Method and system for identifying smoke disease cerebral blood flow collateral establishment condition based on TCD parameters

Abstract

The invention provides a method and a system for identifying smog disease cerebral blood flow collateral establishment conditions based on TCD parameters, and relates to the technical field of acoustic diagnosis, the method comprises the following steps: firstly, obtaining an original transcranial Doppler spectrum signal, calculating a filtering factor, and dynamically adjusting bandwidth and notch depth to realize optimal noise reduction; segmenting the cleaned signal by using a sliding window, and continuously analyzing dynamic changes such as instantaneous maximum speed, echo energy, spectral entropy and the like; determining and executing first window resetting according to the window trend representation value to generate a first group of more stable and coherent time domain segments; and performing multilayer decomposition on the first group of segments, extracting features of each sub-band, and triggering a second round of window resetting to obtain a second group of more balanced segments, thereby not only suppressing noise and keeping signal details to the greatest extent, but also continuously optimizing window and filter setting for different monitoring environments, and improving the monitoring efficiency. Therefore, powerful technical support is provided for noninvasive, dynamic and high-confidence identification of smog disease cerebral blood flow collateral establishment conditions.

Description

Technical Field

[0001] This invention relates to the field of acoustic diagnostic technology, and in particular to a method and system for identifying the establishment of collateral blood flow in the brain in patients with moyamoya disease based on TCD parameters. Background Technology

[0002] Moyamoya disease is a cerebral hemodynamic disorder characterized by progressive stenosis and occlusion of major intracranial arteries, accompanied by the formation of an abnormal collateral vascular network. Its characteristic feature is the appearance of "smoke-like" collateral circulation in the blood vessels at the base of the brain. Due to the slow progression of vascular stenosis, patients often rely on collateral vessel development to maintain cerebral perfusion, thus affecting stroke risk and prognosis. Transcranial Doppler ultrasound (TCD) is of significant value in hemodynamic monitoring of Moyamoya disease due to its non-invasive, dynamic, and real-time characteristics. TCD can quantify parameters such as mean flow velocity, pulsatility index, and resistance index, dynamically observing the process of collateral blood flow compensation. However, the blood flow signals in patients with Moyamoya disease often exhibit multi-peak, distorted, and even microcirculatory abnormalities, leading to significant technical challenges in identifying the status of collateral establishment.

[0003] For example, CN1883380A discloses a neurocritical care system and a method for synchronous monitoring of multiple human parameters. The system includes a signal acquisition system and a PC system for processing and displaying the acquired signals. The signal acquisition system includes: a TCD module with signal acquisition channels, an EEG / MP module, a sample-and-hold circuit for synchronously sampling the signals output from each acquisition channel, an A / D converter, and a control module for generating A / D conversion control signals and sampling control signals S / H. The method is as follows: A. Set a sampling control signal S / H synchronized with the same clock pulse; B. Use the S / H signal to uniformly control each signal channel to synchronously sample the TCD signal, EEG, and MP signal, so that the sampling points of each signal channel are the same; C. Convert the analog signal samples acquired by synchronous sampling into digital signals and buffer and output them; D. The PC system processes and displays the digital signal.

[0004] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: Moyamoya disease TCD signals are often interfered with by skull echoes and environmental noise, making it difficult to extract effective blood flow characteristics stably, especially in different time periods and frequency bands of the signal. Therefore, analysis of a single frequency band or scale is difficult to capture low-frequency overall perfusion and high-frequency pulsation details at the same time, which limits the fine assessment of the establishment of blood flow collateral pathways. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for identifying the establishment of collateral blood flow in the brain in patients with moyamoya disease based on TCD parameters. The technical solution is as follows: On the one hand, a method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters is provided, the method comprising: S1. Obtain the Doppler spectrum signal of the smoke disease and perform filtering analysis. Analyze the filtering factor of the smoke disease signal and optimize the filtering parameters of the filter.

[0006] S2, perform sliding window segmentation on the filtered smoke disease Doppler spectrum signal to obtain each time domain signal segment of the smoke disease Doppler spectrum signal, and perform dynamic change analysis on each time domain signal segment of the smoke disease Doppler spectrum signal.

[0007] S3 determines the execution requirements of the first window reset adjustment process, monitors the execution effect of the first window reset adjustment process, and obtains the time domain signal segments of the first smoke disease continuous Doppler.

[0008] S4. The first smoke disease continuous Doppler signal segments in each time domain are decomposed into small packets to obtain the sub-band signals of the first smoke disease continuous Doppler signal in each time domain and analyze them. Based on this, the execution requirements of the second window reset adjustment process are determined, the execution effect of the second window reset adjustment process is monitored, and the segments of the second smoke disease continuous Doppler signal in each time domain are obtained.

[0009] S5. Based on the segmentation of continuous Doppler signals in each time domain of the second moyamoya disease, identify the cerebral blood flow side of the moyamoya disease, establish the grading of collateral pathways, and generate a visual report.

[0010] Optionally, the filtering factor of the smoke disease signal is analyzed. The specific process is as follows: acquire the Doppler spectrum signal data of the smoke disease, including the signal-to-noise ratio gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width.

[0011] The signal-to-noise ratio gain and its definition, power spectral kurtosis and its definition, instantaneous frequency variance and its definition, and autocorrelation hysteresis width and its definition are extracted and analyzed for their respective proportions. Weighting coefficients are then introduced to obtain the filtering factor for the smoke disease signal. The filtering factor for the smoke disease signal is used to quantitatively evaluate the overall quality of the Doppler spectrum signal.

[0012] Optionally, the filter parameters of the filter are optimized and adjusted. The specific process is as follows: extract the filter factor of the smoke disease signal and compare it with the filter factor and filter parameter adjustment ratio of the smoke disease signal in the database to obtain the filter parameter adjustment ratio of the smoke disease signal. The filter parameters of the filter are then optimized and adjusted according to the filter parameter adjustment ratio of the smoke disease signal.

[0013] Optionally, dynamic change analysis is performed on each time-domain signal segment of the Mosmosis Doppler spectral signal. Specifically, dynamic data is obtained for each time-domain signal segment of the Mosmosis Doppler spectral signal, including the gradient change of the instantaneous maximum velocity, the relative increase or decrease ratio of the average echo energy, the abrupt difference of the spectral entropy, and the instantaneous transition of the kurtosis ratio.

[0014] The gradient change of instantaneous maximum velocity is compared with the maximum velocity gradient threshold stored in the database, the average echo energy is compared with the energy fluctuation rate threshold stored in the database, the abrupt difference of spectral entropy is compared with the entropy abrupt change threshold stored in the database, and the instantaneous transition of kurtosis ratio is compared with the kurtosis transition threshold stored in the database. These are then analyzed by weighting coefficients and filtering factors for the smoke disease signal to obtain the window trend characterization value of the smoke disease Doppler spectrum signal. The window trend characterization value of the smoke disease Doppler spectrum signal is used to quantitatively evaluate the smoothness and coherence of the signal characteristics evolving over time under the current sliding window parameters.

[0015] Optionally, the first window reset adjustment process execution requirement is determined. The specific process is as follows: extract the window trend representation value of the smoke disease Doppler spectrum signal and compare it with the window trend representation threshold of the smoke disease Doppler spectrum signal in the database. If the window trend representation value of the smoke disease Doppler spectrum signal is higher than or equal to the window trend representation threshold of the smoke disease Doppler spectrum signal, then the first window reset adjustment process execution requirement is set to require a reset adjustment process.

[0016] If the window trend representation value of the smoke disease Doppler spectrum signal is lower than the window trend representation threshold of the smoke disease Doppler spectrum signal, then the first window reset adjustment process execution requirement will be set to not require a reset adjustment process.

[0017] Optionally, the execution effect of the first window reset adjustment process is monitored to obtain the time-domain signal segments of the first smoke disease continuous Doppler signal. The specific process is as follows: a preset window trend detection period is used to obtain the window trend characterization value of the smoke disease Doppler spectrum signal after the reset adjustment process within the window trend detection period. The window trend characterization value of the smoke disease Doppler spectrum signal after the reset adjustment process is subtracted from the window trend characterization threshold of the smoke disease Doppler spectrum signal to obtain the window trend error value of the smoke disease Doppler spectrum signal. If the window trend error value of the smoke disease Doppler spectrum signal is higher than or equal to the preset window trend error threshold of the smoke disease Doppler spectrum signal, then the execution requirement of the first window reset adjustment process is set to require reset adjustment process. If the window trend error value of the smoke disease Doppler spectrum signal is lower than the preset window trend error threshold of the smoke disease Doppler spectrum signal, then the execution requirement of the first window reset adjustment process does not need to be set to require reset adjustment process. Thus, the time-domain signal segments of the first smoke disease continuous Doppler signal are obtained.

[0018] Optionally, the execution requirements of the second window reset adjustment process are determined. The specific process is as follows: within the preset time window, the sub-band signals of each time domain signal of the first smoke disease continuous Doppler are continuously collected, and the second window reset adjustment factor is analyzed.

[0019] Extract the standard value of the second window adjustment factor stored in the database.

[0020] Subtracting the second window reset adjustment factor from the second window reset adjustment factor yields the deviation value of the second window reset adjustment factor.

[0021] Extract the second window stored in the database and reset the adjustment factor deviation threshold.

[0022] If the deviation of the second window reset adjustment factor is lower than the second window reset adjustment factor deviation threshold, the second window reset adjustment process execution requirement is determined to be that no reset adjustment process is required, thereby directly obtaining the second smoke disease continuous Doppler signal segments in each time domain.

[0023] If the deviation of the second window reset adjustment factor is higher than or equal to the second window reset adjustment factor deviation threshold, the second window reset adjustment process execution requirement is determined to require a reset adjustment process. In this way, the smoke disease Doppler spectrum signal is reacquired and filtered to obtain the second smoke disease continuous Doppler time domain signal segments.

[0024] Optionally, the execution effect of the second window reset adjustment process is monitored. The specific process is as follows: a second window trend detection period is preset, and the number of second window reset adjustment factor deviations below the second window reset adjustment factor deviation threshold is counted during the second window trend detection period. If the number of second window reset adjustment factor deviations below the second window reset adjustment factor deviation threshold is higher than or equal to the set deviation number threshold, then the second smoke disease continuous Doppler time domain signal segments are directly obtained.

[0025] If the number of deviations of the second window reset adjustment factor below the second window reset adjustment factor deviation threshold is lower than the set deviation number threshold, then the smoke disease Doppler spectrum signal is reacquired and filtered for analysis to obtain the second smoke disease continuous Doppler time domain signal segments.

[0026] Optionally, the collateral pathway classification is established on the side of cerebral blood flow in Moyamoya disease, and a visualization report is generated. The specific process is as follows: based on the segmentation of each time domain signal of the second Moyamoya disease continuous Doppler, the multidimensional features of each sub-band are extracted, these features are mapped to a predefined collateral pathway classification model, and a visualization report is generated.

[0027] On the other hand, a system for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters is provided, including: The preprocessing and filter parameter optimization module is used to acquire the Doppler spectrum signal of the smoke disease for filtering analysis, analyze the filtering factor of the smoke disease signal, and optimize and adjust the filtering parameters of the filter. The windowed dynamic feature extraction module is used to perform sliding window segmentation on the filtered smoke disease Doppler spectrum signal to obtain each time domain signal segment of the smoke disease Doppler spectrum signal, and to perform dynamic change analysis on each time domain signal segment of the smoke disease Doppler spectrum signal. The first-level window adaptive adjustment module is used to determine the execution requirements of the first window reset adjustment process, monitor the execution effect of the first window reset adjustment process, and obtain the time domain signal segments of the first smoke disease continuous Doppler. The second-level window adaptive adjustment module is used to decompose the time-domain signals of the first smoke disease continuous Doppler into small packets, obtain the sub-band signals of the first smoke disease continuous Doppler signals in each time domain, and analyze them. Based on this, it determines the execution requirements of the second window reset adjustment process, monitors the execution effect of the first window reset adjustment process, and obtains the time-domain signal segments of the second smoke disease continuous Doppler. The collateral grading and visualization report module is used to identify the collateral pathway grading of the cerebral blood flow side of Moyamoya disease based on the segmentation of continuous Doppler signals in each time domain of the second Moyamoya disease, and generate a visualization report.

[0028] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: (1) First, the original transcranial Doppler spectrum signal is acquired, the filter factor is calculated and the bandwidth and notch depth are dynamically adjusted to achieve optimal noise reduction. Then, the cleaned signal is segmented by a sliding window, and the dynamic changes such as instantaneous maximum velocity, echo energy, and spectral entropy are continuously analyzed. Then, the window trend characterization value is used to determine and execute the first window reset to generate a more stable and coherent first set of time domain segments. Then, the first set of segments is decomposed into multiple layers, the features of each sub-band are extracted, and the second round of window reset is triggered to obtain a second set of more balanced segments. This not only suppresses noise to the maximum extent and preserves signal details, but also continuously optimizes window and filter settings for different monitoring environments, thus providing strong technical support for non-invasive, dynamic and high-confidence identification of the establishment of cerebral blood flow collaterals in Moyamoya disease.

[0029] (2) The present invention obtains the filter factor and then dynamically generates the optimized adjustment ratio of bandwidth and notch depth, updates the filter settings proportionally, and takes effect immediately in the next processing. Through this closed-loop adaptive adjustment scheme, the system can balance noise suppression and blood flow detail preservation in real time for different monitoring environments, making the subsequent multi-level time-frequency feature extraction more stable and reliable, and significantly improving the accuracy of identifying the establishment status of cerebral blood flow collateral pathways in moyamoya disease based on TCD parameters.

[0030] (3) The obtained filter factors are combined to form the window trend characterization value, thereby ensuring the sensitive capture and smooth tracking of small changes in hemodynamics through dynamic monitoring and feedback adaptive optimization of the window, and further providing stable, reliable and more accurate feature extraction support for the establishment of TCD parameter-based identification of Moyamoya disease.

[0031] (4) Dynamically determine whether the current window needs to be re-optimized. Through continuous monitoring and dynamic feedback, ensure that the segmented window of the Doppler spectrum signal achieves the optimal balance between time resolution and detail capture capability. This provides a more stable and targeted multi-scale segmentation basis for TCD parameter identification of the establishment of collateral blood flow pathways in Moyamoya disease, ensuring the accurate expression and identification of hemodynamic features. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of a method for identifying the establishment of cerebral blood flow collaterals in moyamoya disease based on TCD parameters, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the logic flow for identifying the establishment of collateral blood flow in the brain in moyamoya disease based on TCD parameters, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the system modules provided in an embodiment of the present invention. Detailed Implementation

[0034] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0035] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0036] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0037] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0038] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0039] This invention provides a method for identifying the establishment of collateral blood flow in the brain in patients with Moyamoya disease based on TCD parameters. For example... Figure 1 The flowchart shown is for a method to identify the establishment of cerebral collateral blood flow in Moyamoya disease based on TCD parameters. The processing flow of this method may include the following steps: S1. Obtain the Doppler spectrum signal of the smoke disease and perform filtering analysis. Analyze the filtering factor of the smoke disease signal and optimize the filtering parameters of the filter.

[0040] S2, perform sliding window segmentation on the filtered smoke disease Doppler spectrum signal to obtain each time domain signal segment of the smoke disease Doppler spectrum signal, and perform dynamic change analysis on each time domain signal segment of the smoke disease Doppler spectrum signal.

[0041] S3 determines the execution requirements of the first window reset adjustment process, monitors the execution effect of the first window reset adjustment process, and obtains the time domain signal segments of the first smoke disease continuous Doppler.

[0042] S4. The first smoke disease continuous Doppler signal segments in each time domain are decomposed into small packets to obtain the sub-band signals of the first smoke disease continuous Doppler signal in each time domain and analyze them. Based on this, the execution requirements of the second window reset adjustment process are determined, and the segments of the second smoke disease continuous Doppler signal in each time domain are obtained.

[0043] S5. Based on the segmentation of continuous Doppler signals in each time domain of the second moyamoya disease, identify the cerebral blood flow side of the moyamoya disease, establish the grading of collateral pathways, and generate a visual report.

[0044] It should be noted that the system dynamically addresses noise interference and frequency imbalance in the signal by combining a window reset adjustment mechanism with multi-scale signal analysis. First, filter optimization enhances signal quality, removes unnecessary noise, and improves the signal-to-noise ratio. Next, in signal analysis, the system uses a sliding window to segment the signal in the time domain, extracting local signal features and monitoring their dynamic changes in real time. If the signal exhibits drastic fluctuations or instability, the system triggers a window reset adjustment process. By adjusting the window length and overlap ratio, it ensures accurate capture of blood flow dynamics even with significant signal changes, effectively identifying low-frequency overall trends and high-frequency local fluctuations. Through window reset adjustment, the system can dynamically adjust its analysis strategy based on signal changes over different time periods, ensuring accurate assessment of collateral blood flow pathway establishment under various monitoring environments, especially in situations with high noise or drastic signal fluctuations, while maintaining high-precision extraction of key blood flow features. This multi-scale analysis method based on window adaptive adjustment solves the problem of traditional methods failing to balance signal stability and detail capture, achieving a more refined and reliable assessment of collateral blood flow establishment in moyamoya disease.

[0045] The filtering factor for the Smoke Disease signal is analyzed by acquiring the Doppler spectrum signal data of the Smoke Disease, including the signal-to-noise ratio gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width.

[0046] It should be noted that the signal-to-noise ratio (SNR) gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width were obtained through multi-dimensional time-domain and frequency-domain analysis of the smoke-induced Doppler spectrum signal. First, to calculate the SNR gain, the system preprocesses the original TCD signal, primarily by removing low-frequency noise and power frequency interference through filters. Then, the power of the filtered signal and the original signal are calculated. The SNR gain represents the degree of power enhancement of the filtered signal relative to the noise; it is calculated by the ratio of the energy of the filtered signal to the energy of the noise before filtering. A higher SNR gain indicates a better denoising effect from the filter, and an effective improvement in signal quality. Next, the power spectral kurtosis is calculated through spectral analysis of the filtered signal. First, the signal is converted to the frequency domain using a Fast Fourier Transform (FFT) to obtain its power spectral density. Power spectral kurtosis is an indicator of the sharpness of a signal's spectrum. It represents the concentration of energy within a specific frequency range of the spectrum. A higher kurtosis value indicates a more prominent spectral characteristic of the signal, which typically reflects the dominant fluctuations of blood flow in a particular frequency band. Further analysis of instantaneous frequency variance requires first performing time-frequency analysis on the filtered signal using Short-Time Fourier Transform (STFT) or wavelet transform to obtain the frequency changes of the signal at different time points. Instantaneous frequency refers to the frequency value of the signal at a specific moment. By performing local analysis of the signal's spectrum, the trajectory of this frequency change over time can be obtained. Instantaneous frequency variance calculates the degree of fluctuation in the signal's frequency change within each time window. A larger variance value indicates more drastic frequency changes and higher signal instability, while a smaller variance indicates more stable frequency changes. This is particularly important for assessing the dynamic characteristics of cerebral blood flow, especially when assessing the stability of collateral blood flow. Finally, autocorrelation hysteresis width requires first performing autocorrelation analysis on the filtered signal. Autocorrelation is an indicator of the similarity between a signal and its own lagged version, revealing the periodicity of the signal. By calculating the autocorrelation function, the autocorrelation values ​​of the signal at different lag times can be obtained, and the width of its main peak can be further analyzed. The autocorrelation lag width reflects the duration of the signal's periodicity; the larger the width, the more obvious the periodicity of the signal and the stronger its stability; if the width is small, it indicates that the signal's periodicity is weak or that it is more affected by noise. In Doppler signal analysis of Moyamoya disease, the autocorrelation lag width can help determine whether cerebral blood flow maintains a stable pulsating pattern or exhibits abnormal fluctuations.

[0047] The signal-to-noise ratio gain and its definition, power spectral kurtosis and its definition, instantaneous frequency variance and its definition, and autocorrelation hysteresis width and its definition are extracted and analyzed for their respective proportions. Weighting coefficients are then introduced to obtain the filtering factor for the smoke disease signal. The filtering factor for the smoke disease signal is used to quantitatively evaluate the overall quality of the Doppler spectrum signal.

[0048] It should be noted that the filtering factor for the smoke disease signal, and the specific analysis conditions, are as follows: ; In the formula, U represents the filtering factor of the smoke disease signal, X1 represents the signal-to-noise ratio gain, X represents the defined signal-to-noise ratio gain set in the database, P2 represents the power spectral kurtosis, P represents the defined power spectral kurtosis set in the database, F3 represents the instantaneous frequency variance, F represents the defined instantaneous frequency variance set in the database, K4 represents the autocorrelation hysteresis width, K represents the defined autocorrelation hysteresis width set in the database, A represents the weighting coefficient corresponding to the signal-to-noise ratio gain set in the database, B represents the weighting coefficient corresponding to the power spectral kurtosis set in the database, C represents the weighting coefficient corresponding to the instantaneous frequency variance set in the database, and D represents the weighting coefficient corresponding to the autocorrelation hysteresis width set in the database.

[0049] It should be noted that in the quality assessment of Doppler spectral signals in Moyamoya disease, the four indicators—signal-to-noise ratio (SNR) gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width—are closely related. For example, increasing the SNR gain usually reduces the instantaneous frequency variance because stronger noise suppression makes frequency fluctuations more concentrated. While increasing spectral kurtosis can highlight the sharp features of the principal components of blood flow, excessive amplification of certain transient peaks can also cause slight jitter in the instantaneous frequency variance. The autocorrelation hysteresis width reflects the integrity of the signal's periodicity. When the SNR gain is too high and too many components are filtered out, the autocorrelation hysteresis width is often compressed, indicating that the periodicity is weakened. Conversely, an appropriate increase in kurtosis will enhance the width of the main peak of the correlation hysteresis curve, making the periodic information clearer and thus suppressing autocorrelation sidelobes. Under the interaction of these four factors, the optimization of filter parameters needs to strike a balance between improving the SNR, enhancing spectral concentration, and maintaining frequency stability and autocorrelation integrity to ensure that a high-quality signal that is both smooth and retains key hemodynamic features is obtained.

[0050] It should be noted that the weighting coefficients corresponding to the average signal-to-noise ratio (SNR) gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width are all stored in the database, and their values ​​are typically set between 0 and 1. For example, by constructing mapping tables between SNR gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width and their weighting coefficients, the real-time detected SNR gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width are input into the corresponding mapping tables in the database, thereby quickly obtaining the weighting coefficients corresponding to the average SNR gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width, respectively.

[0051] The filter parameters of the filter are optimized and adjusted as follows: the filter factor of the smoke disease signal is extracted and compared with the filter factor and filter parameter adjustment ratio of the smoke disease signal in the database to obtain the filter parameter adjustment ratio of the smoke disease signal. The filter parameters are then optimized and adjusted according to the filter parameter adjustment ratio of the smoke disease signal.

[0052] It should be noted that the specific process of optimizing the filter parameters based on the adjustment ratio of the filter parameters of the smoke disease signal is as follows: multiply the current filter parameters of the smoke disease signal by the adjustment ratio of the filter parameters of the smoke disease signal. The filter parameters include the filter bandwidth and notch depth. This yields the adjustment filter parameters of the smoke disease signal to be executed this time. These two new parameters are then sent to the filter module and take effect immediately when the smoke disease Doppler spectrum signal is preprocessed next time, thereby realizing dynamic closed-loop optimization based on real-time signal quality feedback.

[0053] Dynamic changes of each time-domain signal segment of the Moyamoya disease Doppler spectrum signal are analyzed. Specifically, dynamic data of each time-domain signal segment of the Moyamoya disease Doppler spectrum signal are obtained, including the gradient change of the instantaneous maximum velocity, the relative increase or decrease of the average echo energy, the abrupt difference of the spectral entropy, and the instantaneous transition of the kurtosis ratio.

[0054] It should be noted that the gradient change of the instantaneous maximum velocity is obtained by calculating the rate of change of the maximum velocity in the time series of the Doppler signal. First, the system calculates the blood flow velocity at each time point to find the maximum velocity within each time window, and then calculates the velocity difference between adjacent time points, i.e., the gradient change. The relative increase / decrease ratio of the average echo energy is a quantification of the signal intensity change. In a Doppler signal, the echo energy at each moment represents the intensity of blood flow at that time point. By calculating the average echo energy over a period of time and comparing it with the energy of preceding and following time periods, the relative increase / decrease ratio of the echo energy can be obtained. Spectral entropy is used to measure the degree of order in the signal power spectrum; a higher spectral entropy usually indicates a more chaotic frequency distribution, while a lower spectral entropy indicates a more stable frequency distribution. In Doppler signal analysis, the abrupt difference in spectral entropy indicates the degree of abrupt change in the frequency distribution, reflecting whether sudden changes or abnormal events have occurred in the blood flow over time. Finally, the instantaneous jump in the kurtosis ratio refers to the change in the peak value in the signal spectrum. The kurtosis ratio reflects the prominence of peaks in a signal spectrum. In blood flow signals, instantaneous jumps in the kurtosis ratio usually indicate abnormal signal fluctuations or sudden changes within a short period. By analyzing the kurtosis of a signal, sharp changes in blood flow signals can be identified, which may be related to sudden changes in local blood vessels, blood flow instability, or microcirculatory abnormalities.

[0055] The gradient change of instantaneous maximum velocity is compared with the maximum velocity gradient threshold stored in the database, the average echo energy is compared with the energy fluctuation rate threshold stored in the database, the abrupt difference of spectral entropy is compared with the entropy abrupt change threshold stored in the database, and the instantaneous transition of kurtosis ratio is compared with the kurtosis transition threshold stored in the database. These are then analyzed by weighting coefficients and filtering factors for the smoke disease signal to obtain the window trend characterization value of the smoke disease Doppler spectrum signal. The window trend characterization value of the smoke disease Doppler spectrum signal is used to quantitatively evaluate the smoothness and coherence of the signal characteristics evolving over time under the current sliding window parameters.

[0056] It should be noted that the window trend representation value of the Moyamoya disease Doppler spectrum signal is analyzed under the following specific conditions: ; In the formula, The window trend representation value of the Moyamoya disease Doppler spectrum signal, Q 1j Q represents the gradient change of the instantaneous maximum velocity of the j-th time-domain signal segment of the Moyamoya disease Doppler spectrum signal, Q1 represents the maximum velocity gradient threshold stored in the database, and Q 2j Q represents the relative increase or decrease in the average echo energy of the j-th time-domain signal segment of the Moyamoya disease Doppler spectrum signal, Q2 represents the energy volatility threshold stored in the database, and Q 3j Q represents the entropy abrupt change threshold of the j-th time-domain signal segment of the Moyamoya disease Doppler spectrum signal, Q3 represents the energy volatility threshold kurtosis transition threshold stored in the database, and Q 4j This represents the instantaneous transition of the kurtosis ratio of the j-th time-domain signal segment of the Mosmosis Doppler spectrum signal. Q4 represents the kurtosis transition threshold stored in the database. U represents the filter factor of the Mosmosis signal. E1 represents the weighting coefficient corresponding to the gradient change of the instantaneous maximum velocity set in the database. E2 represents the weighting coefficient corresponding to the relative increase or decrease ratio of the average echo energy set in the database. E3 represents the weighting coefficient corresponding to the abrupt difference of the spectral entropy set in the database. E4 represents the weighting coefficient corresponding to the instantaneous transition of the kurtosis ratio set in the database. E5 represents the weighting coefficient corresponding to the filter factor of the Mosmosis signal set in the database. j represents the number of each time-domain signal segment, j=1,2,3,...,n, where n is the total number of time-domain signal segments.

[0057] It should be noted that in the dynamic analysis of characteristic time series, the four indicators—gradient change of instantaneous maximum velocity, relative increase / decrease of average echo energy, abrupt change in spectral entropy, and instantaneous transition of kurtosis ratio—also exhibit complex interrelationships. For example, a sharp increase in the gradient of instantaneous maximum velocity is usually accompanied by a significant rise in average echo energy, as the blood flow impact peak brings a stronger echo signal. Large energy fluctuations lead to abrupt increases in spectral entropy, making the power spectrum distribution more disordered and widespread, thus pushing up the entropy value. Simultaneously, the kurtosis ratio often exhibits sharp transitions under the combined influence of energy and entropy. Kurtosis indicates the presence of short-term peak components in the signal, but if these peaks are excessively amplified, they will be lost in the next moment. Conversely, it suppresses the continuous rise of the instantaneous maximum velocity gradient and causes a brief drop in spectral entropy. Conversely, when spectral entropy suddenly drops, it means that the signal energy is more concentrated, which often reduces the sharp fluctuations in kurtosis ratio and stabilizes the level of average echo energy, thereby suppressing the sharp fluctuations in velocity gradient. Furthermore, if the filter factor is at a low level, it indicates that noise still exists, and the increase or decrease in echo energy and the abrupt change in velocity gradient are easily amplified by noise, resulting in excessive spikes in the instantaneous transition of kurtosis ratio. Conversely, when the filter factor is high, noise suppression is in place, and the influence of velocity gradient and energy changes on spectral entropy and kurtosis can more accurately reflect the physiological impact of collateral blood flow, while a moderate transition in kurtosis ratio will enhance the clarity of the main peak in autocorrelation analysis.

[0058] It should be noted that the weighting coefficients corresponding to the gradient change of instantaneous maximum velocity, the relative increase / decrease of average echo energy, the abrupt difference in spectral entropy, the instantaneous transition of kurtosis ratio, and the filtering factor of the smoke disease signal are all stored in the database, and their values ​​are usually set between 0 and 1. For example, by constructing mapping tables between the gradient change of instantaneous maximum velocity, the relative increase / decrease of average echo energy, the abrupt difference in spectral entropy, the instantaneous transition of kurtosis ratio, and the filtering factor of the smoke disease signal, respectively, the real-time detected gradient change of instantaneous maximum velocity, the relative increase / decrease of average echo energy, the abrupt difference in spectral entropy, and the instantaneous transition of kurtosis ratio, respectively, are input into the corresponding mapping tables in the database, thereby quickly obtaining the weighting coefficients corresponding to the gradient change of instantaneous maximum velocity, the relative increase / decrease of average echo energy, the abrupt difference in spectral entropy, the instantaneous transition of kurtosis ratio, and the filtering factor of the smoke disease signal.

[0059] The specific process for determining the execution requirement of the first window reset adjustment procedure is as follows: extract the window trend representation value of the smoke disease Doppler spectrum signal and compare it with the window trend representation threshold of the smoke disease Doppler spectrum signal in the database. If the window trend representation value of the smoke disease Doppler spectrum signal is higher than or equal to the window trend representation threshold of the smoke disease Doppler spectrum signal, then the execution requirement of the first window reset adjustment procedure is set to require the reset adjustment procedure. If the window trend representation value of the smoke disease Doppler spectrum signal is lower than the window trend representation threshold of the smoke disease Doppler spectrum signal, then the first window reset adjustment process execution requirement will be set to not require a reset adjustment process.

[0060] The execution effect of the first window reset adjustment process is monitored to obtain the time-domain signal segments of the first smoke disease continuous Doppler signal. The specific process is as follows: a preset window trend detection period is set. During the window trend detection period, the window trend representation value of the smoke disease Doppler spectrum signal after the reset adjustment process is obtained. The window trend representation value of the smoke disease Doppler spectrum signal after the reset adjustment process is subtracted from the window trend representation threshold of the smoke disease Doppler spectrum signal to obtain the window trend error value of the smoke disease Doppler spectrum signal. If the window trend error value of the smoke disease Doppler spectrum signal is higher than or equal to the preset window trend error threshold of the smoke disease Doppler spectrum signal, then the execution requirement of the first window reset adjustment process is set to require reset adjustment process. If the window trend error value of the smoke disease Doppler spectrum signal is lower than the preset window trend error threshold of the smoke disease Doppler spectrum signal, then the execution requirement of the first window reset adjustment process does not need to be set to require reset adjustment process. Thus, the time-domain signal segments of the first smoke disease continuous Doppler signal are obtained.

[0061] The determination of the second window reset adjustment process execution requirement is as follows: Within a preset time window, the sub-band signals of each time domain signal of the first smoke disease continuous Doppler are continuously acquired, and the second window reset adjustment factor is analyzed; the standard value of the second window reset adjustment factor stored in the database is extracted; the second window reset adjustment factor deviation value is obtained by subtracting the second window reset adjustment factor from the second window reset adjustment factor; the second window reset adjustment factor deviation threshold value is extracted from the database; if the second window reset adjustment factor deviation is lower than the second window reset adjustment factor deviation threshold, the second window reset adjustment process execution requirement is determined to be that no reset adjustment process is required, thereby directly obtaining the second smoke disease continuous Doppler signal segments in each time domain; if the second window reset adjustment factor deviation is higher than or equal to the second window reset adjustment factor deviation threshold, the second window reset adjustment process execution requirement is determined to be that a reset adjustment process is required, thereby reacquiring the smoke disease Doppler spectrum signal and performing filtering analysis to obtain the second smoke disease continuous Doppler signal segments in each time domain.

[0062] It should be noted that the second window reset adjustment factor works as follows: After wavelet packet decomposition of each time-domain signal, the spectral entropy time series of the decomposed mid-frequency band (usually covering a range of about 2–8 Hz) is extracted. Based on the mid-frequency entropy jitter, sub-band energy distribution, and kurtosis, the variance of this series under the sliding window is calculated. That is, within each sliding window of a preset length, the system first adds up all mid-frequency entropy sample values ​​within the coverage area of ​​the window and divides them by the total number of samples to obtain the average spectral entropy of the window. Then, for each spectral entropy sample in the same window, the difference between it and the average value is calculated and the difference is squared. Finally, all the squared difference terms are summed and divided by the total number of samples. The result is the variance of the spectral entropy sequence within the window. This variance value is defined as the second window reset adjustment factor, which is used to quantify the jitter amplitude of the mid-frequency band spectral entropy under the current window.

[0063] The monitoring of the second window reset adjustment process is as follows: if the second window reset adjustment process is deemed unnecessary, the second smoke disease continuous Doppler signal segments in each time domain are directly obtained; if the second window reset adjustment factor deviation is lower than the second window reset adjustment factor deviation threshold, the second window reset adjustment process is deemed necessary, and the smoke disease Doppler spectrum signal is reacquired and filtered to obtain the second smoke disease continuous Doppler signal segments in each time domain.

[0064] The second window trend detection period is preset. During the second window trend detection period, the number of times the second window reset adjustment factor deviation is lower than the second window reset adjustment factor deviation threshold is counted. If the number of times the second window reset adjustment factor deviation is lower than the second window reset adjustment factor deviation threshold is higher than or equal to the set deviation number threshold, then the second smoke disease continuous Doppler time domain signal segments are directly obtained. If the number of deviations of the second window reset adjustment factor below the second window reset adjustment factor deviation threshold is lower than the set deviation number threshold, then the smoke disease Doppler spectrum signal is reacquired and filtered for analysis to obtain the second smoke disease continuous Doppler time domain signal segments.

[0065] It should be noted that the specific process of reacquiring the Doppler spectrum signal of the Moyamoya disease for filtering analysis is as follows: while retaining the existing window length and overlap ratio, the window length is automatically increased or decreased in small steps according to the direction and magnitude of the most recently calculated adjustment factor deviation, and the overlap ratio is adjusted accordingly. Then, the next fixed-duration second window trend detection cycle is restarted. During this period, the system will continuously monitor the new adjustment factor deviation performance and count the number of deviations that meet the deviation threshold again at the end of each cycle. This cycle is repeated until the number of deviations in multiple consecutive detection cycles meets the convergence condition, at which point the parameter fine-tuning stops and the subsequent feature extraction and side branch recognition process is formally entered.

[0066] To identify the cerebral blood flow side of Moyamoya disease, establish the collateral pathway classification, and generate a visualization report, the specific process is as follows: based on the segmentation of each time domain signal of the second Moyamoya disease continuous Doppler, extract the multidimensional features of each sub-band, map these features to a predefined collateral pathway classification model, and generate a visualization report.

[0067] It should be noted that after completing the segmentation of the second group of continuous Doppler signals for Moyamoya disease in each time domain, the system first extracts multidimensional features from all wavelet subbands of each segmented signal and maps them to a predetermined collateral pathway to establish a grading model to achieve cerebral blood flow collateral pathway grading. Specifically, the system reads the typical feature threshold ranges defined in the database for different grades, including: Grade I collateral (early budding) has a low kurtosis ratio and instantaneous maximum velocity, while the energy fluctuation amplitude is limited; Grade II collateral (moderate compensation) has a slightly decreased spectral entropy and the average echo energy is in the medium range; Grade III collateral (highly compensated) is characterized by a significant increase in energy concentration in multiple frequency bands, a further decrease in spectral entropy, and a significant short-term peak in the velocity gradient; Grade IV collateral (overcompensation or reverse perfusion) may show abnormal artifact features in the high-frequency band and be accompanied by an increase in autocorrelation side peaks. The system will compare the feature values ​​calculated for each time-domain segment in the second group with these grading thresholds sequentially, and score them according to the degree of matching: if a segment simultaneously meets the threshold of Grade II or above in the main indicators (such as instantaneous maximum velocity and spectral entropy) and does not exceed the limit in the secondary indicators (such as energy volatility and kurtosis ratio), then the segment is temporarily judged as a Grade II collateral; if its features gradually approach higher grades in several consecutive segments, the overall grading assessment will be improved to reflect the dynamic evolution characteristics of collateral pathways over time. After completing the grading and scoring of all segments, a final grading result representing the patient's collateral establishment status under this test is output.

[0068] Figure 2 This paper presents a schematic diagram of the logical flow of collateral cerebral blood flow identification based on TCD parameters in Moyamoya disease. It organically combines adaptive filtering with dual-window adjustment, sliding window dynamics analysis, and multi-layer wavelet packet decomposition. By establishing threshold decision and feedback closed loops at multiple key nodes in signal preprocessing, feature extraction, and segmentation determination, it can optimize filter parameters in real time to balance noise suppression and detail preservation, and sensitively capture dynamic changes in cerebral blood flow at different scales. Finally, it outputs highly stable and interpretable collateral pathway classification results, providing accurate and reliable technical support for non-invasive and continuous identification of collateral cerebral blood flow establishment in Moyamoya disease.

[0069] on the other hand, Figure 3 A schematic diagram of a system module for identifying the establishment of cerebral collateral blood flow in Moyamoya disease based on TCD parameters is provided, including: The preprocessing and filter parameter optimization module is used to acquire the Doppler spectrum signal of the smoke disease for filtering analysis, analyze the filtering factor of the smoke disease signal, and optimize and adjust the filtering parameters of the filter.

[0070] The windowed dynamic feature extraction module is used to perform sliding window segmentation on the filtered smoke disease Doppler spectrum signal to obtain each time domain signal segment of the smoke disease Doppler spectrum signal, and to perform dynamic change analysis on each time domain signal segment of the smoke disease Doppler spectrum signal.

[0071] The first-level window adaptive adjustment module is used to determine the execution requirements of the first window reset adjustment process, monitor the execution effect of the first window reset adjustment process, and obtain the time domain signal segments of the first smoke disease continuous Doppler.

[0072] The second-level window adaptive adjustment module is used to decompose the segments of the first smoke disease continuous Doppler signal into small packets, obtain the sub-band signals of the first smoke disease continuous Doppler signal in each time domain, and analyze them. Based on this, it determines the execution requirements of the second window reset adjustment process, monitors the execution effect of the first window reset adjustment process, and obtains the segments of the second smoke disease continuous Doppler signal in each time domain.

[0073] The collateral grading and visualization report module is used to identify the collateral pathway grading of the cerebral blood flow side of Moyamoya disease based on the segmentation of continuous Doppler signals in each time domain of the second Moyamoya disease, and generate a visualization report.

[0074] For ease of explanation, Figure 3 Only the main components of the system are shown. The system of this embodiment can be used to perform... Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0075] In an exemplary embodiment, the present invention also provides an electronic device, the electronic device comprising: processor; The memory stores computer-readable instructions, which, when loaded and executed by the processor, implement the steps of the method described above for identifying the establishment of collateral blood flow in the brain in Moyamoya disease based on TCD parameters.

[0076] In an exemplary embodiment, the present invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the steps of the method described above for identifying the establishment of collateral blood flow in moyamoya disease based on TCD parameters. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0077] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0078] The use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0079] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0080] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0081] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0083] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0087] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters, characterized in that, The method includes: S1. Obtain the Doppler spectrum signal of the smoke disease and perform filtering analysis. Analyze the filtering factor of the smoke disease signal and optimize the filtering parameters of the filter. S2, Perform sliding window segmentation on the filtered smoke disease Doppler spectrum signal to obtain each time domain signal segment of the smoke disease Doppler spectrum signal, and perform dynamic change analysis on each time domain signal segment of the smoke disease Doppler spectrum signal; S3, determine the execution requirements of the first window reset adjustment process, monitor the execution effect of the first window reset adjustment process, and obtain the time domain signal segments of the first smoke disease continuous Doppler. S4, perform small packet decomposition on the segments of the continuous Doppler signal of the first smoke disease to obtain the sub-band signals of the continuous Doppler signal of the first smoke disease and analyze them, thereby determining the execution requirements of the second window reset adjustment process, monitoring the execution effect of the second window reset adjustment process, and obtaining the segments of the continuous Doppler signal of the second smoke disease. S5. Based on the segmentation of continuous Doppler signals in each time domain of the second moyamoya disease, identify the cerebral blood flow side of the moyamoya disease, establish the grading of collateral pathways, and generate a visual report.

2. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The specific process for analyzing the filter factor of the smoke disease signal is as follows: Acquire Doppler spectrum signal data for Moyamoya disease, including signal-to-noise ratio gain, power spectral kurtosis, instantaneous frequency variance, and autocorrelation hysteresis width; The signal-to-noise ratio gain and the defined signal-to-noise ratio gain, the power spectral kurtosis and the defined power spectral kurtosis, the instantaneous frequency variance and the defined instantaneous frequency variance, and the autocorrelation hysteresis width and the defined autocorrelation hysteresis width are extracted and their proportions are analyzed. Weighting coefficients are introduced to obtain the filtering factor of the smoke disease signal. The filtering factor of the smoke disease signal is used to quantitatively evaluate the overall quality of the Doppler spectrum signal.

3. The method for identifying the establishment of collateral blood flow in cerebral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The specific process of optimizing and adjusting the filter parameters of the filter is as follows: The filter factor of the smoke disease signal is extracted and compared with the filter factor and filter parameter adjustment ratio of the smoke disease signal in the database to obtain the filter parameter adjustment ratio of the smoke disease signal. The filter parameters are then optimized and adjusted according to the filter parameter adjustment ratio of the smoke disease signal.

4. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 3, characterized in that, The specific process of performing dynamic change analysis on each time-domain signal segment of the Moyamoya disease Doppler spectrum signal is as follows: Dynamic data were obtained from various time-domain signal segments of the Moyamoya disease Doppler spectrum signal, including gradient changes of instantaneous maximum velocity, relative increases and decreases in average echo energy, abrupt differences in spectral entropy, and instantaneous transitions in kurtosis ratio. The gradient change of instantaneous maximum velocity is compared with the maximum velocity gradient threshold stored in the database, the average echo energy is compared with the energy fluctuation rate threshold stored in the database, the abrupt difference of spectral entropy is compared with the entropy abrupt change threshold stored in the database, and the instantaneous transition of kurtosis ratio is compared with the kurtosis transition threshold stored in the database. These are then analyzed by weighting coefficients and filtering factors for the smoke disease signal to obtain the window trend characterization value of the smoke disease Doppler spectrum signal. The window trend characterization value of the smoke disease Doppler spectrum signal is used to quantitatively evaluate the smoothness and coherence of the signal characteristics evolving over time under the current sliding window parameters.

5. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The specific process for determining the first window reset adjustment process execution requirement is as follows: Extract the window trend representation value of the Doppler spectrum signal of the smoke disease and compare it with the window trend representation threshold of the Doppler spectrum signal of the smoke disease in the database. If the window trend representation value of the Doppler spectrum signal of the smoke disease is higher than or equal to the window trend representation threshold of the Doppler spectrum signal of the smoke disease, then set the first window reset adjustment process execution requirement to require reset adjustment process. If the window trend representation value of the smoke disease Doppler spectrum signal is lower than the window trend representation threshold of the smoke disease Doppler spectrum signal, then the first window reset adjustment process execution requirement will be set to not require a reset adjustment process.

6. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The monitoring of the first window reset adjustment process results in the time-domain signal segments of the first smoke disease continuous Doppler signal. The specific process is as follows: A preset window trend detection period is established. During this period, the window trend representation value of the Doppler spectrum signal of the smoke disease after the reset adjustment process is obtained. The difference between the window trend representation value of the Doppler spectrum signal of the smoke disease after the reset adjustment process and the window trend representation threshold of the Doppler spectrum signal of the smoke disease is calculated to obtain the window trend error value of the Doppler spectrum signal of the smoke disease. If the window trend error value of the Doppler spectrum signal of the smoke disease is higher than or equal to the preset window trend error threshold of the Doppler spectrum signal of the smoke disease, the execution requirement of the first window reset adjustment process is set to require reset adjustment process. If the window trend error value of the Doppler spectrum signal of the smoke disease is lower than the preset window trend error threshold of the Doppler spectrum signal of the smoke disease, the execution requirement of the first window reset adjustment process does not need to be set to require reset adjustment process. Thus, the time domain signal segments of the first continuous Doppler of the smoke disease are obtained.

7. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The specific process for determining the second window reset adjustment process execution requirement is as follows: Within a preset time window, the sub-band signals of each time domain signal of the first smoke disease continuous Doppler were continuously acquired, and the adjustment factor of the second window was analyzed. Retrieve the standard value of the second window reset adjustment factor stored in the database; Subtracting the second window reset adjustment factor from the second window reset adjustment factor yields the deviation value of the second window reset adjustment factor. Extract the second window reset adjustment factor deviation threshold stored in the database; If the deviation of the second window reset adjustment factor is lower than the second window reset adjustment factor deviation threshold, the second window reset adjustment process execution requirement is determined to be that no reset adjustment process is required, thereby directly obtaining the second smoke disease continuous Doppler signal segments in each time domain. If the deviation of the second window reset adjustment factor is higher than or equal to the second window reset adjustment factor deviation threshold, the second window reset adjustment process execution requirement is determined to require a reset adjustment process. In this way, the smoke disease Doppler spectrum signal is reacquired and filtered to obtain the second smoke disease continuous Doppler time domain signal segments.

8. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The specific process for monitoring the effect of the second window reset adjustment procedure is as follows: The second window trend detection period is preset. During the second window trend detection period, the number of times the second window reset adjustment factor deviation is lower than the second window reset adjustment factor deviation threshold is counted. If the number of times the second window reset adjustment factor deviation is lower than the second window reset adjustment factor deviation threshold is higher than or equal to the set deviation number threshold, then the second smoke disease continuous Doppler time domain signal segments are directly obtained. If the number of deviations of the second window reset adjustment factor below the second window reset adjustment factor deviation threshold is lower than the set deviation number threshold, then the smoke disease Doppler spectrum signal is reacquired and filtered for analysis to obtain the second smoke disease continuous Doppler time domain signal segments.

9. The method for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters according to claim 1, characterized in that, The process of identifying the cerebral blood flow side in patients with Moyamoya disease, establishing collateral pathway classification, and generating a visual report is as follows: Based on the segmentation of the continuous Doppler signal of the second moyamoya disease in each time domain, and the extraction of multidimensional features of each sub-band, these features are mapped to a predefined lateral pathway hierarchical model, and a visualization report is generated.

10. A system for identifying the establishment of cerebral collateral blood flow in moyamoya disease based on TCD parameters, using the method described in any one of claims 1-9, characterized in that, include: The preprocessing and filter parameter optimization module is used to acquire the Doppler spectrum signal of the smoke disease for filtering analysis, analyze the filtering factor of the smoke disease signal, and optimize and adjust the filtering parameters of the filter. The windowed dynamic feature extraction module is used to perform sliding window segmentation on the filtered smoke disease Doppler spectrum signal to obtain each time domain signal segment of the smoke disease Doppler spectrum signal, and to perform dynamic change analysis on each time domain signal segment of the smoke disease Doppler spectrum signal. The first-level window adaptive adjustment module is used to determine the execution requirements of the first window reset adjustment process, monitor the execution effect of the first window reset adjustment process, and obtain the time domain signal segments of the first smoke disease continuous Doppler. The second-level window adaptive adjustment module is used to decompose the segments of the first smoke disease continuous Doppler signal in each time domain into small packets, obtain the sub-band signals of the first smoke disease continuous Doppler signal in each time domain and analyze them, thereby determining the execution requirements of the second window reset adjustment process, monitoring the execution effect of the second window reset adjustment process, and obtaining the segments of the second smoke disease continuous Doppler signal in each time domain. The collateral grading and visualization report module is used to identify the collateral pathway grading of the cerebral blood flow side of Moyamoya disease based on the segmentation of continuous Doppler signals in each time domain of the second Moyamoya disease, and generate a visualization report.

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