A risk assessment method and system for Alzheimer's disease

Abstract

A risk assessment method and system for Alzheimer's disease, relating to the field of data processing technology. The method includes: obtaining brain magnetic resonance image data of a target user; performing time-frequency analysis on the EEG parameters in the brain magnetic resonance image data to determine the EEG spectrum curve; constructing a head motion trajectory curve based on the head posture parameters in the brain magnetic resonance image data; and generating a neuron activity curve based on the brain blood flow parameters in the brain magnetic resonance image data; determining multiple target feature points; calculating a first risk value corresponding to each target feature point in the EEG spectrum curve, a second risk value corresponding to the head motion trajectory curve, and a third risk value corresponding to the neuron activity curve; and combining the first, second, and third risk values to determine the Alzheimer's disease risk assessment result. Implementing the technical solution provided by this application achieves the effect of improving the accuracy of Alzheimer's disease risk assessment.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a risk assessment method, system, electronic device, and storage medium for Alzheimer's disease. Background Art

[0002] With the advancement of medical technology, early diagnosis has become a key link in the management of many neurodegenerative diseases. In particular, in the field of Alzheimer's disease research, the development of early diagnosis technology is of great significance for the prevention and treatment of the disease.

[0003] Currently, existing Alzheimer's disease risk assessment methods primarily acquire data through methods such as electroencephalograms (EEGs) and assess risk based on this single-dimensional data. However, in practice, because Alzheimer's disease risk is influenced by multiple parameters, risk assessment based solely on single-dimensional data often lacks comprehensive consideration of multi-source data, making it difficult to comprehensively assess a patient's risk status. This results in low accuracy in Alzheimer's disease risk assessments. Summary of the Invention

[0004] The present application provides a risk assessment method and system for Alzheimer's disease, which has the effect of improving the accuracy of risk assessment for Alzheimer's disease.

[0005] In a first aspect, the present application provides a method for risk assessment of Alzheimer's disease, comprising:

[0006] Obtaining brain magnetic resonance image data of the target user;

[0007] performing time-frequency analysis on brain wave parameters in the brain magnetic resonance image data to determine a brain wave spectrum curve of the target user, constructing a head motion trajectory curve of the target user based on head posture parameters in the brain magnetic resonance image data, and generating a neuron activity curve of the target user based on brain blood flow parameters in the brain magnetic resonance image data;

[0008] Determining a plurality of target feature points in the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve;

[0009] Calculating a first risk value corresponding to each target feature point in the brain wave spectrum curve, a second risk value corresponding to each target feature point in the head movement trajectory curve, and a third risk value corresponding to each target feature point in the neuron activity curve;

[0010] The first risk value, the second risk value, and the third risk value are combined to determine an Alzheimer's disease risk assessment result of the target user.

[0011] In a second aspect of the present application, a risk assessment system for Alzheimer's disease is provided, the system comprising:

[0012] A data acquisition module, used to acquire brain magnetic resonance image data of a target user;

[0013] a curve determination module, configured to perform time-frequency analysis on the EEG parameters in the brain MRI data to determine the EEG spectrum curve of the target user, construct a head motion trajectory curve of the target user based on the head posture parameters in the brain MRI data, and generate a neuron activity curve of the target user based on the brain blood flow parameters in the brain MRI data;

[0014] a risk value calculation module, configured to determine a plurality of target feature points in the EEG spectrum curve, the head motion trajectory curve, and the neuron activity curve; and calculate a first risk value corresponding to each target feature point in the EEG spectrum curve, a second risk value corresponding to each target feature point in the head motion trajectory curve, and a third risk value corresponding to each target feature point in the neuron activity curve;

[0015] The risk assessment module is configured to determine an Alzheimer's disease risk assessment result of the target user by combining the first risk value, the second risk value, and the third risk value.

[0016] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a risk assessment method for Alzheimer's disease when loaded and executed by the processor.

[0017] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a risk assessment method for Alzheimer's disease.

[0018] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] By employing the above technical solution, a comprehensive analysis and risk assessment of target users' brain magnetic resonance imaging data is performed, enabling accurate identification and quantitative assessment of early-stage Alzheimer's disease risk. By performing time-frequency analysis of EEG parameters to generate EEG spectrum curves, the system accurately reflects the electrical activity characteristics of brain neurons and cognitive function. By analyzing head posture parameters to construct head motion trajectory curves, the system effectively captures subtle changes in motor control ability. By analyzing cerebral blood flow parameters to generate neuronal activity curves, the system intuitively reflects the energy metabolism and functional status of brain tissue. This multi-dimensional feature extraction approach ensures comprehensive assessment metrics. During the feature analysis phase, key pathological changes are accurately located by identifying target feature points within each curve. The degree of abnormality is quantitatively characterized by calculating the primary, secondary, and tertiary risk values. This risk value calculation method, based on multiple target feature points, not only improves assessment accuracy but also reflects the dynamic progression of the disease. During the risk assessment phase, a comprehensive analysis combining risk values from three dimensions establishes a multi-level risk assessment system that effectively identifies early-stage pathological changes and potential risks, enhancing the accuracy of Alzheimer's disease risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 1 is a flow chart of a method for risk assessment of Alzheimer's disease provided in an embodiment of the present application;

[0021] Figure 2 1 is a schematic structural diagram of a risk assessment system for Alzheimer's disease provided in an embodiment of the present application;

[0022] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0023] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0025] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0026] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0027] The present application embodiment provides a method for risk assessment of Alzheimer's disease. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of a method for risk assessment of Alzheimer's disease provided by an embodiment of the present application. This method can be implemented by a computer program, which can be integrated into an application or run as a standalone tool application. This method can also be implemented by a single-chip microcomputer or run on a risk assessment system for Alzheimer's disease based on a von Neumann architecture. Specifically, this method can include the following steps:

[0028] Step 101: Obtain brain magnetic resonance image data of a target user.

[0029] Brain MRI data refers to a collection of multi-dimensional physiological parameter information obtained by scanning the target user's brain using MRI equipment. This brain MRI data includes various parameters reflecting the brain's structure and function, including EEG parameters describing neuronal electrical activity, cerebral blood flow parameters representing brain tissue metabolism, and head posture parameters reflecting motor control ability.

[0030] Specifically, a brain scan of the target user is performed using a magnetic resonance imaging device to obtain magnetic resonance imaging (MRI) data of the target user's brain. This MRI device can simultaneously collect multiple physiological parameter information, including brain wave parameters, cerebral blood flow parameters, and head posture parameters. EEG parameters reflect the electrical activity of the target user's brain neurons and can be used to assess nervous system function; cerebral blood flow parameters characterize the oxygen supply and metabolism of brain tissue and reflect neuronal activity; and head posture parameters reflect the target user's motor control ability. These parameters are closely related to the pathogenesis of Alzheimer's disease. In its implementation, the MRI device uses high-resolution three-dimensional imaging technology, synchronously acquiring different parameter information using a multi-channel coil array to ensure temporal consistency of the data. During the scan, the target user remains in a supine position with their head fixed to reduce motion artifacts. The scan typically lasts 15-20 minutes. After preprocessing, the acquired MRI image data automatically extracts the aforementioned three parameter information and stores it digitally. This multi-parameter synchronous acquisition method not only improves data acquisition efficiency but also ensures temporal correlation between different parameters, laying the foundation for subsequent comprehensive analysis.

[0031] Step 102: Perform time-frequency analysis on the EEG parameters in the brain MRI data to determine the EEG spectrum curve of the target user. Based on the head posture parameters in the brain MRI data, construct a head motion trajectory curve of the target user. Based on the brain blood flow parameters in the brain MRI data, generate a neuron activity curve of the target user.

[0032] Among them, the brain wave parameters refer to the potential fluctuation signals generated by the discharge of brain neuron groups collected by the electrode array set on the scalp surface of the target user. This signal reflects the electrophysiological activity characteristics of neuron groups in different areas of the cerebral cortex when processing and transmitting information. The brain wave parameters include brain wave rhythms in different frequency bands, such as delta waves (0.5-4Hz), theta waves (4-8Hz), alpha waves (8-13Hz), beta waves (13-30Hz) and gamma waves (above 30Hz). Among them, alpha waves mainly reflect the basic activity state of the brain, beta waves are related to cognitive processing and attention, theta waves are related to memory and emotional processing, and gamma waves are closely related to advanced cognitive functions.

[0033] Cerebral blood flow parameters refer to real-time data reflecting the state of blood supply to brain tissue, measured using near-infrared spectroscopy sensors. This data primarily includes information on blood oxygen concentration and blood flow velocity. The latter reflects the relative amounts of oxyhemoglobin and deoxyhemoglobin in brain tissue, while the latter represents the rate of blood flow through blood vessels in a specific brain region per unit time. When near-infrared light penetrates the scalp and skull and reaches the brain, hemoglobin in different oxygenation states exhibits different absorption characteristics for specific wavelengths of near-infrared light. By detecting changes in the intensity of the reflected light, the dynamic changes in blood oxygen concentration can be calculated.

[0034] Head posture parameters refer to a set of data collected in real time by an inertial measurement unit that reflects the movement state of the target user's head in three-dimensional space. This data mainly includes angular velocity information and acceleration information. The angular velocity information describes the change in the rotation rate of the head around different axes, and the acceleration information reflects the change in the displacement acceleration of the head in space. Specifically, the gyroscope is responsible for collecting angular velocity data of the head in the three degrees of freedom of pitch, roll and yaw, while the accelerometer measures the linear acceleration data of the head in the three orthogonal directions of X, Y and Z. These data can accurately characterize the instantaneous motion characteristics of the head, including the amplitude, speed, acceleration, and smoothness and coordination of the movement.

[0035] An EEG spectrum curve is a time series curve derived from time-frequency analysis of EEG parameters, reflecting the energy variations in EEG activity across different frequency bands. The curve represents time on the horizontal axis and energy intensity across different frequency bands on the vertical axis, demonstrating the dynamic characteristics of EEG activity through its continuous curve form.

[0036] A head motion trajectory curve is a three-dimensional spatial curve that reflects head motion characteristics, obtained by performing posture calculation and trajectory reconstruction on head posture parameters. This curve is composed of a series of continuous spatial locations, each of which contains precise three-dimensional coordinate information and a corresponding timestamp, fully recording the spatial trajectory and time course of head motion.

[0037] The neuronal activity curve is a time series curve reflecting the metabolic activity of neurons in a local brain region, derived by processing cerebral blood flow parameters. The curve represents the progression of time on the horizontal axis and the intensity of neuronal activity on the vertical axis, and is used to quantitatively describe the functional state and energy metabolism of brain tissue.

[0038] Specifically, after acquiring multi-source data, specialized data processing methods are required to convert the raw data into analytically applicable characteristic curves. First, a time-frequency analysis is performed on the collected EEG parameters. Wavelet transforms are used to decompose the time-domain signals into frequency bands, capturing the dynamic characteristics of the energy changes in each frequency band over time. In this implementation, wavelets are selected as basis functions and a continuous wavelet transform is performed on the raw EEG signals. A time-frequency energy distribution map is calculated, and the energy variation trends of different frequency bands are extracted to create an EEG spectrum curve. This curve intuitively reflects the dynamic changes in EEG activity across different frequency bands, facilitating the identification of abnormal EEG rhythm patterns. Secondly, based on the head posture parameters collected by the inertial measurement unit, the angular velocity and acceleration data are fused using a Kalman filter algorithm to eliminate measurement noise and drift errors. The processed data are then converted into spatial position coordinates using a posture resolution algorithm, ultimately constructing a continuous head motion trajectory curve. This curve accurately describes the spatial characteristics and temporal evolution of head motion, facilitating the detection of abnormal movement patterns. At the same time, for cerebral blood flow parameters, a hemodynamic response model is used to convert near-infrared spectral signals into a time series reflecting changes in local brain blood flow. Combined with blood flow velocity information, a neuronal activity curve is generated to characterize neuronal metabolic activity. This curve can reflect the functional activity and energy metabolism levels of different brain regions, helping to detect local blood flow abnormalities. By constructing these three characteristic curves, physiological information from different dimensions can be converted into a unified time series format, laying the foundation for subsequent multidimensional data fusion analysis.

[0039] Based on the above embodiment, as an optional embodiment, in step 102: performing time-frequency analysis on the brain wave parameters in the brain magnetic resonance image data to determine the brain wave spectrum curve of the target user, this step may also include the following steps:

[0040] Step 201: Determine a first EEG signal corresponding to the left brain region and a second EEG signal corresponding to the right brain region in EEG wave parameters; and calculate a correlation coefficient between the first EEG signal and the second EEG signal.

[0041] The first EEG signal refers to the time series of EEG waves collected from specific electrode locations in the target user's left brain region. This signal is a comprehensive representation of the electrical activity of neuronal populations in the left hemisphere's cerebral cortex, reflecting the functional state and information processing characteristics of the left brain region.

[0042] The second EEG signal refers to the time series of brainwaves collected from specific electrode locations in the target user's right brain region. This signal is a comprehensive representation of the electrical activity of neuronal populations in the right hemisphere's cerebral cortex, reflecting the functional state and information processing characteristics of the right brain region.

[0043] The cross-correlation coefficient is a numerical indicator used to quantify the temporal correlation strength between the first and second EEG signals in the left and right brain regions. This coefficient, calculated using the Pearson correlation coefficient and ranging from -1 to 1, represents the degree of synchronization and functional connectivity between the signals in the two brain regions.

[0044] Specifically, after obtaining the target user's EEG parameters, the functional connectivity between the left and right brain regions needs to be assessed. This is because early Alzheimer's disease can cause changes in the efficiency of interhemispheric information transmission. First, EEG parameters are spatially divided into left and right brain regions according to the international 10-20 system electrode layout standard. For the left brain region, signals collected by electrodes F3, C3, P3, and T3 are selected as the first EEG signal. These electrodes correspond to the frontal, central, parietal, and temporal regions of the left hemisphere, respectively. For the right brain region, signals corresponding to electrodes F4, C4, P4, and T4 are selected as the second EEG signal, covering the corresponding functional regions of the right hemisphere. Before signal separation, preprocessing is required to improve signal quality. This includes using a 50Hz notch filter to remove power frequency interference, applying a wavelet transform to remove artifacts such as electromyography and electrooculography, and performing baseline drift correction. After obtaining the cleaned left and right brain signals, the signals are segmented using 2-second time windows, with a 50% overlap between adjacent windows to ensure temporal continuity. Then, the Pearson correlation coefficient is calculated for the first EEG signal and the second EEG signal in each time window to obtain the cross-correlation coefficient reflecting the strength of the functional connection between the left and right brains. The value range of this coefficient is between -1 and 1, which can quantitatively characterize the degree of synchronization between the two signals. By analyzing the time series characteristics of the cross-correlation coefficient, it can be found that under normal conditions, the EEG activities of the left and right brain regions show strong synchronization, and the cross-correlation coefficient is usually maintained at a high level of 0.6-0.8; in the early stages of the disease, due to the degeneration of neuronal function and damage to the corpus callosum fiber bundle, the cross-correlation coefficient will be significantly reduced to the range of 0.2-0.4, and obvious fluctuations will occur. This method based on cross-correlation analysis can sensitively detect abnormal changes in functional connectivity between the cerebral hemispheres, providing a reliable quantitative indicator for disease risk assessment.

[0045] Step 202: Filter the first EEG signal and the second EEG signal to obtain a first signal feature of the left brain region and a second signal feature of the right brain region.

[0046] The first signal feature is a multidimensional feature set derived from frequency-domain filtering and feature extraction of the first EEG signal from the left brain region. This feature set encompasses the energy distribution and time-varying characteristics of EEG activity across different frequency bands in the left brain region, and is used to characterize the functional state of the neuronal population in the left hemisphere.

[0047] The second signal feature is a multidimensional feature set derived from frequency-domain filtering and feature extraction of the second EEG signal in the right brain region. This feature set encompasses the energy distribution and time-varying characteristics of EEG activity in different frequency bands in the right brain region, and is used to characterize the functional state of neuronal populations in the right hemisphere.

[0048] Specifically, after completing the separation and correlation analysis of the left and right brain region signals, in order to further extract the characteristic information of EEG activity, the first EEG signal and the second EEG signal need to be subjected to frequency domain analysis and filtering. This is because EEG rhythms in different frequency bands reflect different states of neural activity, and Alzheimer's disease can cause changes in the energy distribution of specific frequency bands. In specific implementation, a Butterworth bandpass filter is first used to decompose the signal into frequency bands, and the first EEG signal and the second EEG signal are filtered into five frequency bands: delta wave (0.5-4Hz), theta wave (4-8Hz), alpha wave (8-13Hz), beta wave (13-30Hz) and gamma wave (30-45Hz). The order of the filter is set to 4 to ensure good frequency selectivity and phase characteristics. The signal of each frequency band is Hilbert transformed, and the instantaneous amplitude and phase information are calculated to obtain the envelope reflecting the energy change of each frequency band. Through this filtering process, the first signal characteristics of the left brain region and the second signal characteristics of the right brain region can be obtained respectively. These features include information such as the energy distribution of each frequency band, its time-varying characteristics, and the energy ratio between frequency bands. Under normal conditions, alpha waves exhibit a significant energy advantage when the eyes are closed and quiet, while the energy of theta and delta waves is relatively low. In the early stages of Alzheimer's disease, a significant decrease in alpha wave energy is observed, while the energy of theta waves is relatively increased. This change in spectral characteristics often precedes the onset of clinical symptoms. By comparing and analyzing the first and second signal features, we can reveal energy asymmetry between the left and right brain regions in each frequency band, providing richer characteristic indicators for disease risk assessment.

[0049] Step 203: Generate a brainwave spectrum curve of the target user based on the cross-correlation coefficient, the first signal feature, and the second signal feature.

[0050] Specifically, to accurately reflect the signal synergy and respective characteristic features of the target user's left and right hemispheres, it is necessary to generate an EEG spectrum curve based on the cross-correlation coefficient, the first signal feature, and the second signal feature. First, the cross-correlation coefficient is used as a weighting factor to adjust the fusion ratio of the first and second signal features. When the cross-correlation coefficient is high, indicating strong signal synchronization between the left and right hemispheres, the fusion weight of the two signal features is increased; when the cross-correlation coefficient is low, the fusion weight is reduced accordingly to highlight the differences in their respective characteristics. During the signal feature fusion process, the first and second signal features are combined using a weighted average method. The temporal dimension is also introduced, and the feature values at different time points are sequentially connected to form a time-varying spectrum curve. The ordinate of this EEG spectrum curve represents signal strength, and the abscissa represents time. The fluctuation characteristics of the curve intuitively reflect the EEG activity patterns of the target user. This curve generation method based on multi-feature fusion not only preserves the unique characteristics of the left and right hemispheres, but also reflects the synergistic relationship between the two regions through the cross-correlation coefficient. This allows the generated spectrum curve to more comprehensively represent the overall working state of the brain.

[0051] Based on the above embodiment, as an optional embodiment, in step 102: constructing the target user's head motion trajectory curve based on the head posture parameters in the brain magnetic resonance image data, this step may also include the following steps:

[0052] Step 204: Obtain angular velocity information and acceleration information from the head posture parameters; determine the target user's head rotation angle based on the angular velocity information, and determine the target user's head displacement based on the acceleration information.

[0053] Angular velocity information is a physical quantity that describes the rate at which the target user's head rotates around a fixed axis. It represents the angle of head rotation per unit time. This angular velocity information includes the instantaneous angular velocity values of the head rotating around the X-axis (for nodding), Y-axis (for shaking), and Z-axis (for tilting) in three-dimensional space.

[0054] Acceleration information is a physical quantity that represents the rate of change of the target user's head movement, reflecting the speed of the head's motion changes in three-dimensional space. This acceleration information includes linear acceleration components along the X-axis (front-back), Y-axis (left-right), and Z-axis (up-down), as well as the tangential acceleration component caused by rotation.

[0055] Specifically, angular velocity and acceleration information are first extracted from head posture parameters acquired by the magnetic resonance imaging device. These two types of information, respectively, reflect the dynamic characteristics of head rotation and translation. When processing the angular velocity information, a numerical integration method is used to integrate the angular velocity over time to obtain the head rotation angle at different moments. An appropriate integration step size is selected, and the angular velocity changes over various time periods are accumulated. Zero-drift compensation is also performed in conjunction with gyroscope data to ensure the accuracy of the calculated results. For processing the acceleration information, a double integration method is employed: acceleration is first integrated once to obtain velocity, and then integrated twice to obtain head displacement. During the integration process, a Kalman filter algorithm is introduced to suppress the cumulative effect of integration errors, and noise interference is filtered out by setting a reasonable threshold. This analysis method based on kinematic parameters can quantitatively describe the stability and coordination of the target user's head movements, characteristics that are closely related to the early degeneration of motor control function in Alzheimer's disease. Accurately acquiring head rotation angle and displacement information provides reliable basic data for subsequent trajectory analysis, facilitating the early detection of abnormal movement patterns. At the same time, this analysis method has good real-time performance and can dynamically track changes in head movement status, improving the timeliness and accuracy of the evaluation.

[0056] Step 205: Generate a head motion trajectory curve of the target user according to the head rotation angle and the head displacement.

[0057] Specifically, a three-dimensional coordinate system is first established, and head rotation angle and displacement data are time-synchronized. A parametric equation is used to describe the head's motion in space. The head rotation angle is decomposed into three components: pitch, yaw, and roll, while the head displacement consists of spatial coordinates in the X, Y, and Z directions. Using a time-series mapping method, the angle and displacement data at each time point are combined into spatial locations. These discrete locations are then smoothly connected using a cubic spline interpolation algorithm to form a continuous motion trajectory curve. During the curve generation process, an adaptive filtering algorithm is introduced to eliminate the effects of high-frequency jitter and measurement noise, ensuring the smoothness and accuracy of the trajectory curve. The generated head motion trajectory curve is presented as a three-dimensional graph, where the curve shape reflects the spatial characteristics of the motion, the curve smoothness characterizes the coordination of the motion, and the curve density reflects the frequency characteristics of the motion. This visual trajectory representation not only intuitively demonstrates the stability and regularity of head movement, but also enables the quantitative analysis of trajectory characteristics to identify potential motor control abnormalities. By comparing the trajectory patterns with those of a healthy individual, abnormal motion characteristics associated with Alzheimer's disease can be promptly identified, providing a basis for risk assessment.

[0058] Based on the above embodiment, as an optional embodiment, in step 102: generating a neuron activity curve of the target user based on the brain blood flow parameters in the brain magnetic resonance image data, this step may further include the following steps:

[0059] Step 206: Obtain blood oxygen concentration information and blood flow velocity information from the cerebral blood flow parameters; calculate the concentration change rate of the blood oxygen concentration information within a preset sampling period.

[0060] Blood oxygen concentration (BOC) refers to a physiological parameter that reflects the ratio of hemoglobin to oxygen molecules in the target user's brain tissue, representing the level of oxygen in the blood in the local brain tissue. This BOC information includes the relative ratio of oxyhemoglobin to deoxyhemoglobin in the brain tissue, as well as the dissolved oxygen content in the blood.

[0061] Blood velocity information is a physical quantity that characterizes the speed of blood flow in the target user's brain vessels, reflecting the dynamic characteristics of brain tissue blood perfusion. This blood velocity information includes the linear velocity and volume flow of blood flowing in the cerebral arteries, veins, and capillary networks, as well as the pulsation characteristics and direction of blood flow.

[0062] Specifically, blood oxygen concentration (BOC) and blood velocity information are first extracted from brain blood flow parameters acquired by magnetic resonance imaging (MRI). These two types of information reflect the oxygen supply level and blood perfusion efficiency of brain tissue, respectively. To process the BOC information, a reasonable sampling period, such as 100 milliseconds, is set, and time series analysis is performed on the continuously acquired BOC data. The instantaneous rate of change of BOC is calculated by calculating the BOC difference between adjacent sampling points and dividing it by the sampling interval. To improve accuracy, a sliding window method is used to average the data over multiple sampling periods, while a digital filtering algorithm is introduced to eliminate the influence of measurement noise. This time series-based analysis method accurately reflects the dynamic changes in brain tissue oxygen supply. The BOC rate of change directly reflects the oxygen consumption characteristics and metabolic activity of brain tissue. Since early Alzheimer's disease is often accompanied by abnormal brain metabolism, monitoring the changing trend of BOC can help detect potential pathological changes early on. Furthermore, this analysis method has high temporal resolution, capturing short-term BOC fluctuations, providing important insights for assessing brain function. The acquisition and processing of this information provides reliable physiological parameter support for subsequent risk assessment and improves the accuracy of the assessment results.

[0063] Step 207: Determine the brain oxygen supply index corresponding to the blood flow velocity information based on a preset blood flow velocity mapping table.

[0064] The pre-set blood flow velocity mapping table is a data structure that establishes the correspondence between blood flow velocity and brain oxygenation index. It contains different blood flow velocity intervals and their corresponding standardized oxygenation index reference values. This mapping table is constructed based on large-scale clinical research data and statistical analysis results of healthy people. It includes multiple blood flow velocity intervals, each of which corresponds to a standardized oxygenation index value.

[0065] The brain oxygenation index (BOI) is a comprehensive indicator used to quantitatively assess the adequacy of oxygen supply to brain tissue, reflecting the overall efficiency of blood perfusion and oxygen delivery to brain tissue. The index is a dimensionless value calculated by normalizing multiple physiological parameters, including blood flow velocity, vascular cross-sectional area, and blood oxygen concentration. Its value typically ranges from 0 to 1, with higher values indicating better oxygenation.

[0066] Specifically, to accurately quantify the oxygen supply status of brain tissue, it is necessary to convert blood flow velocity information into a more physiologically meaningful brain oxygenation index. First, a pre-defined blood flow velocity mapping table is established. This mapping table, derived from statistical analysis of extensive clinical data, contains standardized oxygenation index values corresponding to different blood flow velocity intervals. During the mapping conversion, real-time blood flow velocity information is matched to the velocity intervals in the mapping table, and the corresponding brain oxygenation index is determined using piecewise linear interpolation. To improve the accuracy of the conversion, the mapping process takes into account factors such as anatomical location and diameter of blood vessels, and applies corresponding weighting coefficients to correct blood flow velocities in different brain regions. Furthermore, temperature correction and pressure compensation mechanisms are introduced to eliminate the influence of environmental factors on blood flow velocity measurements. This mapping-based conversion method not only simplifies complex hemodynamic calculations but also provides a standardized assessment metric. By converting continuous blood flow velocity data into a brain oxygenation index, the degree of oxygenation in brain tissue can be intuitively reflected. Since early Alzheimer's disease is often accompanied by localized brain tissue blood supply deficiency, this standardized oxygenation index can help identify potential circulatory dysfunction early. At the same time, this conversion method has good comparability, which facilitates comparative analysis between different periods and different individuals, and provides a quantitative basis for risk assessment.

[0067] Step 208: Generate a neuron activity curve of the target user based on the time change rate and the brain oxygen supply index.

[0068] Among them, the neuron activity curve refers to a graphical expression that describes the dynamic changes in the functional state of neurons in the target user's brain over time, reflecting the energy metabolism and information transmission efficiency of nerve cells.

[0069] Specifically, to display the target user's brain neuronal function, a neuronal activity curve is generated based on the time-dependent rate of change of blood oxygen concentration (BOC) and the brain oxygen supply index (BOS). First, the BOC and BOS data are synchronized to ensure a consistent relationship between the two sets of data on the time axis. These two metrics are then fused using a linear weighting method. The weight coefficient for the BOC is set to 0.6, reflecting the dominant role of neuronal metabolic demand, and the weight coefficient for the BOS is set to 0.4, reflecting the fundamental supporting role of BOS capacity. During data processing, the calculated results are smoothed using a sliding average algorithm. An appropriate time window (typically 200 milliseconds) is selected for smoothing to eliminate the influence of transient fluctuations. Furthermore, a fixed sampling interval (typically 50 milliseconds) is set to ensure a uniform distribution of data points. In the coordinate system, time is plotted on the horizontal axis, with units marked in seconds, and the calculated neuronal activity intensity is plotted on the vertical axis. The values are normalized to a range between 0 and 1. A continuous neuronal activity curve is generated by connecting adjacent data points and using cubic spline interpolation to achieve smooth transitions. This visualization method can directly reflect the dynamic changes in neuronal activity. The amplitude of the curve reflects the degree of change in neuronal excitability, and the frequency of the curve represents the rhythmic characteristics of neuronal activity. Because early Alzheimer's disease often manifests as subtle changes in neuronal function, analyzing the characteristic parameters of the activity curve, such as average activity level, fluctuation period, and peak-to-valley ratio, can help detect potential functional abnormalities early. This analysis method has high temporal precision and can capture rapid changes in neuronal activity.

[0070] Step 103: Determine multiple target feature points in the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve.

[0071] Among them, target feature points refer to key data points with specific physiological significance in the EEG spectrum curve, head movement trajectory curve and neuron activity curve. These data points can reflect significant changes in the functional state of the brain.

[0072] Specifically, to accurately capture key changes in the target user's brain function, it is necessary to extract diagnostically valuable feature points from the EEG spectrum, head motion trajectory, and neuronal activity curves. These three curves are analyzed synchronously using a unified time base, and physiologically significant feature points are identified on each curve. In the EEG spectrum, peaks, valleys, and inflection points of alpha waves (8-13Hz), beta waves (14-30Hz), and theta waves (4-7Hz) are primarily extracted. These feature points reflect the intensity changes of brain activity in different frequency bands. In the head motion trajectory, turning points, pauses, and acceleration changes in movement speed are identified. These feature points characterize the stability and coordination of head movements. In the neuronal activity curve, the focus is on the maximum, minimum, and mutation points of activity intensity. These feature points reflect the dynamic changes in neuronal function. During feature point extraction, a threshold detection method is used to determine the locations of numerical mutations. Effective feature points are selected by setting reasonable threshold parameters (e.g., deviations exceeding 20% from the local mean). At the same time, a time window constraint is introduced to ensure that there is a sufficient time interval between adjacent feature points (usually no less than 100 milliseconds) to avoid excessive density of feature points. For each identified feature point, its time coordinate and amplitude information are recorded, and the feature points are classified and labeled for subsequent extraction and analysis of target features. This feature point extraction method can effectively capture key change information in the curve and provide accurate quantitative indicators for assessing the early risk of Alzheimer's disease. Since the early stages of the disease often manifest as coordinated abnormalities in multiple physiological indicators, potential functional disorders can be discovered by analyzing the spatiotemporal correlation of feature points on different curves. This multi-dimensional feature extraction method improves the sensitivity and specificity of diagnosis and provides a reliable data basis for subsequent risk assessment.

[0073] Based on the above embodiment, as an optional embodiment, in step 103: determining multiple target feature points in the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve, this step may further include the following steps:

[0074] Step 301: Divide the brainwave spectrum curve, the head movement trajectory curve, and the neuron activity curve into multiple time windows.

[0075] Specifically, to accurately analyze the dynamic characteristics of the target user's brain function state, it is necessary to time-window the EEG spectrum curves, head movement trajectory curves, and neuronal activity curves. First, a unified time base is established, dividing the total time range of the monitoring process (typically 10 minutes) into multiple continuous and equal-length time windows. Each time window is set to 30 seconds in length, and a 25% overlap is allowed between adjacent windows to ensure continuity in data analysis. During the time-windowing process, a synchronized time scale is used for the three curves to ensure temporal correspondence between the data within the same time window. To improve analysis accuracy, equally spaced sampling is used within each time window, with a sampling interval of 50 milliseconds to ensure a uniform distribution of data points. Furthermore, a Hanning window function is used for smoothing at the window boundaries to minimize the impact of windowing on signal continuity. This time-windowing method decomposes long, continuous signals into multiple, more analyzable short-term series, facilitating the capture of characteristic changes at local time scales. Because early functional changes in Alzheimer's disease often manifest as abnormal fluctuations within specific time periods, windowed analysis can accurately pinpoint the time intervals when these abnormalities occur.

[0076] Step 302: Based on multiple data points of the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve in each time window, the mean and standard deviation of the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve in each time window are calculated accordingly.

[0077] Specifically, data points within each 30-second time window were first preprocessed to remove potential outliers (data points outside the range of plus or minus three standard deviations of the local mean). For the EEG spectrum curve, the arithmetic mean of the energy in the alpha, beta, and theta bands was calculated to reflect the average intensity of EEG activity in each band. The standard deviation of the energy values in these bands was also calculated to characterize the dispersion of energy fluctuations. For the head movement trajectory curve, the arithmetic mean of the movement velocity was calculated to reflect the overall level of head movement activity, and the standard deviation of the velocity values was calculated to characterize the fluctuations in movement stability. For the neuronal activity curve, the arithmetic mean of the activity intensity was calculated to reflect the average functional level of the neuronal population, and the standard deviation of the activity values was calculated to characterize the fluctuations in functional status. During the calculation process, the mean of the n sampling points within each time window was calculated using the arithmetic mean formula, and the standard deviation was calculated using the population standard deviation formula to ensure the accuracy of the statistical results. This statistical feature calculation method can summarize the detailed changes within the time window into statistically significant characteristic parameters. Because early Alzheimer's disease often manifests as abnormal fluctuations in physiological indicators, analyzing the mean can reveal overall changes in functional levels, while analyzing the standard deviation can identify abnormalities in functional stability. For each time window, the six statistical features calculated (the means and standard deviations of the three curves) are combined to form a feature vector, which comprehensively reflects the statistical characteristics of brain function during that time period.

[0078] Step 303: For each time window, data points in the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve that exceed a preset multiple of the mean standard deviation are determined as target feature points.

[0079] Specifically, to accurately identify diagnostically valuable abnormal fluctuation points from massive amounts of data, the data points within each time window of the three curves are screened based on statistical features. Data points that significantly deviate from the normal range are identified as target feature points. First, a threshold of multiples of the standard deviation is set for each curve. The threshold for the EEG spectrum curve is set at 2.5 standard deviations to account for the natural fluctuations of EEG signals; the threshold for the head movement trajectory curve is set at 2 standard deviations to reflect the strict requirements of motor coordination; and the threshold for the neuronal activity curve is set at 3 standard deviations to reflect the wide physiological fluctuation range of neuronal activity. Within each 30-second time window, the deviation between each data point and the window mean is calculated, and the absolute value of the deviation is divided by the corresponding standard deviation to obtain the standardized deviation value. When the standardized deviation value of a data point exceeds the preset multiple threshold, the point is marked as a target feature point, and its time coordinate, amplitude, and curve type are recorded. This statistically significant feature point identification method can adaptively adjust the judgment criteria and reasonably identify abnormal fluctuation points under different activity states. Because functional abnormalities in the early stages of Alzheimer's disease often manifest as discrete mutational events, this method can accurately capture these critical moments of change.

[0080] Step 104: Calculate a first risk value corresponding to each target feature point in the EEG spectrum curve, a second risk value corresponding to each target feature point in the head motion trajectory curve, and a third risk value corresponding to each target feature point in the neuron activity curve.

[0081] Among them, the first risk value refers to a quantitative indicator calculated based on the abnormal characteristics of the target feature points in the brain wave spectrum curve. This indicator comprehensively reflects the degree of abnormality of the brain's electrical activity at a specific moment.

[0082] The second risk value refers to a quantitative indicator calculated based on the abnormal features of the target feature points in the head motion trajectory curve. This indicator comprehensively reflects the abnormal degree of the target user's head motion control ability.

[0083] The third risk value refers to a quantitative indicator calculated based on the abnormal characteristics of the target feature points in the neuron activity curve. This indicator comprehensively reflects the degree of abnormality in the functional state of the brain neuron population.

[0084] Specifically, to accurately quantify the degree of abnormality in each of the target user's physiological indicators, it is necessary to calculate risk values for target feature points in the EEG spectrum curve, head motion trajectory curve, and neuronal activity curve. In implementation, the target feature points of each curve are first classified and grouped according to their time window and curve type. For target feature points in the EEG spectrum curve, the calculation of their first risk value considers three key factors: the normalized deviation amplitude of the feature point (e.g., a weight of 40%), the energy proportion of the feature point in the corresponding frequency band (e.g., a weight of 35%), and the temporal correlation with neighboring feature points (e.g., a weight of 25%). These three factors are weighted and normalized to obtain a first risk value ranging from 0 to 1. For target feature points in the head motion trajectory curve, the calculation of their second risk value focuses on the amplitude of the sudden change in motion speed (e.g., a weight of 45%), the rate of change in motion acceleration (e.g., a weight of 30%), and the degree of abnormality in motion duration (e.g., a weight of 25%). Similarly, a weighted fusion and normalization process is used to obtain the second risk value. For target feature points in the neuronal activity curve, the third risk value is calculated by comprehensively considering: the degree of deviation in activity intensity (e.g., 50% weight), the persistence of activity changes (e.g., 30% weight), and synchronization with other physiological indicators (e.g., 20% weight). These factors are weighted and normalized to obtain the third risk value. This multi-dimensional risk quantification method can comprehensively assess the severity of different types of physiological abnormalities. Since early Alzheimer's disease often manifests as coordinated abnormalities in multiple indicators, calculating risk values across different dimensions can accurately capture early signs of the disease.

[0085] Based on the above embodiment, as an optional embodiment, in step 104: calculating the first risk value corresponding to each target feature point in the EEG spectrum curve, the second risk value corresponding to each target feature point in the head motion trajectory curve, and the third risk value corresponding to each target feature point in the neuron activity curve, this step may further include the following steps:

[0086] Step 401: Calculate the average characteristic value of the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve corresponding to each target characteristic point.

[0087] The average eigenvalue refers to the comprehensive numerical index obtained by extracting features and performing weighted averaging on the corresponding physiological indicators within the local time window corresponding to the target feature point.

[0088] Specifically, the target feature points of each curve are first grouped in time series. A sampling window (e.g., a window length of 2 seconds) is taken centered around the target feature point, and the data sequence within the window is extracted. For target feature points in the EEG spectrum curve, the average eigenvalue is calculated by first decomposing the spectral data within the window into alpha, beta, and theta waves. The weighted average of the energy in each frequency band is then calculated, with the weight coefficient determined based on the correlation between each frequency band and cognitive function. For target feature points in the head motion trajectory curve, the average eigenvalue is calculated by comprehensively considering the instantaneous velocity, acceleration, and angular velocity within the window, and then normalizing the data to obtain a weighted average. For target feature points in the neuronal activity curve, the average eigenvalue is calculated by smoothing the activity intensity data within the window, removing high-frequency noise, and then calculating the average activity level. This local window-based average eigenvalue calculation method effectively extracts stable features around the target feature point and reduces the impact of random fluctuations.

[0089] Step 402: Obtain a preset first correlation coefficient corresponding to the brain wave spectrum curve, a preset second correlation coefficient corresponding to the head movement trajectory curve, and a preset third correlation coefficient corresponding to the neuron activity curve.

[0090] Among them, the preset first correlation coefficient refers to a weight coefficient determined based on large-scale clinical research data and expert experience, which reflects the importance of the brain wave spectrum curve in the early diagnosis of Alzheimer's disease.

[0091] The preset second correlation coefficient refers to a weight coefficient determined based on large-scale clinical research data and expert experience, reflecting the importance of the head movement trajectory curve in the early diagnosis of Alzheimer's disease.

[0092] The preset third correlation coefficient refers to a weight coefficient determined based on large-scale clinical research data and expert experience, reflecting the importance of the neuronal activity curve in the early diagnosis of Alzheimer's disease.

[0093] Specifically, based on large-scale clinical research data and expert experience, statistical analysis was conducted to determine the strength of correlation between three curves and the early diagnosis of Alzheimer's disease. For the EEG spectrum curve, the first correlation coefficient was set to 0.4, as changes in EEG activity are one of the most significant and specific manifestations of the disease in its early stages. For the head movement trajectory curve, the second correlation coefficient was set to 0.3, reflecting the important supplementary role of motor control dysfunction in early diagnosis. For the neuronal activity curve, the third correlation coefficient was set to 0.3, reflecting the role of neuronal function in indicative of disease progression. These correlation coefficients were set using an adaptive weighting mechanism, dynamically adjusting the coefficient values based on the diagnostic value of each indicator at different disease stages, while always ensuring that the sum of the three coefficients is 1. This clinically validated correlation coefficient configuration scheme effectively balances the contribution of various physiological indicators in risk assessment, improving the accuracy and reliability of the assessment results. Since the progression of Alzheimer's disease often exhibits coordinated changes in multiple physiological indicators, rationally setting correlation coefficients can better capture these multidimensional pathological changes.

[0094] Step 403: Multiply the preset first correlation coefficient by the average eigenvalue of the EEG spectrum curve at each target feature point to obtain a first risk value; multiply the preset second correlation coefficient by the average eigenvalue of the head movement trajectory curve at each target feature point to obtain a second risk value; multiply the preset third correlation coefficient by the average eigenvalue of the neuron activity curve at each target feature point to obtain a third risk value.

[0095] Specifically, to convert the characteristic values of each physiological indicator into a comparable and diagnostically meaningful risk assessment indicator, a weighted calculation is performed between the preset correlation coefficient and the corresponding average characteristic value. First, for the EEG spectrum curve, the preset first correlation coefficient of 0.4 is multiplied by the average characteristic value at each target characteristic point to obtain a first risk value reflecting the degree of EEG abnormality. Then, for the head movement trajectory curve, the preset second correlation coefficient of 0.3 is multiplied by the average characteristic value at each target characteristic point to obtain a second risk value representing motor function abnormality. Finally, for the neuronal activity curve, the preset third correlation coefficient of 0.3 is multiplied by the average characteristic value at each target characteristic point to obtain a third risk value reflecting the functional state of the neuron. This weighted calculation method not only takes into account the actual measured values of each physiological indicator but also reflects the importance of different indicators in disease diagnosis through the preset correlation coefficients. Because the early manifestations of Alzheimer's disease often involve coordinated changes in multiple physiological systems, this method of calculating each risk value separately can independently assess the degree of abnormality in each system. In practical applications, this calculation scheme effectively balances the contributions of various indicators, ensuring that the calculated risk value maintains the variability of the original characteristics while also reflecting the importance of different indicators based on clinical diagnostic experience. Through this weighted conversion, physiological indicators of different dimensions and physical meanings can be uniformly converted into standardized risk assessment indicators, providing standardized input parameters for subsequent comprehensive risk assessments.

[0096] Step 105: Determine the Alzheimer's disease risk assessment result of the target user by combining the first risk value, the second risk value, and the third risk value.

[0097] Among them, the Alzheimer's disease risk assessment result refers to a comprehensive assessment index obtained by performing nonlinear weighted fusion calculation on the first risk value, the second risk value and the third risk value. The result includes quantitative risk level judgment, such as low risk 0-0.3, medium risk 0.3-0.6, high risk 0.6-1.0, as well as abnormal characteristic analysis of various physiological functions.

[0098] Specifically, a three-dimensional feature space is first constructed, with the first, second, and third risk values as the three coordinate axes. The target user's risk status is represented as a feature point in this space. A nonlinear weighted fusion algorithm is then used to account for the interactions between the three risk values to calculate a comprehensive risk score. This algorithm not only considers the independent contribution of each risk value but also introduces cross-terms to characterize the amplification effect of multi-system synergistic abnormalities. Specifically, when multiple risk values increase simultaneously, the comprehensive risk exhibits a nonlinear increase. Based on extensive clinical validation data, multiple risk thresholds are set, categorizing the comprehensive risk score into three levels: low risk (0-0.3), moderate risk (0.3-0.6), and high risk (0.6-1.0). Furthermore, a time series analysis method is used to examine the changing trends of each risk value, using the rate of risk change as an important supplementary information to the assessment results. This multidimensional risk assessment approach can effectively capture subtle changes in the early stages of a disease, particularly before certain physiological indicators reach overtly abnormal levels. By analyzing the synergistic patterns of changes in multiple indicators, potential pathological changes can be detected early. In practical applications, this assessment method not only gives a quantitative risk level judgment, but also provides a detailed abnormal feature analysis report, including the specific abnormal manifestations and change trends of various physiological functions, providing decision support for clinicians to formulate personalized intervention plans.

[0099] Based on the above embodiment, as an optional embodiment, in step 105: combining the first risk value, the second risk value, and the third risk value to determine the Alzheimer's disease risk assessment result of the target user, this step may further include the following steps:

[0100] Step 501: Calculate the weighted average of the first risk value, the second risk value, and the third risk value; determine the risk probability corresponding to the weighted average based on a preset risk threshold interval, where the preset risk threshold interval includes multiple weighted average intervals corresponding to risk probabilities.

[0101] Among them, risk probability refers to a standardized probability indicator obtained by performing a weighted average calculation on the first risk value, the second risk value, and the third risk value, and mapping it based on a preset risk threshold interval. Its numerical range is 10%-90% or above, reflecting the possibility that the target user will suffer from Alzheimer's disease.

[0102] Specifically, a weighted average algorithm was first used to calculate the combined value of the first, second, and third risk values. The calculation process employed a weight configuration optimized based on clinical research, assigning weight coefficients of 0.4, 0.3, and 0.3 to the three risk values, respectively. This weight distribution not only took into account the clinical importance of each indicator but also maintained consistency with the aforementioned correlation coefficients. The weighted average values were then mapped to risk threshold intervals established based on statistical analysis of large-scale clinical data. The preset risk threshold intervals included: a weighted average value in the range of 0-0.2 corresponds to a risk probability of 10%-30%, indicating a mild abnormality; a weighted average value in the range of 0.2-0.4 corresponds to a risk probability of 30%-50%, indicating a moderate abnormality; a weighted average value in the range of 0.4-0.6 corresponds to a risk probability of 50%-70%, indicating a significant abnormality; a weighted average value in the range of 0.6-0.8 corresponds to a risk probability of 70%-90%, indicating a severe abnormality; and a weighted average value in the range of 0.8-1.0 corresponds to a risk probability of more than 90%, indicating an extremely severe abnormality.

[0103] Step 502: According to the risk probability, the Alzheimer's disease risk level corresponding to the target user is matched in the database.

[0104] Specifically, a mapping relationship table between risk probability and risk level was first constructed in a pre-established risk assessment database. This table was developed based on a large number of clinical case statistics and expert consensus, and divided the risk probability into five intervals corresponding to different risk levels: 10%-30% corresponds to mild risk, indicating that only regular observation is required; 30%-50% corresponds to low risk, and it is recommended to strengthen cognitive function monitoring; 50%-70% corresponds to moderate risk, requiring targeted intervention; 70%-90% corresponds to high risk, and specialist evaluation and early intervention are recommended; and above 90% corresponds to extremely high risk, requiring immediate professional medical intervention. By querying the database, the system matches the target user's risk probability with the preset level interval to determine the corresponding risk level. This risk grading method based on database matching has strong reliability and standardization characteristics, and can provide a clear grading basis for clinical decision-making.

[0105] Step 503: Generate a risk assessment report corresponding to the risk probability and risk level, and use the risk assessment report as the Alzheimer's disease risk assessment result of the target user.

[0106] Specifically, a standardized report template is first constructed, encompassing core content such as patient basic information, assessment date, test data for various physiological indicators, risk value analysis results, risk probability, and risk level. During report generation, the system automatically integrates the specific data and time-series trends for the primary, secondary, and tertiary risk values, and combines the calculated risk probability with the corresponding risk level to generate a graphical and textual assessment report. This report not only presents quantitative assessment results but also includes targeted health recommendations and intervention plans. For example, for patients at moderate risk (risk probability 50%-70%), the report details key cognitive function areas requiring attention and recommended preventive measures. The report's visualization uses various charts, such as trend charts and radar charts, to intuitively display the degree of abnormality and changing characteristics of each indicator, making it easier for both doctors and patients to understand the assessment results. This standardized report output method has excellent clinical practicality, providing doctors with comprehensive diagnostic and treatment reference information while also facilitating patients' understanding of their health status.

[0107] Reference Figure 2 , is a risk assessment system for Alzheimer's disease provided in an embodiment of the present application, the system comprising: a data acquisition module, a curve determination module, a risk value calculation module, and a risk assessment module, wherein:

[0108] A data acquisition module, used to acquire brain magnetic resonance image data of a target user;

[0109] a curve determination module for performing time-frequency analysis on the EEG parameters in the brain MRI data to determine the target user's EEG spectrum curve, constructing a head motion trajectory curve for the target user based on the head posture parameters in the brain MRI data, and generating a neuron activity curve for the target user based on the brain blood flow parameters in the brain MRI data;

[0110] A risk value calculation module is used to determine multiple target feature points in the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve; calculate a first risk value corresponding to each target feature point in the brain wave spectrum curve, a second risk value corresponding to each target feature point in the head movement trajectory curve, and a third risk value corresponding to each target feature point in the neuron activity curve;

[0111] The risk assessment module is used to determine the Alzheimer's disease risk assessment result of the target user by combining the first risk value, the second risk value and the third risk value.

[0112] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0113] This application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0114] The communication bus 302 is used to implement the connection and communication between these components.

[0115] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0116] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0117] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface graphics, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented as a separate chip, rather than integrated into the processor 301.

[0118] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application for a risk assessment method for Alzheimer's disease.

[0119] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program for a risk assessment method for Alzheimer's disease stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0120] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0122] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0125] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure in this specification and practice.

[0126] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The description and examples are to be considered as illustrative only.

Claims

1. A method for risk assessment of Alzheimer's disease, characterized in that: include: Acquiring brain magnetic resonance image data of a target user, wherein the brain magnetic resonance image data includes brain wave parameters, head posture parameters, and brain blood flow parameters; Determine the first EEG signal corresponding to the left brain region and the second EEG signal corresponding to the right brain region in the EEG wave parameters calculating a cross-correlation coefficient between the first EEG signal and the second EEG signal; Performing filtering processing on the first EEG signal and the second EEG signal to obtain a first signal feature of the left brain region and a second signal feature of the right brain region; generating a brainwave spectrum curve of the target user based on the cross-correlation coefficient, the first signal feature, and the second signal feature; Obtaining angular velocity information and acceleration information from the head posture parameters; determining a rotation angle of the target user's head based on the angular velocity information, and determining a displacement of the target user's head based on the acceleration information; generating a head motion trajectory curve of the target user according to the head rotation angle and the head displacement; Obtaining blood oxygen concentration information and blood flow velocity information from the cerebral blood flow parameters; Calculating the concentration change rate of the blood oxygen concentration information within a preset sampling period; Determining a brain oxygen supply index corresponding to the blood flow velocity information based on a preset blood flow velocity mapping table; generating a neuron activity curve of the target user according to the time change rate and the brain oxygen supply index; Determining a plurality of target feature points in the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve includes: Dividing the brainwave spectrum curve, the head movement trajectory curve, and the neuron activity curve into multiple time windows; Based on a plurality of data points of the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve in each of the time windows, correspondingly calculating the mean and standard deviation of the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve in each of the time windows; For each of the time windows, data points in the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve that exceed a preset multiple of the mean standard deviation are determined as target feature points; Calculating a first risk value corresponding to each target feature point in the brain wave spectrum curve, a second risk value corresponding to each target feature point in the head movement trajectory curve, and a third risk value corresponding to each target feature point in the neuron activity curve; The first risk value, the second risk value, and the third risk value are combined to determine an Alzheimer's disease risk assessment result of the target user.

2. The risk assessment method for Alzheimer's disease according to claim 1, characterized in that: The calculating of the first risk value corresponding to each target feature point in the brain wave spectrum curve, the second risk value corresponding to each target feature point in the head movement trajectory curve, and the third risk value corresponding to each target feature point in the neuron activity curve includes: Calculating the average characteristic value of the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve corresponding to each target characteristic point respectively; Obtaining a preset first correlation coefficient corresponding to the brain wave spectrum curve, a preset second correlation coefficient corresponding to the head movement trajectory curve, and a preset third correlation coefficient corresponding to the neuron activity curve; Multiplying the preset first correlation coefficient by the average characteristic value of the brain wave spectrum curve corresponding to each target characteristic point to obtain a first risk value; multiplying the preset second correlation coefficient by the average characteristic value of the head motion trajectory curve corresponding to each target characteristic point to obtain a second risk value; The preset third correlation coefficient is multiplied by the average characteristic value of the neuron activity curve corresponding to each target characteristic point to obtain a third risk value.

3. The risk assessment method for Alzheimer's disease according to claim 1, characterized in that: The determining the Alzheimer's disease risk assessment result of the target user by combining the first risk value, the second risk value, and the third risk value includes: Calculating a weighted average of the first risk value, the second risk value, and the third risk value; Determining the risk probability corresponding to the weighted average value based on a preset risk threshold interval, wherein the preset risk threshold interval includes a plurality of weighted average value intervals corresponding to risk probabilities; According to the risk probability, matching the target user's corresponding Alzheimer's disease risk level in the database; A risk assessment report corresponding to the risk probability and the risk level is generated, and the risk assessment report is used as the Alzheimer's disease risk assessment result of the target user.

4. A risk assessment system for Alzheimer's disease, characterized in that: The system comprises: A data acquisition module is used to acquire brain magnetic resonance image data of a target user, wherein the brain magnetic resonance image data includes brain wave parameters, head posture parameters, and brain blood flow parameters; A curve determination module is used to determine the first EEG signal corresponding to the left brain area and the second EEG signal corresponding to the right brain area in the EEG wave parameters. calculating a cross-correlation coefficient between the first EEG signal and the second EEG signal; Performing filtering processing on the first EEG signal and the second EEG signal to obtain a first signal feature of the left brain region and a second signal feature of the right brain region; generating a brainwave spectrum curve of the target user based on the cross-correlation coefficient, the first signal feature, and the second signal feature; Obtaining angular velocity information and acceleration information from the head posture parameters; determining a rotation angle of the target user's head based on the angular velocity information, and determining a displacement of the target user's head based on the acceleration information; generating a head motion trajectory curve of the target user according to the head rotation angle and the head displacement; Obtaining blood oxygen concentration information and blood flow velocity information from the cerebral blood flow parameters; Calculating the concentration change rate of the blood oxygen concentration information within a preset sampling period; Determining a brain oxygen supply index corresponding to the blood flow velocity information based on a preset blood flow velocity mapping table; generating a neuron activity curve of the target user according to the time change rate and the brain oxygen supply index; The risk value calculation module is used to determine multiple target feature points in the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve, including: Dividing the brainwave spectrum curve, the head movement trajectory curve, and the neuron activity curve into multiple time windows; Based on a plurality of data points of the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve in each of the time windows, correspondingly calculating the mean and standard deviation of the brain wave spectrum curve, the head movement trajectory curve, and the neuron activity curve in each of the time windows; For each of the time windows, data points in the EEG spectrum curve, the head movement trajectory curve, and the neuron activity curve that exceed a preset multiple of the mean standard deviation are determined as target feature points; a first risk value corresponding to each of the target feature points in the EEG spectrum curve, a second risk value corresponding to each of the target feature points in the head movement trajectory curve, and a third risk value corresponding to each of the target feature points in the neuron activity curve are calculated; The risk assessment module is configured to determine an Alzheimer's disease risk assessment result of the target user by combining the first risk value, the second risk value, and the third risk value.

5. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a risk assessment method for Alzheimer's disease as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the risk assessment method for Alzheimer's disease according to any one of claims 1 to 3 is executed.

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