Electroencephalogram monitoring system for neurology patient

Abstract

The invention provides an electroencephalogram monitoring system for neurology patients, and relates to the technical field of electroencephalogram monitoring, a signal noise reduction module is used for synchronously acquiring electroencephalogram signal flow and acceleration signals in real time, and performing dynamic noise reduction by taking pre-established quantum baseline features as constraint conditions to generate an electroencephalogram signal feature set; the optimization solving module is used for taking the electroencephalogram signal feature set as input, constructing an optimization model containing quantum Hamiltonian constraint conditions and solving the optimization model to generate an electroencephalogram signal feature set containing a weight coefficient; the sequence generation module is used for calculating brain network topology parameters through a sliding window on the basis of the electroencephalogram signal feature set containing the weight coefficient, and generating a sequential sequence of global efficiency and modular indexes; the quantum early warning module is used for synchronously executing quantum calibration and pathological early warning based on the time sequence, the electroencephalogram state of a patient can be monitored more accurately, and a more reliable basis is provided for neurology disease diagnosis and treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) monitoring, and in particular to an EEG monitoring system for neurology patients. Background Art

[0002] In the field of neurology, EEG monitoring is crucial for disease diagnosis, treatment, and rehabilitation assessment. Traditional EEG monitoring systems are susceptible to interference from various factors during signal acquisition, such as artifacts generated by the patient's head movement, which can degrade signal quality and affect the accuracy of subsequent analysis. Furthermore, traditional methods for processing EEG signals lack precision in extracting and analyzing signal features, making it difficult to fully tap into the complex information contained within them. Furthermore, the lack of effective early warning mechanisms during monitoring prevents the timely and accurate detection of potential pathological changes.

[0003] Therefore, it is necessary to provide an EEG monitoring system for neurological patients to solve the above technical problems. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides an EEG monitoring system for neurology patients. By empowering the EEG monitoring system with quantum technology, it achieves efficient signal noise reduction, accurate feature solution, dynamic monitoring of brain networks and timely warning of pathological risks, significantly improving the efficiency of EEG monitoring in neurology.

[0005] The present invention provides an electroencephalogram (EEG) monitoring system for neurology patients, the system comprising:

[0006] A signal denoising module is used to synchronously collect EEG signal streams and acceleration signals in real time, and perform dynamic denoising based on pre-established quantum baseline characteristics as constraints to generate an EEG signal feature set, wherein the EEG signal feature set includes oscillation index, coherence and peak frequency;

[0007] An optimization solution module, configured to take the EEG signal feature set as input, construct an optimization model including quantum Hamiltonian constraints, and generate an EEG signal feature set including weight coefficients through quantum annealing solution;

[0008] A sequence generation module is used to calculate brain network topology parameters through a sliding window based on the EEG signal feature set including weight coefficients, and generate a time series of global efficiency and modularity index;

[0009] The quantum early warning module is used to synchronously perform quantum calibration and pathology early warning based on the timing sequence.

[0010] Preferably, the signal noise reduction module is specifically used to:

[0011] generating, in real time, an acceleration signal stream that is spatiotemporally aligned with the EEG signal stream using a three-axis accelerometer integrated into the EEG headset;

[0012] Calculating an instantaneous motion intensity factor based on the acceleration signal stream, and determining a motion state according to a preset rule, wherein the motion state includes a static state, daily activities, pathological tremor, and high-intensity motion artifact;

[0013] A noise reduction mode is determined according to the motion state and noise reduction is performed to generate an EEG signal feature set.

[0014] Preferably, in the signal noise reduction module, the preset rules specifically include:

[0015] Based on the instantaneous motion intensity factor M t and acceleration power spectrum P to determine the motion state,

[0016] If M t ∈[0,0.1g), then the motion state is static, g is the acceleration due to gravity,

[0017] If M t ∈[0.1g,0.5g], and P does not exceed the preset ratio in the preset frequency band, then the motion state is daily activity,

[0018] If M t ∈[0.1g,0.5g], and P accounts for more than a preset proportion in the preset frequency band, then the movement state is pathological tremor,

[0019] If M t >0.5g, the motion state is a high-intensity motion artifact.

[0020] Preferably, in the signal noise reduction module, determining the noise reduction mode according to the motion state and performing noise reduction includes:

[0021] If the motion state is a stationary state, the baseline wavelet filter is enabled.

[0022] If the motion state is daily activity or high-intensity motion artifact, the motion compensation filter is activated.

[0023] If the motion state is pathological tremor, all filters are disabled in the preset frequency band.

[0024] In addition to the motion state of the pathological tremor, the quantum baseline characteristics are used as the constraint target to make the output signal meet the following requirements:

[0025]

[0026] Among them, β represents the oscillation index, θ represents the coherence, α represents the peak frequency, β0, θ0 and α0 represent the quantum baseline characteristics of the oscillation index, coherence and peak frequency, respectively.

[0027] Preferably, the optimization solution module is specifically used to:

[0028] Converting the oscillation index, coherence and peak frequency into a phase rotation quantum state, an amplitude modulation quantum state and a unitary transformation quantum state respectively;

[0029] Based on the determined motion state and EEG signal characteristics, a quantum Hamiltonian constraint model including dynamic pathological coupling strength is constructed;

[0030] Taking the quantum Hamiltonian model as input, the entanglement weight coefficient of each EEG signal feature is solved through a quantum-classical hybrid optimization process, and an EEG signal feature set with weight coefficients is output.

[0031] Preferably, the sequence generation module is specifically used to:

[0032] Taking the EEG signal feature set with weight coefficients as input, the oscillation index, coherence and peak frequency are mapped to the node attributes, connection edge weights and default network attributes of the brain network respectively;

[0033] The sliding window length is dynamically adjusted by the rate of change of the peak frequency with a weight coefficient, and the analysis period is divided into fixed time steps;

[0034] In each divided analysis period, the functional connectivity matrix is ​​calculated based on the connection edge weights, and the global efficiency and modularity index of the analysis period are solved accordingly;

[0035] The global efficiency and modularity index calculated at each analysis period are arranged in chronological order and output as a time series.

[0036] Preferably, in the quantum early warning module, the quantum calibration is to reconstruct the quantum Hamiltonian constraint when any of the following conditions is detected to be met:

[0037] Condition A: the global efficiency is continuously lower than a predefined efficiency threshold for a predefined duration, the oscillation index is higher than a predefined feature failure threshold, and the rate of change of the coherence is lower than a predefined coherence sensitivity threshold;

[0038] Condition B: The modularity index continuously decreases by more than a predefined degradation threshold for a predefined duration, and the drift of the peak frequency is lower than a predefined drift sensitivity threshold.

[0039] Preferably, in the quantum early warning module, the pathology early warning is to generate an early warning when any of the following conditions is detected to be met:

[0040] Condition C: the global efficiency is continuously lower than a predefined efficiency threshold for a predefined duration, the oscillation index is lower than a predefined pathological risk threshold, and the coherence is lower than a predefined coherence collapse threshold;

[0041] Condition D: the modularity index is continuously lower than a predefined modularity threshold for a predefined duration, and the drift of the peak frequency is higher than a predefined frequency shift threshold.

[0042] Compared with related technologies, the EEG monitoring system for neurology patients provided by the present invention has the following beneficial effects:

[0043] The signal noise reduction module of the present invention accurately reduces noise through multiple methods, effectively removes interference, and improves signal quality. The optimization solution module converts EEG signal characteristics into quantum states, constructs a more realistic model, and accurately solves weight coefficients. The sequence generation module dynamically adjusts the window, accurately calculates topological parameters, and outputs accurate time series. The quantum early warning module can perform quantum calibration and pathological early warning in a timely manner based on the time series, and detect potential risks in advance. These advantages enable the system to more accurately monitor the patient's EEG status, provide a more reliable basis for the diagnosis and treatment of neurological diseases, and improve treatment efficacy and patient recovery rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a module structure diagram of an EEG monitoring system for neurology patients provided by the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all of the structures. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with one another unless there is a conflict.

[0046] It should also be noted that, for ease of description, only the part relevant to the present invention, rather than all of the content, is shown in the accompanying drawings. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processing or methods depicted as flow charts. Although flow charts describe various operations (or steps) as sequential processing, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. When its operation is completed, the processing can be terminated, but can also have additional steps not included in the accompanying drawings. The processing can correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0047] The present invention provides an electroencephalogram (EEG) monitoring system for neurological patients. Figure 1 As shown, the system includes:

[0048] The signal noise reduction module is used to synchronously collect EEG signal streams and acceleration signals in real time, and perform dynamic noise reduction based on pre-established quantum baseline characteristics as constraints to generate an EEG signal feature set, where the EEG signal feature set includes oscillation index, coherence and peak frequency.

[0049] Specifically, during the implementation of the signal noise reduction module, the three-axis accelerometer integrated into the EEG headset first collects the acceleration signal stream in real time, which is strictly synchronized with the EEG signal. This ensures that the timing deviation of the two signals does not exceed 1ms, and the sampling rate is uniformly set to 1000Hz. For the acceleration signal, the system processes it according to the following process:

[0050] An instantaneous motion intensity factor is calculated based on the acceleration signal stream, and a motion state is determined according to a preset rule, wherein the motion state includes a static state, daily activities, pathological tremor, and high-intensity motion artifact.

[0051] In this embodiment, the system calculates the instantaneous motion intensity factor M every 0.1 seconds. t (Unit: g, unit of gravity acceleration), the calculation formula is:

[0052]

[0053] Where N = 100 (corresponding to a 0.1 second window), g0 = 9.8 m / s 2 is the gravitational acceleration constant, the calculation result is expressed in g units, t represents the current time point (sample index), k represents the summation index variable, from t to t+N-1, Indicates the acceleration value of the kth sample point in the x-axis direction (unit: m / s 2 ), Indicates the acceleration value of the kth sample point in the y-axis direction (unit: m / s 2 ), Indicates the acceleration value of the kth sample point in the z-axis direction (unit: m / s 2 ).

[0054] In addition, the acceleration power spectrum needs to be calculated, including:

[0055] The system performs power spectrum analysis every 2 seconds:

[0056] Take the acceleration data of a 2-second window (2000 samples) and apply Hanning window weighting;

[0057] Calculate the power spectrum P of each axis by fast Fourier transform x(f), P y (f) and P z (f);

[0058] Synthesized total power spectrum: P(f) = P x (f)+P y (f)+P z (f);

[0059] Calculate the proportion of the 3-8 Hz tremor characteristic frequency band η: (Nyquist frequency is 500 Hz when sampling rate is 1,000 Hz) as the acceleration power spectrum, where f is the frequency variable and df is the differential of frequency.

[0060] Then according to the instantaneous motion intensity factor M t And the proportion η, execution status classification, specifically:

[0061] If M t ∈[0,0.1g), then the motion state is static, and g is the acceleration due to gravity;

[0062] If M t ∈[0.1g,0.5g], and the proportion η of P in the preset frequency band does not exceed the preset proportion, then the exercise state is daily activity, where the preset proportion is 60%;

[0063] If M t ∈[0.1g,0.5g], and the proportion η of P in the preset frequency band exceeds the preset proportion, then the movement state is pathological tremor, and the preset proportion value is the same as above;

[0064] If M t >0.5g, the motion state is a high-intensity motion artifact.

[0065] A noise reduction mode is determined according to the motion state and noise reduction is performed to generate an EEG signal feature set.

[0066] Specifically, in the signal noise reduction module, determining the noise reduction mode according to the motion state and performing noise reduction is specifically as follows:

[0067] Static state: Enable Daubechies4 wavelet baseline filter, perform 5-layer decomposition, and apply threshold processing on the approximate coefficient layer to eliminate 0.5-4 Hz low-frequency drift;

[0068] Daily activities / high-intensity motion artifacts: Activate the adaptive motion compensation filter (NLMS algorithm), and the reference signal is M t , the update formula is:

[0069]

[0070] Where ω(n) represents the filter weight vector at the nth moment, n represents the discrete time index, μ represents the step size factor (set to 0.01), e(n) represents the error signal at the nth moment (i.e., the difference between the EEG signal and the filter output), and x(n) represents the reference input signal at the nth moment (i.e., the motion intensity factor M). t discrete sequence), ||x(n)|| represents the Euclidean norm of the reference input vector, and τ is a very small constant (set to 10 -6 , to prevent the denominator from being zero).

[0071] Pathological tremor: Completely disable the filter in the 3-8 Hz frequency band to preserve the original signal.

[0072] In non-pathological tremor states, the system monitors the following characteristics in real time and enforces constraints:

[0073] Oscillation index β: energy proportion in the 4-30 Hz frequency band, with the constraint |β-β0|≤0.2β0;

[0074] Coherence θ: the squared amplitude coherence of leads Fz-Cz in the 8-12 Hz frequency band, with the constraint |θ-θ0|≤0.2θ0;

[0075] Peak frequency α: the frequency corresponding to the maximum power in the α-band, with the constraint |α-α0|≤0.2α0;

[0076] In the above formula, β0, θ0 and α0 represent the quantum baseline characteristics of oscillation index, coherence and peak frequency, respectively.

[0077] When the eigenvalue exceeds the tolerance range, the filter parameters are automatically adjusted (such as increasing the wavelet threshold by 10%) and reprocessing is performed.

[0078] Finally, a set of EEG signal feature sets is output every 2 seconds, which includes three key parameters:

[0079] Oscillation index β: 0.0~1.0 scale value;

[0080] Coherence θ: 0.0~1.0 scale value;

[0081] Peak frequency α: a floating point value in Hz.

[0082] The optimization solution module is used to construct an optimization model containing quantum Hamiltonian constraints using the EEG signal feature set as input, and generate an EEG signal feature set containing weight coefficients through quantum annealing solution.

[0083] Specifically, the optimization solution module is specifically used to:

[0084] The oscillation index, coherence and peak frequency are converted into a phase rotation quantum state, an amplitude modulation quantum state and a unitary transformation quantum state respectively.

[0085] In this embodiment, the EEG features (oscillation index, coherence, peak frequency) and quantum baseline features output by the noise reduction module are received, and quantum state conversion is performed:

[0086] Oscillation index conversion: The β value is mapped to the quantum state |β> through phase rotation. The phase angle calculation formula is φ=π(β / β0), which reflects the dynamic fluctuations of motor cortical excitability.

[0087] Coherence conversion: The θ value is converted into an amplitude-modulated quantum state |θ>, and the rotation angle is determined by θ / θ0, which represents the change in the strength of neural synchronization.

[0088] Peak frequency conversion: Construct a unitary transformation matrix to act on the ground state |0> to generate |α>, with a rotation angle of 2π(α-α0), and record the vector characteristics of the spectrum drift.

[0089] Based on the determined motion state and EEG signal characteristics, a quantum Hamiltonian constraint model including dynamic pathological coupling strength is constructed.

[0090] In this embodiment, a pathology-driven quantum model is constructed based on quantum state and motion state labels:

[0091] Operator definition: Use Pauli operator quantum tunneling and energy confinement as core operators.

[0092] Pathological coupling mechanism: In the pathological tremor state, the β-θ coupling strength is set to 0.8 (strong coupling, corresponding to enhanced neural synchronization in Parkinson's tremor);

[0093] The other states are set to 0.2 (weak coupling).

[0094] The weights of each feature are: oscillation index weight 0.9 (motor cortex hypersensitivity), coherence weight 0.3, and peak frequency weight 0.1.

[0095] Constructing a dynamic Hamiltonian model Its expression is:

[0096]

[0097] in Represents the quantum tunneling intensity, which is used to control the quantum tunneling effect. (T = 20 ns is the total evolution time), represents the classical potential energy intensity, used to control the classical optimization weight, λ = 0.7 is the feature redundancy penalty factor, represents the Pauli x operator, represents the Pauli z operator, ω irepresents the weight of each EEG signal feature, C βθ It represents the pathological coupling coefficient. When the movement state is pathological tremor, its value is 0.8, and in other movement states it is 0.2.

[0098] Taking the quantum Hamiltonian model as input, the entanglement weight coefficient of each EEG signal feature is solved through a quantum-classical hybrid optimization process, and an EEG signal feature set with weight coefficients is output.

[0099] In this example, the optimization process employs a hybrid quantum-classical strategy: The first 50% of the time uses Hadamard quantum gates for global search, leveraging quantum tunneling to avoid local optima; the second 50% uses the Adam optimizer for gradient descent, achieving rapid convergence at a learning rate of 0.01. This segmented approach ensures the ability to capture sudden EEG events (such as epileptic discharges) while meeting the clinical real-time requirement of completing calculations within 200ms.

[0100] After optimization, the final quantum state is projected and compared with the standard Bell state. The quantum state measurement generates entanglement weight coefficients for the three features. These weights are applied to the original features using a differentiated weighting strategy: the oscillation index uses linear weighting to preserve the amplitude of motor cortical excitability, coherence uses square root weighting to enhance weak synchronization signals, and peak frequency uses sign function weighting to maintain the directional characteristics of spectral drift.

[0101] In the final output weighted feature set, the weighted oscillation index is passed to the subsequent modules as the core decision parameter.

[0102] The sequence generation module is used to calculate the brain network topology parameters through a sliding window based on the EEG signal feature set containing weight coefficients, and generate a time series of global efficiency and modularity index.

[0103] Specifically, the sequence generation module is specifically used to:

[0104] Taking the EEG signal feature set with weight coefficients as input, the oscillation index, coherence and peak frequency are mapped to the node attributes, connection edge weights and default network attributes of the brain network respectively.

[0105] In this embodiment, the weighted EEG feature set output by the optimization solution module is received, and the weighted oscillation index is assigned as a node attribute of the brain network, specifically mapped to the primary motor cortex area (Brodmann area 4), and its weight value directly characterizes the excitability level of motor cortical neurons; weighted coherence is used as the connection edge weight and assigned to the corpus callosum and thalamocortical loop, and the weight value reflects the intensity of information transmission between brain regions; weighted peak frequency is used as the default network attribute, marking the posterior cingulate gyrus as the core node, and its weight sign indicates the direction of spectrum drift (positive value means forward shift, negative value means backward shift). This mapping relationship is designed based on the pathological mechanism of Parkinson's disease movement disorder to ensure that the brain network construction is aligned with the clinical pathological characteristics.

[0106] The sliding window length is dynamically adjusted by the rate of change of the peak frequency with a weight coefficient, and the analysis period is divided into fixed time steps.

[0107] In this embodiment, the analysis window is adjusted in real time based on the weighted peak frequency rate of change: the frequency rate of change (in Hz / s) is calculated every 200 milliseconds. When the rate of change is greater than 2 Hz / s, it is determined to be a high-frequency state (such as an epileptic seizure), and the window is shortened to 0.5 seconds. When the rate of change is less than 0.5 Hz / s, it is determined to be a low-frequency state (such as Alzheimer's resting state), and the window is extended to 10 seconds. The window length for the normal state is dynamically calculated according to the formula 1 / |rate of change|, and the range is constrained to between 0.5 and 10 seconds. The fixed step size is set to 200 milliseconds (the minimum time unit for brain network reorganization). When pathological tremor is detected, the window is forcibly locked to 2 seconds to avoid motion artifact interference.

[0108] In each divided analysis period, the functional connectivity matrix is ​​calculated based on the connection edge weights, and the global efficiency and modularity index of the analysis period are solved accordingly.

[0109] In this embodiment, topological parameter calculation is performed within a dynamic window: first, a functional connectivity matrix is ​​constructed based on the 64-lead EEG topology, with edge weights = weighted coherence × physical distance attenuation factor. The matrix elements quantify the strength of functional connectivity between brain regions. Then, global efficiency is calculated by solving the shortest path length of all node pairs and taking the inverse global average. A value below 0.4 indicates deterioration in brain network integration (such as Parkinson's freezing of gait). Finally, the modularity index is calculated, and the Louvain algorithm is applied to detect community structure. An index value below 0.25 indicates weakened brain functional differentiation (such as early dementia). The matrix is ​​calibrated using a standard topological template at the first startup of the day, and the distance attenuation factor is dynamically adjusted based on the temperature of the headset.

[0110] The global efficiency and modularity index calculated at each analysis period are arranged in chronological order and output as a time series.

[0111] In this embodiment, the output data of each window is integrated: each window generates a triple containing a timestamp (UNIX millisecond value at the window start time), a global efficiency value (0.0-1.0), and a modularity index value (0.0-0.5). Pathological event labels are automatically added: when pathological tremor is detected, it is marked as a tremor event; when the global efficiency is less than 0.4, it is marked as a network integration anomaly. The final output is a JSON format time series sequence, with data strictly sorted by time, and 5 sets of data are generated per second (200 millisecond step size), providing high-resolution input for pathological early warning.

[0112] The quantum early warning module is used to synchronously perform quantum calibration and pathology early warning based on the timing sequence.

[0113] Specifically, the system continuously receives a stream of brain network parameter time series data (5 sets per second) output by the sequence generation module, tracking in real time the global efficiency value (an indicator of whole-brain information transmission efficiency, ranging from 0.0-1.0), the modularity index (the degree of separation of brain functional communities, ranging from 0.0-0.5), and pathological event labels (such as tremor event markers). The data is updated every 200 milliseconds, and the last 30 minutes of recordings are retained as a dynamic baseline. When a sudden change in parameters is detected, subsequent processing is activated.

[0114] Quantum calibration is triggered when the following conditions are met:

[0115] Condition A (network efficiency degradation): The global efficiency is lower than the threshold of 0.5 for 1 second (5 sets of data), the oscillation index is continuously higher than 0.8, and the coherence change rate is lower than 0.05 / s (indicating that the noise reduction function fails);

[0116] Condition B (modular structure degradation): The modularity index decreases by more than 0.1 for 2 consecutive seconds (10 sets of data), and the peak frequency drift is continuously lower than 0.3 Hz / s (reflecting brain network decoupling).

[0117] Calibration execution process: freeze the current data stream and reconstruct the quantum Hamiltonian model, increase the pathological coupling coefficient to 0.95 and the oscillation index weight to 0.95; reset the quantum baseline characteristics based on the last 5 minutes of valid data; send parameter update instructions to the noise reduction module (such as increasing the wavelet threshold by 20%).

[0118] A graded alert is generated when the following combined conditions are detected:

[0119] Condition C (Risk of Parkinson's disease): Global efficiency < 0.4 for 2 seconds, oscillation index < 0.15, and coherence < 0.25. Grading based on the number of conditions met: 1 condition → yellow warning (mild movement disorder), 2 conditions → orange warning (risk of freezing of gait), 3 conditions → red warning (loss of movement function);

[0120] Condition D (neurodegeneration risk): Modularity index < 0.25 for 4 seconds and peak frequency drift > 1.5 Hz / s. A 4-second duration indicates a yellow alert (early cognitive decline), while a 10-second duration indicates a red alert (brain network disintegration).

[0121] It is best to generate structured warning information: including timestamp, warning type (such as C3), severity level (red / orange / yellow), trigger parameter value and clinical treatment recommendations (such as immediate injection of levodopa). Through multi-channel output: the headset triggers vibration and red light warnings, the medical staff's PAD pop-up reminders, and the cloud is synchronized and pushed to the electronic medical record and family APP. Realize clinical treatment linkage: red warnings automatically call the emergency system, orange warnings adjust the dosage of the drug infusion pump, and yellow warnings initiate follow-up reminders. The whole process delay is less than 300ms, and reliability is guaranteed by motion artifact filtering (pausing the warning when a high-intensity motion tag is detected).

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0124] It should also be noted that the term "includes" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, product, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, product, or apparatus. In the absence of further limitations, an element defined by the phrase "includes a..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

Claims

1. An electroencephalogram (EEG) monitoring system for neurology patients, characterized in that: The system comprises: A signal denoising module is used to synchronously collect EEG signal streams and acceleration signals in real time, and perform dynamic denoising based on pre-established quantum baseline characteristics as constraints to generate an EEG signal feature set, wherein the EEG signal feature set includes oscillation index, coherence and peak frequency; An optimization solution module, configured to take the EEG signal feature set as input, construct an optimization model including quantum Hamiltonian constraints, and generate an EEG signal feature set including weight coefficients through quantum annealing solution; A sequence generation module is used to calculate brain network topology parameters through a sliding window based on the EEG signal feature set including weight coefficients, and generate a time series of global efficiency and modularity index; The quantum early warning module is used to synchronously perform quantum calibration and pathology early warning based on the timing sequence.

2. The EEG monitoring system for neurology patients according to claim 1, characterized in that: The signal noise reduction module is specifically used to: generating, in real time, an acceleration signal stream that is spatiotemporally aligned with the EEG signal stream using a three-axis accelerometer integrated into the EEG headset; Calculating an instantaneous motion intensity factor based on the acceleration signal stream, and determining a motion state according to a preset rule, wherein the motion state includes a static state, daily activities, pathological tremor, and high-intensity motion artifact; A noise reduction mode is determined according to the motion state and noise reduction is performed to generate an EEG signal feature set.

3. The EEG monitoring system for neurology patients according to claim 2, characterized in that: In the signal noise reduction module, the preset rules specifically include: Based on the instantaneous motion intensity factor M t and acceleration power spectrum P to determine the motion state, If M t ∈[0,0.1g), then the motion state is static, g is the acceleration due to gravity, If M t ∈[0.1g,0.5g], and P does not exceed the preset ratio in the preset frequency band, then the motion state is daily activity, If M t ∈[0.1g,0.5g], and P accounts for more than a preset proportion in the preset frequency band, then the movement state is pathological tremor, If M t >0.5g, the motion state is a high-intensity motion artifact.

4. The EEG monitoring system for neurology patients according to claim 3, characterized in that: In the signal noise reduction module, determining a noise reduction mode according to the motion state and performing noise reduction includes: If the motion state is a stationary state, the baseline wavelet filter is enabled. If the motion state is daily activity or high-intensity motion artifact, the motion compensation filter is activated. If the motion state is pathological tremor, all filters are disabled in the preset frequency band. In addition to the motion state of the pathological tremor, the quantum baseline characteristics are used as the constraint target to make the output signal meet the following requirements: Among them, β represents the oscillation index, θ represents the coherence, α represents the peak frequency, β0, θ0 and α0 represent the quantum baseline characteristics of the oscillation index, coherence and peak frequency, respectively.

5. The EEG monitoring system for neurology patients according to claim 4, characterized in that: The optimization solution module is specifically used to: Converting the oscillation index, coherence and peak frequency into a phase rotation quantum state, an amplitude modulation quantum state and a unitary transformation quantum state respectively; Based on the determined motion state and EEG signal characteristics, a quantum Hamiltonian constraint model including dynamic pathological coupling strength is constructed; Taking the quantum Hamiltonian model as input, the entanglement weight coefficient of each EEG signal feature is solved through a quantum-classical hybrid optimization process, and an EEG signal feature set with weight coefficients is output.

6. The EEG monitoring system for neurology patients according to claim 5, characterized in that: The sequence generation module is specifically used to: Taking the EEG signal feature set with weight coefficients as input, the oscillation index, coherence and peak frequency are mapped to the node attributes, connection edge weights and default network attributes of the brain network respectively; The sliding window length is dynamically adjusted by the rate of change of the peak frequency with a weight coefficient, and the analysis period is divided into fixed time steps; In each divided analysis period, the functional connectivity matrix is ​​calculated based on the connection edge weights, and the global efficiency and modularity index of the analysis period are solved accordingly; The global efficiency and modularity index calculated at each analysis period are arranged in chronological order and output as a time series.

7. The EEG monitoring system for neurology patients according to claim 6, characterized in that: In the quantum early warning module, the quantum calibration is to reconstruct the quantum Hamiltonian constraint when any of the following conditions is detected to be met: Condition A: the global efficiency is continuously lower than a predefined efficiency threshold for a predefined duration, the oscillation index is higher than a predefined feature failure threshold, and the rate of change of the coherence is lower than a predefined coherence sensitivity threshold; Condition B: The modularity index continuously decreases by more than a predefined degradation threshold for a predefined duration, and the drift of the peak frequency is lower than a predefined drift sensitivity threshold.

8. The EEG monitoring system for neurology patients according to claim 1, characterized in that: In the quantum early warning module, the pathological early warning is generated when any of the following conditions is detected: Condition C: the global efficiency is continuously lower than a predefined efficiency threshold for a predefined duration, the oscillation index is lower than a predefined pathological risk threshold, and the coherence is lower than a predefined coherence collapse threshold; Condition D: the modularity index is continuously lower than a predefined modularity threshold for a predefined duration, and the drift of the peak frequency is higher than a predefined frequency shift threshold.

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