value of multiple cycles, such as 10 to 20 cycles, can be taken as the final amplitude.

[0071] If frequency domain analysis is used, the amplitude of the dominant frequency component (usually corresponding to heart rate) can be extracted by analyzing the spectrum of the pressure signal through Fourier transform (FFT).

[0072] The signal preprocessing module is used to preprocess the acquired multimodal metabolic rhythm signals to obtain preprocessed multimodal metabolic rhythm signals;

[0073] Specifically, the signal preprocessing module is configured to eliminate physiological noise interference signals by using time-domain analysis or frequency-domain analysis;

[0074] In this embodiment, time-domain analysis can be performed using the multi-period amplitude averaging method; frequency-domain analysis can be performed using the Fourier transform method.

[0075] Furthermore, the lactic acid concentration is obtained through a Pt-Sn / LDH sensor and converted into an electrochemical signal; the tissue pH value is acquired by an IrOx sensor, and after wavelet denoising and sliding window correction, it is converted into a standardized pH value, which can be calculated using the formula: pH = 3.21V + 7.0; the γ oscillation power is calculated by the instantaneous amplitude integral through 30-80Hz bandpass filtering and Hilbert transform, and the final output is a logarithmically compressed power value.

[0076] The dynamic parameter adjustment module is used to introduce multimodal metabolic rhythm signal regulation factors, perform coupling degree analysis on multimodal metabolic rhythm signals, and modify the control parameters in the dynamic regulation equation by combining the multimodal metabolic rhythm signal regulation factors and coupling degree analysis results to obtain the dynamic adjustment equation of biological signal intensity.

[0077] The original biosignal intensity adjustment equation directly calculates the final biosignal intensity by weighting and summing the signals according to their respective weight ratios. It cannot dynamically adjust based on the correlation between the signals. The formula is as follows:

[0078] (1),

[0079] in, The corrected biosignal intensity. This is the current baseline value for biosignal intensity. For the safety constraint parameters of stimulus intensity, To achieve the desired degree of synchronization of metabolic rhythms, The degree of synchronization of actual metabolic rhythms;

[0080] This embodiment comprehensively considers the acquired multimodal metabolic rhythm signals and introduces a lactate concentration correction function and a cerebrospinal fluid pulse gain factor to modify the control parameters in the dynamic adjustment equation. The modified dynamic adjustment equation is as follows:

[0081] (2),

[0082] in, The lactate correction function is expressed as follows:

[0083] (3),

[0084] The cerebrospinal fluid pulse gain factor is expressed as follows:

[0085] (4),

[0086] in, , These are the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg.

[0087] In the dynamic parameter adjustment module, when performing coupling analysis on the acquired lactate concentration data and cerebrospinal fluid pulse data, the specific formula for calculating the signal coupling degree is as follows:

[0088] (5),

[0089] in, The covariance between lactate concentration and cerebrospinal fluid impulse. The calculation formula is: The unit is μM. The standard deviation of cerebrospinal fluid pulse amplitude. The calculation formula is: The unit is mmHg; This represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude. The signal coupling coefficient, ranging from 0 to 1, characterizes the synergistic effect between lactate concentration and cerebrospinal fluid impulses. This indicates no correlation; This represents the average lactic acid concentration. This represents the average amplitude of cerebrospinal fluid pulses. When the signal coupling degree... When the value exceeds a set threshold, such as 0.6, the weight of the factor regulating the multimodal metabolic rhythm signal is adjusted. , Adjustments are made, specifically through a fuzzy logic controller to adjust the weights. , The adjustment process includes the following steps: Step 1: Divide the fuzzy dataset of multimodal metabolic rhythm signals into different fuzzy states; specifically, this includes adjusting the signal coupling degree... lactic acid concentration and cerebrospinal fluid pulse amplitude Perform fuzzy set partitioning;

[0090] Among them, signal coupling degree The fuzzy set is divided into weakly coupled, moderately coupled, and strongly coupled sets using a triangular membership function. Specifically, in this embodiment, the signal coupling degree is... The range of values ​​(0-0.4) is defined as a weakly coupled set, which determines the signal coupling degree. The value range is (0.4-0.7), defined as a trapezoidal membership function, which determines the signal coupling degree. A set with values ​​in the range (0.7-1) is defined as a strongly coupled set;

[0091] Among them, lactic acid concentration The fuzzy set is divided into three categories—low, normal, and high—using a trapezoidal membership function. Specifically, in this embodiment, the lactic acid concentration is... Values ​​within the range of less than 2 μM are defined as low-fuzzy sets, and lactic acid concentration is used as an example. The value range (2-4 μM) is defined as a normal fuzzy set, and the lactic acid concentration is... Values ​​greater than 4μM are defined as highly fuzzy sets;

[0092] Among them, cerebrospinal fluid pulse amplitude The fuzzy set is divided into weak pulses, medium pulses, and strong pulses using a Gaussian membership function; specifically, in this embodiment, the cerebrospinal fluid pulse amplitude is... A pulse less than 15 mmHg is defined as a weak pulse, and the amplitude of the cerebrospinal fluid pulse is... A value range of (15-30 μM) is defined as a medium pulse, and the amplitude of the cerebrospinal fluid pulse is... A value greater than 30 mmHg is defined as a strong pulse.

[0093] For example, when When the corresponding fuzzy set is the signal coupling degree, For moderately coupled sets, lactic acid concentration For highly fuzzy sets, cerebrospinal fluid pulse amplitude Medium pulse;

[0094] pH values ​​were divided into three fuzzy sets: acidic (<7.0), normal (7.0-7.4), and alkaline (>7.4); γ power was divided into three fuzzy sets: low (<6dB), medium (6-12dB), and high (>12dB).

[0095] 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 of the rule to be activated and the weight value corresponding to the trigger strength in the rule base.

[0096] In this embodiment, all triggering rules are determined based on the fuzzy state of the input variables, the triggering intensity of each triggering rule is calculated, the triggering intensity is combined with the initial weight coefficient in the rule base is determined, the triggering intensity and the initial weight coefficient are combined to calculate the weight coefficient update amount corresponding to the triggering rule, and then the weight coefficient update amounts of all triggering rules are weighted and averaged to obtain the updated weight value.

[0097] It should be noted that the fuzzy state and triggering rules of the input variables are preset according to requirements, for example, when Activate " (15-30 mmHg)" "Rules: In this embodiment, not all presets will be explained one by one. They can be set according to actual needs."

[0098] Specifically, when calculating the rule trigger strength, the corresponding rule trigger strength is calculated based on the real-time measured input variables under each fuzzy set and their corresponding membership functions; for example, when At that time, it belongs to the "medium-pulse" fuzzy set. It can be calculated using the Gaussian membership function;

[0099] The process of updating the weight coefficient is illustrated using a triggering rule as an example.

[0100] For example, when Activate " (15-30 mmHg)" "Rule, find the rule trigger strength at this time, such as trigger strength is..." And the rule base Then the rule base will be for The contribution is .

[0101] Furthermore, and The value is updated using a weighted update triggered by rules, and the specific update formula is as follows:

[0102] (6),

[0103] (7),

[0104] (8),

[0105] in, This represents the weighting coefficients corresponding to the updated lactate correction function. This represents the weighting coefficients corresponding to the lactate correction function before the update. This represents the weighting coefficients corresponding to the updated cerebrospinal fluid pulse amplitude correction function. This represents the weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function before the update. and These represent the weighted average of the weight coefficients after the intensity update is triggered by the rule; and Indicated based on lactic acid concentration C lac Triggering strength of fuzzy rules and cerebrospinal fluid pulse amplitude A CSF The trigger strength of fuzzy rules To activate the rule number, input variables (signal coupling degree r, lactate concentration C) are used. lac Cerebrospinal fluid pulse amplitude A CSF The fuzzy set partitioning is calculated to obtain the result. This represents the initial weighting coefficients corresponding to the lactic acid concentration correction function. This represents the initial weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function;

[0106] In this embodiment, when the signal coupling degree When it is greater than 0.8, The adjustment amplitude is reduced by 50%, when the cerebrospinal fluid pulse amplitude When it is greater than 30 mmHg, The gain factor is fixed at 2.3;

[0107] Step 3: Constrain the adjusted weight values;

[0108] In this embodiment, The value range is [0.1, 0, 6]. If the value exceeds this range, an alarm will be triggered and recorded in the log.

[0109] The value range is [0.05, 0, 4]. If the step size is increased by 20% after three consecutive adjustments in the same direction.

[0110] The multi-objective optimization module is used to optimize the biosignal intensity by combining the dynamic adjustment equation of biosignal intensity with the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0111] In this embodiment, the established multi-objective optimization function is:

[0112] (9),

[0113] in, This is the square norm of the derivative torque of the neural signal, reflecting the synchronicity of the neural rhythm; specifically, Calculated from the second derivative of the local field potential, The stimulus energy consumption estimation function is quadratically related to stimulus parameters such as amplitude, frequency, and pulse width. For tissue pH value, ,in, The current tissue pH value, As the baseline; The adjustment weights for energy consumption targets, where, Based on lactic acid concentration and cerebrospinal fluid pulse amplitude Dynamic calculation; ,in, and As an empirical value, preferably, , ,

[0114] Weights are controlled using a fuzzy logic controller. , Adjustments are made accordingly. ;

[0115] Adjustment First, the membership degree of each fuzzy set is calculated based on the real-time measured pH and γ power values ​​(e.g., the membership degree to the normal set is 0.8 when pH=7.1). The minimum membership degree of the cross condition is taken as the rule triggering strength. Then, for all triggering rules... Weighted average of adjustment amounts: For example, triggering simultaneously , ( )and ( When, finally ;

[0116] In this embodiment, The value range is [0.1, 0.6];

[0117] The constraint parameters include:

[0118] γ oscillation power constraint: This constraint ensures that the gamma oscillation power maintains the minimum threshold required for cognitive function, below which memory decline is associated.

[0119] Tissue pH constraint: This constraint is within the physiological safety range; exceeding this range will activate the emergency compensation mechanism.

[0120] Stimulation intensity within a physiologically safe range: The upper limit of the stimulus intensity under this constraint can be determined based on cortical heat loss threshold experiments.

[0121] The MOEA / D algorithm (population size 50, 200 iterations) was used to solve for the Pareto optimal solution set, while satisfying physiological constraints such as γ oscillation ≥8dB, pH 7.0-7.6, and stimulation intensity ≤2.3mA. Finally, the optimal solution was modified a second time through a feedback compensation layer. For example, when pH <7.2, the stimulation intensity was automatically reduced by 15% and an alkaline buffer was triggered, or when the γ power was continuously exceeded, a 2Hz frequency was added to suppress excessive synchronization, forming a complete closed-loop regulatory chain of "dynamic pre-regulation - multi-objective optimization - physiological compensation".

[0122] This invention, based on the acquired multimodal metabolic rhythm signals, introduces multimodal metabolic rhythm signal regulation factors, performs coupling degree analysis on the multimodal metabolic rhythm signals, and combines the multimodal metabolic rhythm signal regulation factors and coupling degree analysis results to correct the control parameters in the dynamic regulation equation, thereby generating regulatory signals more accurately.

[0123] Example 2

[0124] This embodiment provides a metabolic rhythm synchronization closed-loop neural rhythm signal modulation system, including the metabolic rhythm synchronization closed-loop neural rhythm signal modulation device as described in Embodiment 1.

[0125] It should be noted that the specific implementation of the metabolic rhythm synchronization closed-loop neural rhythm signal modulation device system in this embodiment of the invention is similar to the specific implementation of the metabolic rhythm synchronization closed-loop neural rhythm signal modulation device in this embodiment of the invention. For details, please refer to the description of the device section. To reduce redundancy, it will not be repeated here.

[0126] Example 3

[0127] This embodiment provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, performs the following steps:

[0128] Acquire multimodal metabolic rhythm signals;

[0129] Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals;

[0130] Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0131] Example 4

[0132] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps:

[0133] Acquire multimodal metabolic rhythm signals;

[0134] Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals;

[0135] Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0136] Example 5

[0137] This embodiment provides a program product, which is a computer program product, including a computer program. When the computer program is executed by a processor, it performs the following steps:

[0138] Acquire multimodal metabolic rhythm signals;

[0139] Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals;

[0140] Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity.

[0141] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0142] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A closed-loop neural rhythm signal modulation device for synchronizing metabolic rhythms, characterized in that, It includes a sensor probe and a signal processing module, which are connected to each other; The signal processing module is configured to: acquire multimodal metabolic rhythm signals, introduce multimodal metabolic rhythm signal regulation factors, perform coupling degree analysis on multimodal metabolic rhythm signals, and optimize the biological signal intensity signal based on the multimodal metabolic rhythm signal regulation factors and coupling degree analysis results, combined with the constructed multi-objective optimization framework and constraints, to obtain the optimized biological signal intensity. In the signal processing module, the formula for calculating the coupling degree of multimodal metabolic rhythm signals is as follows: ， in, The signal coupling coefficient, The value represents the covariance between lactate concentration and cerebrospinal fluid pulses, in μM. The standard deviation of cerebrospinal fluid pulse amplitude is expressed in mmHg. This represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg. In the signal processing module, when the signal coupling degree exceeds a set threshold, the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted using fuzzy logic, including: The fuzzy dataset of multimodal metabolic rhythm signals is divided into different fuzzy states; All triggering rules are determined based on the fuzzy state of the input variables. The triggering intensity of each triggering rule is calculated. The triggering intensity is combined with the initial weight coefficient in the rule base. The weight coefficient update amount corresponding to the triggering rule is calculated by combining the triggering intensity and the initial weight coefficient. The updated weight value is obtained by weighted averaging the weight coefficient update amounts of all triggering rules. In the signal processing module, the formula for calculating the updated weight values ​​is: ， ， ， in, This represents the weighting coefficients corresponding to the updated lactate correction function. This represents the weighting coefficients corresponding to the lactate correction function before the update. This represents the weighting coefficients corresponding to the updated cerebrospinal fluid pulse amplitude correction function. This represents the weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function before the update. and These represent the weighted average of the weight coefficients after the intensity update is triggered by the rule; and Indicated based on lactic acid concentration C lac Triggering strength of fuzzy rules and cerebrospinal fluid pulse amplitude A CSF The trigger strength of fuzzy rules The activation rule number is calculated by partitioning the input variables into fuzzy sets. This represents the initial weighting coefficients corresponding to the lactic acid concentration correction function. This represents the initial weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function.

2. The metabolic rhythm synchronization closed-loop neural rhythm signal modulation device as described in claim 1, characterized in that, In the signal processing module, the established multi-objective optimization function is as follows: ， in, This is the square norm of the derivative torque of the neural signal, reflecting the synchronicity of the neural rhythm; specifically, Calculated from the second derivative of the local field potential, The stimulus energy consumption estimation function is quadratically related to stimulus parameters such as amplitude, frequency, and pulse width. For tissue pH value, ,in, The current pH value of the tissue. As the baseline; The adjustment weights for energy consumption targets, where, Based on lactic acid concentration and cerebrospinal fluid pulse amplitude Dynamic calculation; , and Based on experience points. As the adjustment weight for pH stability, , These are the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor.

3. The metabolic rhythm synchronization closed-loop neural rhythm signal modulation device as described in claim 1, characterized in that, In the signal processing module, the optimized biosignal intensity is: ， in, The lactate correction function is expressed as follows: , The cerebrospinal fluid pulse gain factor is expressed as follows: ， in, The corrected biosignal intensity. This is the current baseline value for biosignal intensity. To achieve the desired degree of synchronization of metabolic rhythms, The degree of synchronization of actual metabolic rhythms, , These are the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg.

4. A closed-loop neural rhythm signal regulation system for synchronizing metabolic rhythms, characterized in that, Includes the metabolic rhythm synchronization closed-loop neural rhythm signal modulation device as described in any one of claims 1-3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the following steps: Acquire multimodal metabolic rhythm signals; Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals; Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity; The formula for calculating the coupling degree of multimodal metabolic rhythm signals is as follows: ， in, The signal coupling coefficient, The value represents the covariance between lactate concentration and cerebrospinal fluid pulses, in μM. The standard deviation of cerebrospinal fluid pulse amplitude is expressed in mmHg. This represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg. When the signal coupling degree exceeds a set threshold, the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted using fuzzy logic, including: The fuzzy dataset of multimodal metabolic rhythm signals is divided into different fuzzy states; All triggering rules are determined based on the fuzzy state of the input variables. The triggering intensity of each triggering rule is calculated. The triggering intensity is combined with the initial weight coefficient in the rule base. The weight coefficient update amount corresponding to the triggering rule is calculated by combining the triggering intensity and the initial weight coefficient. The updated weight value is obtained by weighted averaging the weight coefficient update amounts of all triggering rules. The updated formula for calculating the weight values ​​is: ， ， ， in, This represents the weighting coefficients corresponding to the updated lactate correction function. This represents the weighting coefficients corresponding to the lactate correction function before the update. This represents the weighting coefficients corresponding to the updated cerebrospinal fluid pulse amplitude correction function. This represents the weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function before the update. and These represent the weighted average of the weight coefficients after the intensity update is triggered by the rule; and Indicated based on lactic acid concentration Triggering strength of fuzzy rules and cerebrospinal fluid pulse amplitude The trigger strength of fuzzy rules The activation rule number is calculated by partitioning the input variables into fuzzy sets. This represents the initial weighting coefficients corresponding to the lactic acid concentration correction function. This represents the initial weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it performs the following steps: Acquire multimodal metabolic rhythm signals; Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals; Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity; The formula for calculating the coupling degree of multimodal metabolic rhythm signals is as follows: ， in, The signal coupling coefficient, The value represents the covariance between lactate concentration and cerebrospinal fluid pulses, in μM. The standard deviation of cerebrospinal fluid pulse amplitude is expressed in mmHg. This represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg. When the signal coupling degree exceeds a set threshold, the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted using fuzzy logic, including: The fuzzy dataset of multimodal metabolic rhythm signals is divided into different fuzzy states; All triggering rules are determined based on the fuzzy state of the input variables. The triggering intensity of each triggering rule is calculated. The triggering intensity is combined with the initial weight coefficient in the rule base. The weight coefficient update amount corresponding to the triggering rule is calculated by combining the triggering intensity and the initial weight coefficient. The updated weight value is obtained by weighted averaging the weight coefficient update amounts of all triggering rules. The updated formula for calculating the weight values ​​is: ， ， ， in, This represents the weighting coefficients corresponding to the updated lactate correction function. This represents the weighting coefficients corresponding to the lactate correction function before the update. This represents the weighting coefficients corresponding to the updated cerebrospinal fluid pulse amplitude correction function. This represents the weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function before the update. and These represent the weighted average of the weight coefficients after the intensity update is triggered by the rule; and Indicated based on lactic acid concentration Triggering strength of fuzzy rules and cerebrospinal fluid pulse amplitude The trigger strength of fuzzy rules The activation rule number is calculated by partitioning the input variables into fuzzy sets. This represents the initial weighting coefficients corresponding to the lactic acid concentration correction function. This represents the initial weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function.

7. A program product, said program product being a computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it performs the following steps: Acquire multimodal metabolic rhythm signals; Multimodal metabolic rhythm signal regulation factors are introduced, and coupling degree analysis is performed on multimodal metabolic rhythm signals; Based on the analysis results of multimodal metabolic rhythm signal regulation factors and coupling degree, the biosignal intensity is optimized by combining the constructed multi-objective optimization framework and constraints to obtain the optimized biosignal intensity; The formula for calculating the coupling degree of multimodal metabolic rhythm signals is as follows: ， in, The signal coupling coefficient, The value represents the covariance between lactate concentration and cerebrospinal fluid pulses, in μM. The standard deviation of cerebrospinal fluid pulse amplitude is expressed in mmHg. This represents the covariance between lactate concentration and cerebrospinal fluid pulse amplitude. Lactic acid concentration, μM. The amplitude of the cerebrospinal fluid pulse is measured in mmHg. When the signal coupling degree exceeds a set threshold, the weighting coefficients corresponding to the lactate correction function and the cerebrospinal fluid pulse gain factor are adjusted using fuzzy logic, including: The fuzzy dataset of multimodal metabolic rhythm signals is divided into different fuzzy states; All triggering rules are determined based on the fuzzy state of the input variables. The triggering intensity of each triggering rule is calculated. The triggering intensity is combined with the initial weight coefficient in the rule base. The weight coefficient update amount corresponding to the triggering rule is calculated by combining the triggering intensity and the initial weight coefficient. The updated weight value is obtained by weighted averaging the weight coefficient update amounts of all triggering rules. The updated formula for calculating the weight values ​​is: ， ， ， in, This represents the weighting coefficients corresponding to the updated lactate correction function. This represents the weighting coefficients corresponding to the lactate correction function before the update. This represents the weighting coefficients corresponding to the updated cerebrospinal fluid pulse amplitude correction function. This represents the weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function before the update. and These represent the weighted average of the weight coefficients after the intensity update is triggered by the rule; and Indicated based on lactic acid concentration Triggering strength of fuzzy rules and cerebrospinal fluid pulse amplitude The trigger strength of fuzzy rules The activation rule number is calculated by partitioning the input variables into fuzzy sets. This represents the initial weighting coefficients corresponding to the lactic acid concentration correction function. This represents the initial weighting coefficients corresponding to the cerebrospinal fluid pulse amplitude correction function.

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