Visual analysis method for cerebral blood flow velocity signals obtained through long-time monitoring

Abstract

The invention relates to a visual analysis method for cerebral blood flow velocity signals obtained through long-time monitoring, and belongs to the technical field of biomedical signal processing. According to the method, frequency spectrum morphological characteristics of cerebral blood flow velocity signals are extracted, a visual coding system is constructed, multi-dimensional characteristics of the signals are visually displayed in coding modes such as colors and shapes, and the change trend and abnormal distribution of the cerebral blood flow signals in a long-time sequence are clearly displayed through means such as a thermodynamic diagram. A doctor can be helped to quickly position the abnormality of the cerebral blood flow signal, and the diagnosis efficiency and accuracy are improved. The system overcomes the problems that in the prior art, signals are prone to noise interference, and visual analysis is not comprehensive, and is suitable for monitoring and management of brain diseases.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical signal processing, and in particular relates to a visualization analysis method for cerebral blood flow velocity signals acquired through long-term monitoring. Background Art

[0002] In the biomedical field, long-term monitoring of cerebral blood flow velocity is of great significance for the diagnosis, treatment and assessment of various brain diseases. Currently, commonly used cerebral blood flow velocity monitoring technologies such as transcranial Doppler ultrasound (TCD) can obtain cerebral blood flow velocity signals, but there are many problems in the long-term monitoring process. For example, the cerebral blood flow velocity signals obtained by existing technologies are easily interfered with by external environmental noise and the noise of the human body's own physiological activities, resulting in a decrease in signal quality. In terms of signal visualization analysis, existing methods can often only present a simple curve of blood flow velocity changing with time, and cannot intuitively display the multidimensional characteristics of blood flow velocity signals, such as the energy distribution in different frequency bands, the fluctuation pattern of blood flow velocity, etc., which is not conducive to doctors' comprehensive and accurate analysis of the disease.

[0003] Taking the commonly used transcranial Doppler ultrasound (TCD) technology as an example, its most common detection method is manual search. During the test, the clinician holds the probe and places it on the patient's temporal window. The feedback image initially determines the location of the blood vessel. After the patient puts on the probe holder, the angle is adjusted to finally determine the blood vessel orientation. For general testing, the entire search process usually takes more than five minutes. After searching for the target vessel and obtaining a stable blood flow signal, the doctor will intercept one or more rhythmic spectrum images for observation and analysis.

[0004] During long-term monitoring, TCD technology's data processing and analysis methods are relatively simple. Especially when detecting microemboli in blood vessels, recording time should typically exceed one hour after the monitoring head frame is installed and secured to ensure accurate and reliable results. However, any movement of the head frame during this period, such as from a patient's violent coughing, can result in signal loss. Furthermore, there is a lack of effective spectral feature extraction and rapid analysis methods for the large amount of cerebral blood flow velocity signal data collected over a long period of time. Existing numerical analysis methods often only provide basic blood flow velocity parameters, such as average velocity and peak velocity, and display simple numerical curves. Analysis of the spectral morphology of cerebral blood flow signals primarily relies on visual observation of spectral images. This manual observation method is highly inefficient in long-term monitoring scenarios, making it difficult to fully and accurately grasp the complex changes in the spectral morphology of cerebral blood flow signals, potentially hindering the accurate diagnosis and timely intervention of brain diseases. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for visual analysis of cerebral blood flow velocity signals acquired through long-term monitoring, so as to solve the problems in the prior art that cerebral blood flow velocity signal monitoring is susceptible to noise interference and visual analysis cannot fully present multi-dimensional features.

[0006] The technical solution adopted by the present invention is a method for visually analyzing cerebral blood flow velocity signals acquired through long-term monitoring, comprising the following steps:

[0007] Step S1: signal acquisition, providing data basis for subsequent analysis and processing;

[0008] Step S2, preprocessing the signal to remove noise interference in the original signal of cerebral blood flow velocity obtained in S1;

[0009] Step S3, extracting waveform features of the cerebral blood flow velocity signal;

[0010] Step S4: Designing a visual coding for the characteristics of a cycle of the cerebral blood flow velocity signal.

[0011] Furthermore, in S2, a fourth-order Butterworth filter is used to remove noise interference in the original signal of cerebral blood flow velocity.

[0012] Furthermore, in step S3, the cerebral blood flow velocity signal pre-processed in step S2 is subjected to period division, signal quality assessment, and feature point identification to obtain a cerebral blood flow velocity signal with the spectral feature identification point identified. The specific steps are as follows:

[0013] S31, dividing the cerebral blood flow signal after noise reduction into periods;

[0014] S32, quality assessment of the cerebral blood flow signal after completing the cycle division;

[0015] S33, feature point identification, uses the second-order derivative extreme point identification algorithm to perform first-order and second-order derivative processing on the cerebral blood flow velocity signal within a cycle, and accurately identifies the "three peaks and one valley" by analyzing the derivative situation.

[0016] Furthermore, the specific steps of identifying the feature points in S33 include:

[0017] S33a, take the absolute value v(t) of the cerebral blood flow velocity signal x(t) within one cycle, and the time range of x(t) is [t start ,t end ], where t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within one cycle;

[0018] S33b, calculate the first-order derivative of v(t), which is used to characterize the change in signal slope. The expression is:

[0019]

[0020] Where f′(t) represents the slope of the cerebral blood flow velocity signal v(t) at time t;

[0021] Because the cerebral blood flow velocity signal is actually a discrete signal, the central difference method is used for discretization calculation. The specific formula is as follows:

[0022]

[0023] Where Δt represents the sampling time interval (unit: s), N represents the total number of sampling points in the period, and t i represents the time of the i-th sampling point, t i+1 and t i-1 Respectively represent the time of the i+1th and i-1th sampling points, f′(t i ) represents the cerebral blood flow velocity signal v(t) at the time t of the i-th sampling point i The slope at

[0024] S33c, calculate the second-order derivative of v(t) to characterize the change in signal curvature, and the expression is:

[0025]

[0026] Where f″(t) represents the curvature of the cerebral blood flow velocity signal v(t) at time t;

[0027] Perform discretization calculations:

[0028]

[0029] Among them, Δt represents the sampling time interval, N represents the total number of sampling points in the period, and t i represents the time of the i-th sampling point, t i+1 and t i-1 Respectively represent the time of the i+1th and i-1th sampling points, f″(t i ) represents the cerebral blood flow velocity signal v(t) at the time t of the i-th sampling point i The curvature at

[0030] S33d, determine the candidate points of the peak and valley based on the first and second order derivatives of v(t);

[0031] The candidate points of the peak need to meet the following conditions:

[0032] f′(t p )=0 and f″(tp )<0

[0033] Among them, t p represents the time of the peak candidate point, f′(t p ) represents the cerebral blood flow signal at time t p The slope at , f″(t p ) represents the cerebral blood flow velocity signal at time t p The curvature at

[0034] The candidate points of the valley bottom need to meet the following conditions:

[0035] f′(t v )=0 and f″(t v )>0

[0036] Among them, t v represents the moment of the valley candidate point, f′(t v ) represents the cerebral blood flow signal at time t v The slope at , f″(t v ) represents the cerebral blood flow velocity signal at time t v The curvature at

[0037] S33e, screening candidate points, setting feature point screening rules from two aspects: time constraint and amplitude constraint;

[0038] S33f, performing conflict resolution on the conflicting feature points selected in S33e, and finally obtaining the feature points corresponding to the moment, as follows:

[0039] For dicrotic notch conflicts, the time of occurrence is:

[0040]

[0041] For diastolic peak conflicts, only the peak with the largest amplitude after the dicrotic notch is retained as the D peak. The time when the D peak appears is:

[0042]

[0043] For systolic conflicts, the peak with the largest amplitude is selected as S1. Among the remaining candidate points, the one with the closest timing to S1 and the second largest amplitude is selected as S2. The time when the S1 peak appears is:

[0044]

[0045] The time when the S2 peak appears is screened among the remaining peak candidate points, and its appearance time is:

[0046]

[0047] Among them, t Nis the moment corresponding to the diastolic dicrotic notch, t S1 is the moment corresponding to the systolic peak S1, t S2 is the moment corresponding to the secondary systolic peak, t D is the moment corresponding to the diastolic peak, T cycle represents the total duration of a single cycle, Δt tol represents the allowable time error threshold, t p is the moment of the peak candidate point, t v is the moment of the valley candidate point, argmax{·} is to find the value in the input variable that makes the function value reach the maximum. t start is the starting time of the cerebral blood flow velocity signal within a cycle, t end is the ending time of the cerebral blood flow velocity signal within a cycle. v(t p ) represents the cerebral blood flow velocity value at the peak candidate point, v(t v ) represents the cerebral blood flow velocity value at the valley candidate point;

[0048] S34, obtaining the abnormal spectral morphology feature points based on the moment when the feature points obtained from S33f appear, including abnormal peak arrival time, abnormal peak value, abnormal S1 < S2, abnormal fusion of S1 and S2, and abnormal blood flow direction, specifically as follows:

[0049] Abnormal peak arrival time: Subtract the moments corresponding to the peak points and starting points of the S1 peak, S2 peak, and D peak. If the difference is greater than the set threshold, it is determined that the peak arrival time is abnormal, that is:

[0050] ΔT S1 >ΔT th_S1 or ΔT S2 >ΔT th_S2 or ΔT D >Δt th_D

[0051] ΔT S1 =t S1 -t start ,ΔT S2 =t S2 -t start ,ΔT D =t D -t start

[0052] Abnormal peak value: As long as one of the S1 peak, S2 peak, and D peak exceeds the set threshold, it is recognized as an abnormal peak value, that is: [[ID = 66]] [[ID = 67]]

[0053] [[ID = 68]]v(t[[ID = 69]] S1 [[ID = 70]])>V[[ID = 71]] th_S1 [[ID = 72]]or v(t[[ID = 73]] S2 [[ID = 74]])>V [[ID = 75]] th_S2 [[ID = 76]]or v(t [[ID = 77]] D)>V th_D

[0054] S1 < S2 anomaly: By comparing the peak values of the S1 peak and the S2 peak, when the peak value of the S1 peak is less than that of the S2 peak, this anomaly occurs, i.e.:

[0055] v(t S1 ) < v(t S2 )

[0056] S1 and S2 fusion anomaly: If the corresponding times of the S1 peak and the S2 peak are relatively close or even equal, it is determined that S1 and S2 are fused, i.e.:

[0057] |t S1 -t S2 |< Δt th_tol

[0058] Blood flow direction anomaly: When the S1 peak, S2 peak, and D peak do not have the same direction, or the blood flow is extremely weak or even disappears, it is recognized as a blood flow direction anomaly. For the same-direction anomaly:

[0059] sign(x(t S1 )) ≠ sign(x(t S2 )) or sign(x(t S1 )) ≠ sign(x(t D )) or

[0060] sign(x(t S2 )) ≠ sign(x(t D ))

[0061] For the anomaly of extremely weak or disappearing cerebral blood flow velocity signal:

[0062] v(t S1 ) < V min or v(t S2 ) < V min or v(t D ) < V min

[0063] Among them, ΔT S1 represents the peak arrival time of S1, ΔT S2 represents the peak arrival time of S2, ΔT D represents the peak arrival time of S1, ΔT th_S1 represents the peak arrival time threshold set for the S1 peak, ΔT th_S2 represents the peak arrival time threshold set for the S2 peak, ΔT th_D represents the peak arrival time threshold set for the D peak, t S1 is the time corresponding to the systolic peak Sl, t S2 is the time corresponding to the secondary systolic peak, t Dis the time corresponding to the diastolic peak, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, v(t S1 ) represents the non-negative cerebral blood flow signal value of S1 peak, v(t S2 ) represents the non-negative cerebral blood flow signal value of S2, v(t D ) represents the non-negative cerebral blood flow signal value of D peak, V th_S1 Indicates the peak threshold set for the S1 peak, V th_S2 Indicates the peak threshold set for peak D, V th_D Indicates the peak threshold set for peak D, Δt th_tol is the time overlap threshold, sign(·) represents the sign function, x(t S1 ) represents the cerebral blood flow velocity signal value of S1 without absolute value processing, x(t S2 ) represents the cerebral blood flow velocity signal value of S2 without absolute value processing, x(t D ) represents the cerebral blood flow velocity signal value without absolute value processing, V min The lower limit threshold of the signal amplitude is set.

[0064] Furthermore, the screening rules described in S33e are as follows:

[0065] For time constraints, the moments when feature points appear need to strictly meet the time sequence:

[0066] t S1 <t S2 <t N <t D

[0067] The timing of the dicrotic notch is around 2 / 3 of the cycle length, that is:

[0068]

[0069] T cycle =t start -t end

[0070] For amplitude constraints, the amplitude of the feature points must strictly satisfy:

[0071] v(t S1 )>v(t S2 )>v(t D )>v(t N )

[0072] The amplitude of the S1 peak is the maximum within the cycle, the amplitude of the S2 peak is second, and the amplitude of the D peak is the maximum after the dicrotic notch, that is:

[0073] v(t S1 )=max{v(t)|t∈[t start ,t end ]}

[0074] v(t S2 )=max{v(t)|t∈[t S1 ,t end ]}

[0075] v(t D )=max{v(t)|t∈[t N ,t end ]}

[0076] Among them, t S1 is the time corresponding to the systolic peak S1, t S2 is the time corresponding to the secondary peak of the systolic period, t N is the time corresponding to the diastolic dicrotic notch, t D is the time corresponding to the diastolic peak, v(t S1 ) represents the cerebral blood flow velocity value of the systolic peak S1, v(t S2 ) represents the cerebral blood flow velocity value of the secondary peak S2 of the systolic period, v(t D ) represents the cerebral blood flow velocity value of the diastolic peak D, v(t N ) represents the cerebral blood flow velocity value corresponding to the dicrotic notch, v(t) represents the cerebral blood flow velocity signal after taking the absolute value, max{·} represents the maximum value, t represents the time, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, T cycle Indicates the total duration of a single cycle, Δt tol Indicates the allowed time error threshold.

[0077] Furthermore, the step S4 is to design visual coding based on the abnormal spectral morphological feature points extracted in step S34, specifically including:

[0078] S41, a feature visualization coding design for a cycle of cerebral blood flow velocity signals, uses different visual symbols to represent different abnormality types, including:

[0079] Normal peak: appears as a single point;

[0080] Delayed peak time: represented by two wavy lines "~" followed by the corresponding peak point;

[0081] High peak: represented by an upward arrow;

[0082] Low peak value: represented by a downward arrow;

[0083] S1 < S2: represented by a slant line with the left end lower and the right end higher, and the two ends of the slant line correspond to the two peak points of S1 peak and S2 peak;

[0084] Fusion of S1 and S2 peaks: represented by a thick dot;

[0085] Abnormal blood flow direction: different visual symbols are used to represent normal blood flow direction, weak or even disappearing blood flow, and reverse blood flow.

[0086] S42, for the abnormal rapid positioning of a time series of cerebral blood flow velocity signals monitored for a long time, a heat map is used for visual coding design, specifically including:

[0087] A specific color mapping system is used to distinguish different abnormal types, and a specific color is used to represent the overall abnormality;

[0088] The density of abnormalities occurring in each cycle is reflected by the change in the depth of color, and the darker the color, the more abnormal conditions there are.

[0089] Furthermore, the overall abnormality is reflected by the number of times of abnormal appearance of each spectral morphological feature point within a cycle, and the difference in the overall abnormality degree between different cycles is intuitively displayed through the change in the depth of color.

[0090] Advantages of the present invention:

[0091] 1. The present invention extracts the spectral morphological features of the cerebral blood flow velocity signal in a complete cycle, divides each feature and its abnormal condition of the cerebral blood flow velocity signal into several visual coding units, and performs color mapping coding on various abnormalities and overall abnormalities occurring in the long-term detected cerebral blood flow signal sequence, facilitating users to quickly locate and view details of the abnormalities.

[0092] 2. The present invention constructs a visual coding system highly adapted to the spectral morphological features of the cerebral blood flow velocity signal, and uses different colors, shapes or patterns for coding different feature points and feature parameters, enabling complex cerebral blood flow information to be presented in an intuitive and easy-to-understand visual form, greatly improving the efficiency and accuracy of information transmission, overcoming the obscurity of traditional data display methods, and enabling non-professionals (such as patients, ordinary medical staff) to quickly understand the key information of cerebral blood flow signals.

[0093] 3. This invention designs a visualization method for long-term monitoring data of cerebral blood flow velocity signals, which can effectively integrate and display long-term series of cerebral blood flow data. Through the visualization of heat maps, it clearly presents the changing trends and abnormal distribution of cerebral blood flow signals over different time periods, helping researchers and doctors observe the long-term dynamic changes in cerebral blood flow. By quantifying and color-mapping the overall degree of abnormality, and by displaying different types of abnormalities in detail on the timeline, it can quickly locate high-incidence time periods and abnormality type combinations, providing strong support for timely intervention measures. This has significant advantages in the monitoring and management of cerebral blood flow-related diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0095] Figure 1 Flowchart of the present invention.

[0096] Figure 2 This is a diagram showing the noise reduction effect of the cerebral blood flow velocity signal of the present invention.

[0097] Figure 3 This is a diagram of the cycle segmentation steps of the present invention.

[0098] Figure 4 This is a visualization diagram of the feature extraction of the present invention.

[0099] Figure 5 This is a design diagram of the abnormal visual coding of the spectrum morphological feature points of the present invention.

[0100] Figure 6 This is a long-term visual coding design diagram of the present invention.

[0101] Figure 7 This is a reference diagram for the long-term encoding visualization effect of the present invention.

[0102] Figure 8 This is a reference diagram of the visual coding effect of the cerebral blood flow velocity envelope diagram corresponding to different blood flow conditions of the present invention.

[0103] Figure 9 This is a reference diagram for the visualization effect of the long-term visualization coding and cerebral blood flow velocity envelope feature coding corresponding to a cerebral blood flow velocity time series of the present invention. DETAILED DESCRIPTION

[0104] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0105] Example

[0106] like Figures 1 to 9 As shown, an embodiment of the present invention provides a method for visually analyzing cerebral blood flow velocity signals acquired through long-term monitoring, and the steps are as follows:

[0107] Step S1, signal acquisition, select appropriate equipment to accurately and continuously obtain the original signal of the patient's cerebral blood flow velocity, providing a data basis for subsequent analysis and processing.

[0108] For example, a transcranial Doppler ultrasound (TCD) device is a commonly used noninvasive cerebral blood flow monitoring tool that utilizes the Doppler effect of ultrasound to measure blood flow velocity within intracranial vessels. Before signal acquisition, necessary patient preparation is required, such as cleansing the skin to ensure good contact between the probe and the skin. Following standard clinical procedures, the TCD probe is placed at specific acoustic windows on the patient's head, such as the temporal window or occipital window. These locations allow ultrasound waves to better penetrate the skull and reach the intracranial vessels. Cerebral blood flow velocity signals are then continuously acquired over a long period of time. During acquisition, the patient should be kept as still as possible to minimize external interference and the impact of their own movements on the signal. The acquired raw signal is recorded as an electrical signal and transmitted to a data acquisition terminal, which typically performs a preliminary analog-to-digital conversion, converting the analog signal into a digital signal for subsequent processing.

[0109] Step S2, signal preprocessing, uses a fourth-order Butterworth filter to perform bandpass filtering on the cerebral blood flow velocity signal to remove noise interference in the original cerebral blood flow velocity signal obtained in S1, thereby improving the signal quality. The original signal will inevitably be affected by external environmental noise during the acquisition process, such as electromagnetic interference, heartbeat, breathing, etc. These noises will mask the true characteristics of the signal, so noise reduction processing is required. The final denoising effect of the present invention is as follows: Figure 2 As shown, Figure 2 In the figure, Original CBFV is the original cerebral blood flow velocity signal, and filtered CBFV is the cerebral blood flow velocity signal after noise reduction by a fourth-order Butterworth filter.

[0110] Step S3 extracts the waveform features of the cerebral blood flow velocity signal. The cerebral blood flow velocity signal preprocessed in step S2 is input into a cycle segmentation algorithm for cycle division, signal quality assessment, and feature point identification. The cerebral blood flow velocity signal with the identified spectral feature points is obtained, namely, the "three peaks and one valley" signal. The "three peaks" refer to the S1 peak, S2 peak, and D peak, and the "one valley" refers to the dicrotic notch. The specific steps are as follows:

[0111] S31, divide the cerebral blood flow signal after noise reduction into periods. The specific steps include: a. using a moving differential filter to enhance the steep rising edge of the starting pulse of the cerebral blood flow velocity signal after noise reduction; b. locating the search window and narrowing the search range of the starting position of the cerebral blood flow velocity pulse by setting the threshold and refractory period; c. analyzing the signal details within the search window, finding the valley point that meets the conditions, and determining it as the pulse starting point.

[0112] S32, perform quality assessment on the cerebral blood flow signal after cycle division, the specific steps include: a. Normalize the cycle length of the cerebral blood flow signal after cycle division to eliminate the influence of cycle length difference on feature calculation; b. Calculate the average pulse waveform to obtain the average cerebral blood flow velocity signal of one cycle as a template; c. Calculate the Euclidean distance, cross-correlation coefficient and pulse length of the template cerebral blood flow signal; d. Perform outlier detection on the Euclidean distance, cross-correlation coefficient and pulse length using the interquartile range method to filter out low-quality signals.

[0113] The calculations in steps S31 to S32 and the fourth-order Butterworth filter in step S2 are both prior arts.

[0114] S33, feature point identification, uses the second-order derivative extreme point identification algorithm to perform first-order and second-order derivative processing on the cerebral blood flow velocity signal within a cycle. By analyzing the derivative situation, the "three peaks and one valley" are accurately identified, among which the systolic peak (S1 peak) is located at the top of the ascending branch and is the highest peak. The systolic secondary peak (S2 peak) is located after the depression in the descending branch of the systolic period, and its amplitude is lower than S1. The dicrotic notch (point N) is located in the upper 2 / 3 of the descending branch. The appearance of the dicrotic notch marks the end of the systolic period and the beginning of the diastolic period. The diastolic peak (D peak) is the peak that appears after the rearrangement notch, and its amplitude is lower than S1 and S2. The specific steps include:

[0115] S33a, take the absolute value v(t) of the cerebral blood flow velocity signal x(t) within one cycle to eliminate the influence of directionality (the signal has directionality, a positive value indicates direction toward the monitoring probe, and a negative value indicates direction away from the monitoring probe). The time range of x(t) is [t start ,t end ], where t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t endIndicates the end time of the cerebral blood flow velocity signal within one cycle.

[0116] S33b, calculate the first-order derivative of v(t), which is used to characterize the change in signal slope. The expression is:

[0117]

[0118] Wherein, f′(t) represents the slope of the cerebral blood flow velocity signal v(t) at time t.

[0119] Because the cerebral blood flow velocity signal is actually a discrete signal, the central difference method is used for discretization calculation. The specific formula is as follows:

[0120]

[0121] Where Δt represents the sampling time interval (unit: s), N represents the total number of sampling points in the period, and t i represents the time of the i-th sampling point, t i+1 and t i-1 Respectively represent the time of the i+1th and i-1th sampling points, f′(t i ) represents the cerebral blood flow velocity signal v(t) at the time t of the i-th sampling point i The slope at .

[0122] S33c, calculate the second-order derivative of v(t) to characterize the change in signal curvature, and the expression is:

[0123]

[0124] Where f″(t) represents the curvature of the cerebral blood flow velocity signal v(t) at time t.

[0125] Perform discretization calculations:

[0126]

[0127] Where Δt represents the sampling time interval (unit: s), N represents the total number of sampling points in the period, and t i represents the time of the i-th sampling point, t i+1 and t i-1 Respectively represent the time of the i+1th and i-1th sampling points, f″(t i ) represents the cerebral blood flow velocity signal v(t) at the time t of the i-th sampling point i The curvature of the .

[0128] S33d, determining the candidate points of the peak and valley based on the first and second order derivatives of v(t).

[0129] The candidate points of the peak top may be the candidate points of the S1 peak, S2 peak and D peak. These candidate points need to meet the following conditions:

[0130] f′(t p )=0 and f″(t p )<0

[0131] Among them, t p represents the time of the peak candidate point, f′(t p ) represents the cerebral blood flow signal at time t p The slope at , f″(t p ) represents the cerebral blood flow velocity signal at time t p The curvature of the .

[0132] The candidate point of the valley bottom may be a candidate point of the dicrotic notch, which needs to meet the following conditions:

[0133] f′(t v )=0 and f″(t v )>0

[0134] Among them, t v represents the moment of the valley candidate point, f′(t v ) represents the cerebral blood flow signal at time t v The slope at , f″(t v ) represents the cerebral blood flow velocity signal at time t v The curvature of the .

[0135] S33e, screen the candidate points to determine the required feature points. Set the feature point screening rules from two aspects: time constraint and amplitude constraint, as follows:

[0136] For time constraints, the moments when feature points appear need to strictly meet the time sequence:

[0137] t S1 <t S2 <t N <t D

[0138] The timing of the dicrotic notch is around 2 / 3 of the cycle length, that is:

[0139]

[0140] T cycle =t start -t end

[0141] For amplitude constraints, the amplitude of the feature points must strictly satisfy:

[0142] v(t S1 )>v(tS2 )>v(t D )>v(t N )

[0143] The amplitude of the S1 peak is the maximum within the cycle, the amplitude of the S2 peak is second, and the amplitude of the D peak is the maximum after the dicrotic notch, that is:

[0144] v(t S1 )=max{v(t)|t∈[t start ,t end ]}

[0145] v(t S2 )=max{v(t)|t∈[t S1 ,t end ]}

[0146] v(t D )=max{v(t)|t∈[t N ,t end ]}

[0147] Among them, t S1 is the time corresponding to the systolic peak S1, t S2 is the time corresponding to the secondary peak of the systolic period, t N is the time corresponding to the diastolic dicrotic notch, t D is the time corresponding to the diastolic peak, v(t S1 ) represents the cerebral blood flow velocity value of the systolic peak S1, v(t S2 ) represents the cerebral blood flow velocity value of the secondary peak S2 of the systolic period, v(t D ) represents the cerebral blood flow velocity value of the diastolic peak D, v(t N ) represents the cerebral blood flow velocity value corresponding to the dicrotic notch, v(t) represents the cerebral blood flow velocity signal after taking the absolute value, max{·} represents the maximum value, t represents the time, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, T cycle Indicates the total duration of a single cycle, Δt tol Indicates the allowed time error threshold.

[0148] S33f, performing conflict resolution on the conflicting feature points selected in S33e, and finally obtaining the feature points corresponding to the moment, as follows:

[0149] For the dicrotic notch conflict, if there are multiple valley candidate points C N ={t N1 ,t N2 ,...,t N k}, tNk It represents the kth valley candidate point. The valley candidate point at 2 / 3 of the cycle length and the deepest is preferentially selected as the dicrotic notch. The time when the dicrotic notch appears is:

[0150]

[0151] For the allowed time error threshold Δt tol , the present invention preliminarily sets Δt tol =0.05T cycle , that is, take ±5% of the cycle length as the allowed time error threshold. If no candidate point falls within this range, the time error threshold is extended to Δt′ tol =2Δt tol , and then search again, and so on recursively until it is found.

[0152] For diastolic peak conflict, if the diastolic window [t N ,t end ]There are multiple peak candidate points C D ={t D1 ,t D2 ,...,t Dl Only the peak with the largest amplitude after the dicrotic notch is retained as the D peak. The time when the D peak appears is:

[0153]

[0154] For systolic conflict, if the systolic window [t start ,t N ]There are multiple peak candidate points C S ={t S1 ,t S2 ,...,t Sm}, t Sm It represents the m-th peak candidate point. The peak with the largest amplitude is selected as S1. Among the remaining candidate points, the one with the closest timing to S1 and the second largest amplitude is S2. The time when the S1 peak appears is:

[0155]

[0156] The moment when the S2 peak appears is at the remaining peak candidate point C′ S =C S \{t S1}, the time of its appearance is:

[0157]

[0158] Among them, t N is the time corresponding to the diastolic dicrotic notch, t S1 is the time corresponding to the systolic peak S1, t S2is the moment corresponding to the systolic sub-peak, t D is the moment corresponding to the diastolic peak, T cycle represents the total duration of a single cycle, Δt tol represents the allowable time error threshold, t p represents the moment of the peak candidate point, t v represents the moment of the valley candidate point, argmax{·} is to find the value in the input variable that makes the function value reach the maximum. t start represents the start time of the cerebral blood flow velocity signal within a cycle, t end represents the end time of the cerebral blood flow velocity signal within a cycle. v(t p ) represents the cerebral blood flow velocity value at the peak candidate point, v(t v ) represents the cerebral blood flow velocity value at the valley candidate point,

[0159] Through the derivative analysis and rule constraints of the above steps S33a - S33f, the robust recognition of the characteristic points of the single-cycle cerebral blood flow signal is achieved, providing high-precision parameter support for clinical diagnosis and also providing a basis for the subsequent visualization of characteristic points.

[0160] S34. Based on the moments when the characteristic points obtained from S33f appear, obtain typical abnormal spectral morphological characteristic points, including abnormal peak arrival time, abnormal peak value, abnormal S1 < S2, abnormal fusion of S1 and S2, and abnormal blood flow direction, as follows:

[0161] Abnormal peak arrival time: Subtract the moments corresponding to the peak points and start points of the S1 peak, S2 peak, and D peak. If the difference is greater than the set threshold, it is determined as an abnormal peak arrival time, that is:

[0162] ΔT S1 >ΔT th_S1 or ΔT S2 >ΔT th_S2 or ΔT<L D >ΔT th_D

[0163] ΔT S1 =t S1 [[ID=X8]]-t start ,ΔT S2 =t S2 -t start ,ΔT D =t D -t start

[0164] [[ID=6X]]Abnormal peak value: If any one of the S1 peak, S2 peak, and D peak exceeds the set threshold, it is identified as an abnormal peak value, that is:

[0165] v(t S1 )>V th_s1or v(t S2 ) > V th_s2 or v(t D ) > V th_D

[0166] S1 < S2 anomaly: Comparing the peak values of S1 peak and S2 peak, when the peak value of S1 peak is less than that of S2 peak, this anomaly occurs, that is:

[0167] v(t S1 ) < v(t S2 )

[0168] S1 and S2 fusion anomaly: If the corresponding times of S1 peak and S2 peak are relatively close or even equal, it is determined that S1 and S2 are fused, that is:

[0169] |t S1 - t S2 | < Δt th_tol

[0170] Blood flow direction anomaly: When the S1 peak, S2 peak, and D peak do not have the same direction, or the blood flow is extremely weak or even disappears, it is recognized as a blood flow direction anomaly. For the same - direction anomaly:

[0171] sign(x(t S1 )) ≠ sign(x(t S2 )) or sign(x(t S1 )) ≠ sign(x(t D )) or

[0172] sign(x(t S2 )) ≠ sign(x(t D ))

[0173] For the anomaly of extremely weak or disappearing cerebral blood flow velocity signal:

[0174] v(t S1 ) < V min or v(t S2 ) < V min or v(t D ) < V min

[0175] Among them, ΔT S1 represents the peak - arrival time of S1, ΔT S2 represents the peak - arrival time of S2, ΔT D represents the peak - arrival time of S1, ΔT th_S1 represents the peak - arrival time threshold set for the S1 peak, ΔT th_S2 represents the peak - arrival time threshold set for the S2 peak, ΔT th_D represents the peak - arrival time threshold set for the D peak, t S1is the time corresponding to the systolic peak S1, t S2 is the time corresponding to the secondary peak of the systolic period, t D is the time corresponding to the diastolic peak, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, v(t S1 ) represents the non-negative cerebral blood flow signal value of S1 peak, v(t S2 ) represents the non-negative cerebral blood flow signal value of S2, v(t D ) represents the non-negative cerebral blood flow signal value of D peak, V th_s1 Indicates the peak threshold set for the S1 peak, V th_S2 Indicates the peak threshold set for peak D, V th_D Indicates the peak threshold set for peak D, Δt th_tol is the time overlap threshold, sing(·) represents the sign function, x(t S1 ) represents the cerebral blood flow velocity signal value of S1 without absolute value processing, x(t S2 ) represents the cerebral blood flow velocity signal value of S2 without absolute value processing, x(t D ) represents the cerebral blood flow velocity signal value without absolute value processing, V min The lower limit threshold of the signal amplitude is set.

[0176] After completing the above algorithm, feature visualization coding design of a cycle of cerebral blood flow velocity signal and abnormal rapid positioning visualization coding design of a cerebral blood flow velocity signal time series monitored for a long time are performed based on the feature extraction of the above algorithm.

[0177] Step S4, for a cycle of cerebral blood flow velocity signal feature visualization coding design, based on the spectrum morphology feature point abnormality of S34 visual coding design, such as Figure 5 As shown,

[0178] Visual coding of abnormal blood flow direction Figure 5 (b) is shown below, and the specific explanation is as follows:

[0179] Normal peak: Appears as a single point, indicating that the peak value of cerebral blood flow velocity is within the normal range, without time delay or amplitude abnormality.

[0180] Peak time delay: manifested as two wavy lines “~” followed by the corresponding peak point.

[0181] High peak value: It is represented by an upward arrow, indicating that the peak value of cerebral blood flow velocity is higher than the normal range.

[0182] Low peak value: represented by a downward arrow, indicating that the peak value of cerebral blood flow velocity is lower than the normal range.

[0183] S1 < S2: It appears as a slanting line with a lower left and a higher right. The two ends of the slanting line correspond to the two peak points of S1 peak and S2 peak.

[0184] Fusion of S1 and S2 peaks: It appears as a relatively thick dot. At this time, the two peak points of S1 peak and S2 peak are merged from two normal points into a relatively thick dot.

[0185] Blood flow direction:

[0186] Case 1 is normal (towards the direction of the ultrasonic probe). It has a normal cerebral blood flow velocity spectrum pattern, where S1 and S2 peaks appear during the cardiac systolic phase, D peak appears during the cardiac diastolic phase, and the blood flow direction is normal and consistent with the direction of the ultrasonic probe.

[0187] Case 2 is normal (opposite to the direction of the ultrasonic probe). It also has a normal cerebral blood flow velocity spectrum pattern, where S1 and S2 peaks appear during the cardiac systolic phase, D peak appears during the cardiac diastolic phase, and the blood flow directions are all below the baseline, opposite to the direction of the ultrasonic probe.

[0188] Case 3: The blood flow is weak or even disappears (no direction). It occurs during the systolic phase or diastolic phase or both. A dotted line is used to indicate weak or even disappearing blood flow on the baseline during the corresponding period, without an obvious direction. As Figure 5 shown, it indicates that the blood flow in the diastolic phase is weak or even disappears, without an obvious direction.

[0189] Case 4: Blood flow reversal occurs when S1 peak, S2 peak, and D peak are not all in the same direction. As Figure 5 shown, the D peak is below the baseline. At this time, it is not in the same direction as S1 peak and S2 peak, and the blood flow direction in the diastolic phase is reversed.

[0190] For the abnormal rapid positioning of a time series of cerebral blood flow velocity signals monitored for a long time, and based on the heat map for visual coding design, as Figure 6As shown, different colors are used to represent different types of abnormalities and the overall abnormal situation, presenting abstract data in an intuitive color form, enabling users to quickly grasp the overall distribution characteristics and relative relationships of the data. For the result of the abnormal rapid positioning of a time series of cerebral blood flow velocity signals, it is presented in the form of a heat map, showing a regular grid layout, consisting of several rows and columns. Among them, the columns represent different cycle sequences, that is, each square represents a cycle segment of the cerebral blood flow velocity signal, arranged in sequence from left to right, reflecting the continuous cerebral blood flow velocity signal cycle sequence in the time dimension; the rows correspond to different types of abnormal situations, from top to bottom are the overall abnormality, peak time abnormality, peak value abnormality, S1 < S2 abnormality, S1, S2 fusion abnormality, and blood flow direction abnormality. Among them, the overall abnormality is reflected by the number of times abnormal spectral morphological feature points appear within a cycle, such as Figure 6 shown.

[0191] Color mapping rule: Specific colors are used to distinguish different abnormal types, forming a set of color mapping systems. Among them, orange is used to identify peak time abnormalities, pink corresponds to peak value abnormalities, green represents S1 < S2 abnormalities, cyan indicates S1, S2 fusion abnormalities, blue is used to reflect blood flow direction abnormalities, and red is specifically used to represent overall abnormalities. The use of these colors aims to enable observers to quickly visually distinguish different types of abnormal situations. For the overall abnormality (i.e., the first row), the depth change of the color is used to reflect the number of highlighted abnormal squares in the vertical column within each cycle, that is, the overall density of abnormalities in this cycle. The darker the color, the more various abnormal situations appear in this cycle, following the general principle of using colors in a heat map to reflect data density or numerical size. Here, the depth change of red intuitively shows the difference in the overall abnormality degree between different cycles, such as Figure 6 shown.

[0192] Effect display:

[0193] For the characteristic visualization coding design of the cerebral blood flow velocity signal of one cycle, as follows Figure 7As shown in the figure. For patients with normal cerebral blood flow velocity, the corresponding spectral morphology diagram shows clear S1 peak, S2 peak, and D peak, and S1 peak > S2 peak, without peak time delay; for patients with increased intracranial pressure, the S1 peak and S2 peak in the corresponding spectral morphology diagram are fused, and the systolic peak is sharp; for patients with severe stenosis of the cardiac blood vessels, the corresponding spectral morphology diagram shows S1 < S2 and delayed systolic peak; for patients in the middle stage of cerebral circulation arrest, the corresponding spectral morphology diagram is characterized by a sharp systolic blood flow and zero diastolic blood flow; for patients in the late stage of cerebral circulation arrest, the systolic blood flow signal on the corresponding spectral morphology diagram is weak or even absent. Based on the above characteristics, medical staff can diagnose the cerebral blood flow condition of patients according to the spectral morphology diagram characteristics of patients and quickly make a diagnosis for patients.

[0194] For time - coded visualization, as follows Figure 8 As shown in the figure. For each row corresponding to an abnormal type, when a corresponding abnormality appears in a certain cycle segment, the square at the position of the cycle column corresponding to this row will be highlighted in the corresponding color. For example, if the S1 < S2 abnormality appears in a certain cycle segment, then the square located in the fourth row (corresponding to the S1 < S2 abnormal row) and in the column corresponding to this cycle will be highlighted in green, and so on. Each abnormal type shows its occurrence in different cycles in the figure according to such rules. This way of combining square highlighting with specific colors clearly and intuitively shows the presence or absence of various abnormalities in different cycles, just like using different colors to mark areas with different data density levels in a heat map, facilitating observers to quickly capture the distribution rules and change trends of abnormal conditions in a long - time series.

[0195] Experimental verification

[0196] The present invention selects 100 patients with different degrees of cerebrovascular disease risks, covering different ages, genders, and disease types (such as transient ischemic attack, cerebral arteriosclerosis, chronic cerebral blood supply insufficiency, etc.). At the same time, 30 healthy volunteers are selected as normal control samples. Using a transcranial Doppler ultrasound blood flow analyzer, continuous half - hour monitoring of cerebral blood flow velocity signals is carried out on all participants. During the monitoring process, basic vital sign data of patients, such as heart rate, blood pressure, etc., and relevant clinical symptom information are synchronously recorded. The experiment is divided into two groups. One group is the visual coding group using the method of the present invention, and the other group is the naked - eye observation group using the traditional method.

[0197] For the visual coding group, according to the method of the present invention, the collected cerebral blood flow velocity signals are processed, the spectral morphological features of the complete cycle are extracted, the visual coding units are divided, color mapping coding is performed on various types of abnormalities and overall abnormalities, and a visualization result is generated, including a long-time series display based on a heat map, the spectral morphological features corresponding to different blood flow conditions collected, and the visual coding styles such as Figure 9 shown Figure 9 The characteristic visual coding design of the cerebral blood flow velocity signal of each cycle of a certain patient and the abnormal distribution of a time series of cerebral blood flow velocity signals monitored for a long time can be intuitively obtained.

[0198] Figure 9 It shows a reference diagram of the long-time visualization coding corresponding to a time series of cerebral blood flow velocity of a certain patient and the visualization effect of the characteristic coding of the cerebral blood flow velocity envelope diagram. Through the combination of the cerebral blood flow velocity signal waveform and the abnormal heat map, the abnormal distribution characteristics of the cerebral blood flow signal cycle can be intuitively obtained. For example, observing the orange squares in the "abnormal peak time" row of the heat map can locate the problem of abnormal peak time in multiple cycles; the red squares in the "overall abnormality" row clearly show the dark color of a certain cycle due to the superposition of multiple abnormalities through the shade change, reflecting the abnormal density of that cycle; the green squares in the "S1 < S2 abnormality" row directly indicate the abnormal feature that the S1 peak is less than the S2 peak in the corresponding cycle. Medical staff can quickly identify the abnormal types (abnormal peak time, peak value abnormality, S1 < S2 abnormality, etc.) of each signal cycle according to the distribution of the color squares in the heat map, and then combine the overall trend of the cerebral blood flow velocity waveform above to comprehensively analyze the abnormal rules of the cerebral blood flow signal, so as to efficiently judge the cerebral blood flow state of the patient and provide an intuitive visualization analysis basis for the diagnosis of cerebrovascular diseases.

[0199] For the naked-eye observation group, according to the traditional method, the original cerebral blood flow velocity data is sorted and presented to professional doctors in the form of conventional line charts and data tables for their naked-eye observation and analysis.

[0200] The final evaluation indexes are evaluated from three aspects: abnormal detection accuracy, detection time consumption, and user experience satisfaction. Specifically, the abnormal detection rate is the accuracy rate of the evaluation committee composed of professional cerebrovascular disease experts to judge abnormalities in the visual coding group and the naked-eye observation group; the detection time consumption is the average time required for doctors to judge whether there are abnormalities and the types of abnormalities from obtaining data when using the two methods; the user experience satisfaction is to use a questionnaire survey method to let the participating doctors score the ease of use of the two methods, the convenience of information acquisition, etc. (1-5 points, 5 points means very satisfied).

[0201] The final test results are shown in the following table:

[0202] Evaluation Metrics Visual Coding Group Naked eye observation group Anomaly detection accuracy 92％ 98％ Monitoring time (minutes) 3.5 7.2 User experience satisfaction (points) 4.2 2.8

[0203] It can be clearly seen from the above test results that in terms of abnormality detection accuracy, the visual coding group reached 92%, and the naked eye observation group was 98%, which shows that the visual coding method of the present invention can more accurately detect abnormalities in cerebral blood flow velocity signals, which is close to the expert level. In terms of detection time, the visual coding group only takes 3.5 minutes on average, which is much shorter than the 7.2 minutes of the naked eye observation group, greatly improving the detection efficiency. In terms of user experience satisfaction, the visual coding group scored 4.2 points, while the naked eye observation group only scored 2.8 points, indicating that the doctors participating in the evaluation gave higher evaluations to the ease of use and convenience of information acquisition of the visual coding method. Combining these three evaluation indicators, it is fully demonstrated that the method of the present invention has obvious superiority and feasibility compared with the traditional naked eye observation method, and can provide more effective support for the monitoring and diagnosis of cerebrovascular diseases.

[0204] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0205] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A visual analysis method for cerebral blood flow velocity signals acquired through long-term monitoring, characterized in that the steps Including: Step S1, signal acquisition, providing a data basis for subsequent analysis and processing; Step S2, preprocessing the signal to remove noise interference in the original cerebral blood flow velocity signal obtained in S1; Step S3, extracting the waveform features of the cerebral blood flow velocity signal; Step S4, visual encoding design of the features of the cerebral blood flow velocity signal for one cycle.

2. The method for visual analysis of cerebral blood flow velocity signals acquired through long-term monitoring according to claim 1, characterized in that: In S2, a fourth-order Butterworth filter is used to remove noise interference in the original cerebral blood flow velocity signal.

3. The method for visualizing and analyzing cerebral blood flow velocity signals acquired through long-term monitoring according to claim 1, characterized in that: In S3, the cerebral blood flow velocity signal preprocessed in step S2 is subjected to cycle division, signal quality assessment, and feature point recognition to obtain the cerebral blood flow velocity signal in which the spectral feature recognition points are recognized. The specific steps are as follows: S31, performing cycle division on the denoised cerebral blood flow signal; S32, performing quality assessment on the cerebral blood flow signal after cycle division; S33, feature point recognition, using the second-order derivative extreme point recognition algorithm to perform first-order and second-order derivative processing on the cerebral blood flow velocity signal within one cycle, and accurately recognizing "three peaks and one valley" by analyzing the derivative situation.

4. The method for visualizing and analyzing cerebral blood flow velocity signals acquired through long-term monitoring according to claim 3, characterized in that: The specific steps of the feature point recognition described in S33 include: S33a, take the absolute value v(t) of the cerebral blood flow velocity signal x(t) within one cycle, and the time range of x(t) is [s tart ,t end ], where t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within one cycle; S33b, calculating the first-order derivative of v(t) to characterize the change in the signal slope, and the expression is: where f′(t) represents the slope of the cerebral blood flow velocity signal v(t) at time t; Since the cerebral blood flow velocity signal is actually a discrete signal, the central difference method is used for discretization calculation, and the specific formula is as follows: Where Δt represents the sampling time interval (unit: s), N represents the total number of sampling points in the period, and t i represents the time of the i-th sampling point, t i+1 and t i-1 Respectively represent the time of the i+1th and i-1th sampling points, f′(t i ) represents the cerebral blood flow velocity signal v(t) at the time t of the i-th sampling point i The slope at S33c, calculating the second-order derivative of v(t) to characterize the change in the signal curvature, and the expression is: where f″(t) represents the curvature of the cerebral blood flow velocity signal v(t) at time t; Performing discretization calculation: Among them, Δt represents the sampling time interval, N represents the total number of sampling points in the period, and t i represents the time of the i-th sampling point, t i+1 and t i-1 Respectively represent the time of the i+1th and i-1th sampling points, f″(t i ) represents the cerebral blood flow velocity signal v(t) at the time t of the i-th sampling point i The curvature at S33d, determining the candidate points for the peak and valley according to the first-order and second-order derivatives of v(t); The candidate points for the peak need to meet the following conditions: f′(t p )=0 and f″(t p )<0 Among them, t p represents the time of the peak candidate point, f′(t p ) represents the cerebral blood flow signal at time t p The slope at , f″(t p ) represents the cerebral blood flow velocity signal at time t p The curvature at The candidate points for the valley need to meet the following conditions: f′(t v )=0 and f″(t v )>0 Among them, t v represents the moment of the valley candidate point, f′(t v ) represents the cerebral blood flow signal at time t v The slope at , f″(t v ) represents the cerebral blood flow velocity signal at time t v The curvature at S33e, screening the candidate points, and setting feature point screening rules from two major aspects of time constraint and amplitude constraint; S33f, resolving the conflicts of the feature points that conflict in S33e, and finally obtaining the feature points corresponding to the time, specifically as follows: For the conflict of the dicrotic notch, the time when it appears is: For the conflict of the diastolic peak, only the one with the largest amplitude and the time sequence after the dicrotic notch is retained as the D peak, and the time when the D peak appears is: For the conflict of the systolic phase, select the one with the largest amplitude as the S1 peak, and among the remaining candidate points, the one with the time sequence closest to S1 and the second largest amplitude is the S2 peak, and the time when the S1 peak appears is: The time when the S2 peak appears is screened from the remaining peak candidate points, and the time when it appears is: Among them, t N is the time corresponding to the diastolic dicrotic notch, t S1 is the time corresponding to the systolic peak S1, t S2 is the time corresponding to the secondary peak of the systolic period, t D is the time corresponding to the diastolic peak, T cycle Indicates the total duration of a single cycle, Δt tol Indicates the allowed time error threshold, t p represents the time of the peak candidate point, t v represents the moment of the valley candidate point, argmax{·} is to find the value of the input variable that makes the function value reach the maximum, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, v(t p ) represents the cerebral blood flow velocity value at the peak candidate point, v(t v ) represents the cerebral blood flow velocity value of the valley candidate point; S34, obtaining abnormal spectral morphology feature points based on the time when the feature points obtained in S33f appear, including abnormal peak arrival time, abnormal peak value, S1 < S2 abnormality, S1 and S2 fusion abnormality, and blood flow direction abnormality, specifically as follows: Abnormal peak arrival time: Subtract the time corresponding to the peak value point and the starting point of the S1 peak, S2 peak, and D peak. If the difference is greater than the set threshold, it is determined that the peak arrival time is abnormal, that is: ΔT S1 >ΔT th_S1 or ΔT S2 >ΔT th_S2 or ΔT D >ΔT th_D ΔT S1 =t S1 -t start ,ΔT S2 =T S2 -t start ,ΔT D =t D -t start Abnormal peak value: As long as one of the S1 peak, S2 peak, and D peak exceeds the set threshold, it is identified as an abnormal peak value, that is: v(t S1 )>V th_s1 or v(t S2 )>V th_S2 or v(t D )>V th_D S1 < S2 anomaly: By comparing the peak values of the S1 peak and the S2 peak, when the peak value of the S1 peak is less than that of the S2 peak, this anomaly occurs, that is: v(t S1 )<v(t S2 ) Abnormal fusion of S1 and S2: If the corresponding moments of the S1 peak and the S2 peak are relatively close or even equal, it is determined that S1 and S2 are fused, that is: |t S1 -t S2 |<Δt th_tol Abnormal blood flow direction: When the S1 peak, S2 peak, and D peak do not have the same direction, or the blood flow is extremely weak or even disappears, it is recognized as an abnormal blood flow direction. For the co-directional anomaly: sign(x(t S1 ))≠sign(x(t S2 )) or sign(x(t S1 ))≠sign(x(t D ))or sign(x(t S2 ))≠sign(x(t D )) For the anomaly of extremely weak or disappeared cerebral blood flow velocity signal: v(t S1 ) <V min or v(t s2 ) <V min or v(t D ) <V min Where, ΔT S1 Indicates the peak time of S1, ΔT S2 Indicates the peak time of S2, ΔT D Indicates the peak time of S1, ΔT th_S1 Indicates the peak time threshold set for the S1 peak, ΔT th_S2 Indicates the peak time threshold set for the S2 peak, ΔT th_D Indicates the peak time threshold set for D peak, t S1 is the time corresponding to the systolic peak S1, t S2 is the time corresponding to the secondary peak of the systolic period, t D is the time corresponding to the diastolic peak, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, v(t S1 ) represents the non-negative cerebral blood flow signal value of S1 peak, v(t S2 ) represents the non-negative cerebral blood flow signal value of S2, v(t D ) represents the non-negative cerebral blood flow signal value of D peak, V th_S1 Indicates the peak threshold set for the S1 peak, V th_S2 Indicates the peak threshold set for peak D, V th_D Indicates the peak threshold set for peak D, Δt th_tol is the time overlap threshold, sign(·) represents the sign function, x(t S1 ) represents the cerebral blood flow velocity signal value of S1 without absolute value processing, x(t S2 ) represents the cerebral blood flow velocity signal value of S2 without absolute value processing, x(t D ) represents the cerebral blood flow velocity signal value without absolute value processing, V min The lower limit threshold of the signal amplitude is set.

5. The method for visual analysis of cerebral blood flow velocity signals acquired through long-term monitoring according to claim 4, characterized in that: The screening rules described in S33e are specifically as follows: For the time constraint, the moments when the characteristic points appear need to strictly satisfy the time sequence: t S1 <t S2 <t N <t D The timing of the dicrotic notch appears near the 2 / 3 of the cycle duration, that is: T cycle =t start -t end For the amplitude constraint, the amplitudes of the characteristic points need to strictly satisfy: v(t S1 )>v(t S2 )>v(t D )>v(t N ) The amplitude of the S1 peak is the maximum within the cycle, the amplitude of the S2 peak is the second, and the amplitude of the D peak is the maximum after the dicrotic notch, that is: v(t S1 )=max{v(t)|t∈[t start ,t end ]} v(t S2 )=max{v(t)|t∈[t S1 ,t end ]} v(t D )=max{v(t)|t∈[t N ,t end ]} Among them, t S1 is the time corresponding to the systolic peak S1, t S2 is the time corresponding to the secondary peak of the systolic period, t N is the time corresponding to the diastolic dicrotic notch, t D is the time corresponding to the diastolic peak, v(t S1 ) represents the cerebral blood flow velocity value of the systolic peak S1, v(t S2 ) represents the cerebral blood flow velocity value of the secondary peak S2 of the systolic period, v(t D ) represents the cerebral blood flow velocity value of the diastolic peak D, v(t N ) represents the cerebral blood flow velocity value corresponding to the dicrotic notch, v(t) represents the cerebral blood flow velocity signal after taking the absolute value, max{·} represents the maximum value, t represents the time, t start Indicates the starting time of the cerebral blood flow velocity signal within one cycle, t end Indicates the end time of the cerebral blood flow velocity signal within a cycle, T cycle Indicates the total duration of a single cycle, Δt tol Indicates the allowed time error threshold.

6. The method for visualizing and analyzing cerebral blood flow velocity signals acquired through long-term monitoring according to claim 1, characterized in that: The S4 is based on the visual coding design for the abnormal spectral morphology characteristic points extracted based on S34, specifically including: S41, Visual coding design for the characteristic visualization of the cerebral blood flow velocity signal in one cycle, using different visual symbols to represent different abnormal types, including: Normal peak: It is represented as a single point; Peak time delay: It is represented as two wavy lines "~" followed by the corresponding peak point; High peak value: It is represented as an upward arrow; Low peak value: It is represented as a downward arrow; S1 < S2: It is represented as a slanting line with the left end lower and the right end higher, and the two ends of the slanting line correspond to the two peak points of the S1 peak and the S2 peak; Fusion of S1 and S2 peaks: It is represented as a thick dot; Abnormal blood flow direction: Different visual symbols are used to represent normal blood flow direction, extremely weak or disappeared blood flow, and reverse blood flow; S42, For the rapid localization of anomalies in a time series of cerebral blood flow velocity signals monitored for a long time, a heat map is used for visual coding design, specifically including: A specific color mapping system is used to distinguish different abnormal types, and a specific color is used to represent the overall anomaly; The density of anomalies appearing in each cycle is reflected by the depth change of the color. The darker the color, the more abnormal situations there are.

7. The method for visual analysis of cerebral blood flow velocity signals acquired through long-term monitoring according to claim 6, characterized in that: The overall anomaly is reflected by the number of occurrences of abnormal spectral morphology characteristic points in one cycle, and the difference in the overall anomaly degree between different cycles is intuitively shown through the depth change of the color.

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