Method for evaluating the condition of moyamoya disease based on wavelet analysis of low-frequency oscillation of brain oxygen signal

Abstract

The application discloses a method for evaluating the condition of a patient with moyamoya disease based on wavelet analysis of low-frequency oscillation of a cerebral oxygen signal, and relates to the field of biomedical engineering and medical diagnosis technology, and comprises the following steps: a time anchor band is established around the low-frequency oscillation of the cerebral oxygen signal, a scale phase list is generated at the tail of the time anchor band, and the scale phase list is used to record the phase change sequence of each wavelet scale, thereby providing a unified time anchor reference for subsequent analysis. Through the synergistic mechanism of the time anchor band, the scale phase list, the inverse phase traction sequence and the rhythm traction order, flexible regulation and slow release of the energy of the low-frequency oscillation of the cerebral oxygen signal are realized, short-time energy aggregation is avoided, and the evaluation rhythm is kept stable. By setting a breathing type flow gate and a phase dislocation dome, the energy collection and release balance is coordinated, the condition misjudgment is prevented, and the continuity and reliability of the evaluation are improved.

Description

Technical Field

[0001] This invention relates to the fields of biomedical engineering and medical diagnostics, specifically to a method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals. Background Technology

[0002] Moyamoya disease assessment based on low-frequency oscillation wavelet analysis of brain oxygenation signals involves acquiring brain oxygenation signals from changes in oxyhemoglobin and deoxyhemoglobin over time using methods such as near-infrared brain function monitoring. The focus is on low-frequency oscillation components reflecting the self-regulation capacity of cerebral blood flow. Wavelet analysis combined with pattern recognition is used to decompose and characterize the energy distribution, stability, and phase changes of these low-frequency oscillations at different time scales, thereby depicting the temporal regulation of cerebral blood flow oxygenation. The obtained low-frequency oscillation features are then mapped using pattern recognition to the cerebral perfusion abnormality feature space caused by vascular stenosis and compensatory collateral formation in Moyamoya disease patients. This allows for a comprehensive assessment of the severity of the disease, the state of blood flow compensation, and the level of potential risk, providing quantitative evidence for clinical judgment.

[0003] The existing technology has the following shortcomings: In existing technologies, wavelet analysis-based methods for assessing brain oxygenation signals typically establish stable rhythmic mappings of low-frequency oscillations across different wavelet scales during continuous system operation, using this mapping as the basis for determining changes in the condition. However, in practical applications, when the cerebral blood flow regulation process enters an extremely unstable state within a short period, low-frequency oscillations in the brain oxygenation signal may simultaneously experience phase collapse across multiple wavelet scales, causing wavelet coefficients that were originally dispersed across different time scales to abnormally cluster along the time axis. Because existing technologies fail to effectively distinguish between physiological transient instability and actual disease transitions, this instantaneous energy concentration can easily be misjudged as a significant deterioration of the condition within a short period, leading to a large shift in assessment results within a very short time window. This, in turn, distorts the assessment of the disease level, affecting the stability of the system and the reliability of the assessment results.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for assessing the condition of moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the condition of moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals, comprising the following steps: A time anchoring band is established around the low-frequency oscillation of brain oxygen signal. A scale phase list is generated at the end of the time anchoring band. The scale phase list is used to record the phase change sequence of each wavelet scale, providing a unified time anchoring benchmark for subsequent analysis. Multi-scale synchronous collapse traces are extracted based on the scale phase list in the time anchor band. The detected synchronous collapse traces are collected to form an anomalous energy fingerprint frame, and the anomalous energy fingerprint frame is written as a continuous input to the end of the time anchor band. The peak-valley sequence within the abnormal energy fingerprint frame is rearranged to generate an inverse phase traction sequence, and a time boundary is marked at the end of the inverse phase traction sequence to determine the temporal range of subsequent energy reconstruction. The side suppression of the wavelet time window is initiated based on the time boundary of the reverse phase traction sequence, and a time slot misalignment grid is established in conjunction with the central release structure to output rhythmic traction commands to control the time diffusion of energy distribution. Based on the rhythmic traction command and combined with the reverse phase traction sequence, the breathing flow gate of the phase-displaced dome is activated to implement micro-amplitude periodic expansion and contraction adjustment of the analysis channel, suppressing short-term energy accumulation and maintaining the continuity of the disease assessment rhythm.

[0007] Preferably, the steps for generating the scale phase list are as follows: The acquired brain oxygen signal is processed to make it time-continuous, so that oxyhemoglobin and deoxyhemoglobin form a uniform fluctuation background in the time dimension, and the time-dominant rhythm of low-frequency oscillation is extracted according to the overall energy distribution trend to establish time nodes. A continuously extending time anchoring zone is constructed with time nodes as the center, and the energy distribution ratio between adjacent time nodes is calculated to determine the energy gradient change trend of the time anchoring zone; A scale phase list is generated at the end of the time anchor band, and the phase change sequence of each wavelet scale is recorded with the end of the time anchor band as a unified reference starting point. By mapping the scale phase list to the time anchoring bands one by one, the low-frequency oscillation state is uniformly anchored in both time and phase dimensions, providing a stable and continuous time reference for subsequent analysis.

[0008] Preferably, the steps for extracting multi-scale synchronous collapse traces and forming anomalous energy fingerprint frames based on the scale phase list in the time anchoring band are as follows: Each time segment within the time anchor band is scanned segment by segment to identify the time overlap intervals of phase changes at each wavelet scale, and the rhythm nodes of the time anchor band are used as alignment references to capture phase convergence segments. Within the time region of phase convergence, the direction of phase change is tracked along the time axis, and the continuous convergence regions are connected to form a complete synchronous collapse trace. The obtained synchronous collapse traces are collected according to the correspondence between time and scale to construct anomaly energy fingerprint frames that are interwoven in two dimensions of time and scale. By writing the abnormal energy fingerprint frame as a continuous input to the end of the time anchor band, the energy characteristics are continuously updated and the timing is connected as the time anchor band is extended.

[0009] Preferably, the abnormal energy fingerprint frame is embedded in chronological order when written to the end of the time anchor band. The start time of the abnormal energy fingerprint frame is continuously connected with the end time of the time anchor band, so that the energy information and the time rhythm are seamlessly spliced ​​together, thereby realizing the continuous accumulation of energy distribution and the traceable recording of disease characteristics during the extension of the time anchor band.

[0010] Preferably, the steps for rearranging the peak-valley sequences within the abnormal energy fingerprint frame and generating an inverse phase traction sequence are as follows: The energy distribution within the abnormal energy fingerprint frame is structurally identified, and the peak and valley positions are determined along the time direction and arranged in chronological order to form a peak-valley sequence. Using the rhythm nodes of the time anchoring zone as a reference, adjacent peak and valley regions are exchanged sequentially along the time direction to redistribute energy and form a time distribution sequence with inverse phase characteristics. The rearranged peak-valley sequence is integrated to generate a reverse-phase traction sequence, and adjacent peak-valley pairs are connected as traction units to form a continuous energy flow. Marking time boundaries at the tail of the reverse-phase traction sequence determines the temporal range of energy reconstruction, ensuring that the endpoint of energy diffusion aligns with the rhythmic structure of the time anchoring band.

[0011] Preferably, when marking the time boundary at the end of the reverse-phase traction sequence, the termination time of the last traction unit in the reverse-phase traction sequence is used as the time boundary point, and the boundary position is marked synchronously in the time anchoring zone, so that the energy flow changes from the traction stage to the equilibrium stage at the time boundary, thereby forming a transition zone for energy redistribution to maintain the continuity of energy transfer.

[0012] Preferably, the steps for initiating side suppression of the wavelet time window and establishing a time slot misalignment grating based on the time boundary of the tail of the inverse phase traction sequence are as follows: Based on the time boundary of the tail of the inverse phase traction sequence, the side suppression of the wavelet time window is initiated, and a gradually shrinking energy constraint structure is formed in the tail region to limit excessive energy diffusion. After completing the lateral suppression, the energy pullback direction inside the reverse phase traction sequence is used to establish the central release structure, so that the energy of the time window diffuses symmetrically from both sides to the center. Using the rhythm nodes of the time anchoring zone as a reference, and combining the time boundary of the reverse phase traction sequence, a time slot misalignment grid is constructed so that energy release is staggered along the time axis; Based on the time distribution structure of the time slot misalignment grid, a rhythmic traction command is output to ensure that the energy diffusion rhythm is consistent with the rhythmic changes of the time anchoring band.

[0013] Preferably, when the rhythmic traction command is output, the periodic interval of the time slot misalignment grid is used as the time reference, and the energy release sequence is synchronized with the rhythmic nodes of the time anchoring band, so that the energy diffuses from the center to the outside in the central release structure, forming a continuous rhythmic energy traction field, so as to maintain the balance of low-frequency oscillation energy distribution and the stability of the evaluation rhythm.

[0014] Preferably, the steps for activating the breathing flow gate of the phase-displaced dome based on the rhythmic traction command and in combination with the reverse traction sequence are as follows: Using the rhythmic traction command as the time control signal, the time distribution and phase change law of the reverse phase traction sequence are synchronously connected to form an enveloping phase misalignment dome to establish an energy buffer boundary. Within the envelope of the phase-displacement dome, a breathing flow gate is activated, and the analysis channel is adjusted slightly by rhythmic traction as the driving signal to form a flexible energy cycle. The reverse energy distribution relationship between adjacent traction units in the reverse phase traction sequence is used to dynamically coordinate the opening and closing rhythm of the flow gate to keep the energy release synchronized with the rhythm changes; The energy regulation effect of the flow gate is fed back to the end of the time anchoring zone through the phase misalignment dome to form a continuous and stable time energy flow.

[0015] Preferably, during the opening and closing adjustment of the breathing flow gate, the arc-shaped coverage surface of the phase-displaced dome serves as the energy transfer boundary. The flow gate is controlled to contract and open sequentially within a time period by rhythmic traction commands, so that energy is slowly released and redistributed within the dome. This creates a periodic, balanced energy flow state within the time anchoring zone to maintain the rhythmic continuity of the disease assessment.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention introduces a continuous synergistic mechanism of time anchoring bands, scale phase lists, reverse phase traction sequences, and rhythmic traction commands during the low-frequency oscillation analysis of brain oxygen signals. This enables flexible regulation and slow release of energy across multiple scales and time periods, effectively avoiding energy concentration phenomena in brain oxygen signals within a short period. By periodically guiding energy flow and suppressing phase misalignment, the disease assessment process maintains a stable rhythm over time, ensuring that the results are not disturbed by instantaneous fluctuations. This guarantees the continuity and stability of the assessment output and improves the sensitivity and reliability of the assessment to real physiological changes.

[0017] This invention achieves adaptive regulation of energy release and containment rhythm by incorporating a breathing-type flow gate and a phase-shifting dome at the energy flow end, ensuring a coordinated balance of low-frequency oscillations in cerebral oxygen signals across both time and phase dimensions during assessment. Through this energy buffering and redistribution mechanism, the system maintains stable response characteristics when dealing with complex cerebral blood flow regulation states, effectively preventing misjudgments of sudden changes in condition, and guaranteeing the temporal consistency and physiological accuracy of assessment results, thereby providing a more precise quantitative basis for assessing the severity of the condition. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals, as described in this invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals, as shown, includes the following steps: A time anchoring band is established around the low-frequency oscillation of brain oxygen signal. A scale phase list is generated at the end of the time anchoring band. The scale phase list is used to record the phase change sequence of each wavelet scale, providing a unified time anchoring benchmark for subsequent analysis. A time anchoring band is established around the low-frequency oscillations of the brain oxygen signal. A scale-phase list is generated at the end of the time anchoring band. The scale-phase list is used to record the phase change sequence at each wavelet scale, providing a unified time anchoring benchmark for subsequent analysis. The specific implementation steps are as follows: The acquired brain oxygen signals undergo time continuity processing to ensure that the low-frequency oscillations maintain a complete and continuous fluctuation pattern over time. By smoothing the original time series of the brain oxygen signals, a uniform fluctuation background of oxyhemoglobin and deoxyhemoglobin is formed over time. During this process, the dominant temporal rhythm of the low-frequency oscillations is extracted based on the overall energy distribution trend of the brain oxygen signals, and this rhythm is used as a time anchoring reference. This reference is marked as a series of continuous time nodes along the entire time axis, each time node corresponding to a phase inflection point of the brain oxygen signal within the low-frequency range. In this way, a temporal positioning framework for the low-frequency oscillations can be gradually established along the time axis, allowing subsequent steps to record and analyze phase sequences based on this time framework, thereby ensuring the consistency and coherence of brain oxygen signals across different time periods in terms of temporal reference.

[0022] After obtaining the time node distribution of low-frequency oscillations, a local temporal bandwidth is constructed with each time node as the center, forming a continuously extending temporal anchoring band. Structurally, this time anchoring band consists of multiple interconnected time segments, each corresponding to a complete oscillation cycle of the low-frequency oscillations of the brain oxygen signal. Within each time segment, the relative energy distribution ratio between adjacent time nodes is further calculated, thereby determining the energy gradient change trend of the time anchoring band at different locations. In this way, the time anchoring band not only provides the anchoring interval of the low-frequency oscillations of the brain oxygen signal in the time dimension but also reflects the dynamic change characteristics of the low-frequency components over time. During the construction of the time anchoring band, the low-frequency rhythm nodes extracted in the previous step are used as the start and end boundaries of the time segments, ensuring that the formation of the time anchoring band is consistent with the rhythmic changes of the brain oxygen signal, thus providing an ordered temporal structure support for the generation of the scale phase list.

[0023] A scale-phase list is generated at the tail end of the time anchor band. This scale-phase list is a multi-scale phase recording sequence established around the end of the time anchor band, used to carry phase change information at each wavelet scale. Specifically, at the tail end of the time anchor band, based on the rhythmic characteristics and temporal distribution of low-frequency oscillations, phase changes at different scales are arranged sequentially in time order, so that the phase evolution at each scale can form a continuous record on the time axis. To ensure the comparability of phase relationships between different scales, the end of the time anchor band is used as a unified phase reference starting point during list generation, ensuring that phase sequences at different scales have a consistent reference benchmark in time alignment. Through this time synchronization method, the scale-phase list can accurately reflect the multi-scale phase dynamic changes of brain oxygen signals in the time dimension, thus providing a unified temporal framework for subsequent low-frequency oscillation stability analysis. At the same time, the energy gradient distribution of the time anchor band determined in the previous stage is mapped into the scale-phase list, so that the list not only records the phase sequences at each scale, but also reflects the energy transfer trend within each time period, thereby achieving the fusion of time anchor information and scale-phase information.

[0024] A scale-phase list is used to record the phase change sequences at each wavelet scale, serving as the time anchoring reference for subsequent analysis. In this process, the scale-phase list generated in the previous step is mapped one-to-one with the time anchoring bands, ensuring that the low-frequency oscillation state of each time period can be fully characterized at the phase level. By binding the phase change sequences in the scale-phase list with the rhythm nodes of the time anchoring bands, a two-layer anchoring structure constrained by both time and phase is formed. This structure maintains the continuity of the low-frequency oscillation rhythm along the time axis while achieving coordination and unity between different scales in the phase dimension. When the low-frequency oscillations of the brain oxygen signal shift in time, the time anchoring band provides a rhythm alignment reference, while the scale-phase list provides a synchronous reference for phase changes, thus ensuring the consistency of low-frequency oscillation analysis in both time and phase dimensions. Through this two-layer anchoring structure, subsequent analysis processes can rely on this time anchoring reference when processing low-frequency oscillation characteristics, ensuring that the phase changes at each scale have a unified time reference, making the analysis results more stable, continuous, and comparable.

[0025] Multi-scale synchronous collapse traces are extracted based on the scale phase list in the time anchor band. The detected synchronous collapse traces are collected to form an anomalous energy fingerprint frame, and the anomalous energy fingerprint frame is written as a continuous input to the end of the time anchor band. Multi-scale synchronous collapse traces are extracted based on the scale phase list in the time anchor band. The detected synchronous collapse traces are collected to form anomaly energy fingerprint frames, and the anomaly energy fingerprint frames are written as continuous inputs to the end of the time anchor band. The specific implementation steps are as follows: With the time anchoring band established and the scale phase list generated, each time segment within the time anchoring band is scanned segment by segment to identify the temporal overlap intervals of phase changes at each wavelet scale. By temporally aligning the phase change sequences recorded in the scale phase list, the phase change curves at different scales are made comparable on the time axis. In this process, the rhythm nodes of the time anchoring band are used as alignment references to ensure that phase changes at each scale are referenced to the same time anchor point. When multiple scale phase sequences exhibit phase convergence within the same time segment, it indicates that the brain oxygen signal may be showing a synchronous convergence trend of low-frequency oscillations at that moment. This synchronous convergence phenomenon is a precursor to dynamic concentrated changes in the brain oxygen signal at multiple scales, therefore it needs to be continuously recorded. Through this segment-by-segment scanning and time alignment method, the time regions where phase changes converge between different scales can be accurately captured within the time anchoring band, providing a foundation for the subsequent extraction of synchronous collapse traces.

[0026] After identifying time regions with phase convergence within the time anchoring band, multi-scale phase changes within these regions are continuously tracked to extract synchronous collapse traces. Synchronous collapse traces refer to continuous trajectories where multiple wavelet scales simultaneously converge and form phase collapse within the same time period. In this step, the phase change direction is tracked along the time axis, centered on the time overlap intervals identified in the previous stage, connecting regions with continuous phase convergence into a complete time trajectory. Each synchronous collapse trace represents the consistent change process of brain oxygen signals across multiple scales, reflecting the temporal concentration trend of low-frequency oscillation energy. To ensure the integrity of the traces, the starting point of each synchronous collapse trace corresponds to a rhythmic node of the time anchoring band, while the ending point corresponds to the boundary point of an adjacent time segment, ensuring that the formation process of the trace is consistent with the rhythmic changes of the time anchoring band. In this way, the evolution process of multi-scale phase synchronous collapse in brain oxygen signals can be completely recorded in the time dimension, providing clear temporal evidence for the identification of abnormal energy accumulation.

[0027] After obtaining multiple synchronous collapse traces, these traces are aggregated according to their distribution on the time axis and their correspondence at the scale level to form an anomalous energy fingerprint frame. The anomalous energy fingerprint frame is an energy concentration mapping formed by the interweaving of several synchronous collapse traces in both time and scale dimensions, used to express the instantaneous energy accumulation characteristics of brain oxygen signals during low-frequency oscillations. In constructing the anomalous energy fingerprint frame, the start and end points of each synchronous collapse trace obtained in the previous step are matched with the corresponding segments of the time anchoring band, and arranged on the time axis according to the order of trace appearance, so that the formation process of energy accumulation can be presented in a continuous manner. Simultaneously, the intensity of phase collapse at different scales is mapped to a time-continuous energy stacking trajectory, giving the anomalous energy fingerprint frame a continuous energy distribution structure in the time axis direction, while reflecting the hierarchical relationship of multi-level energy accumulation in the scale direction. Through this dual mapping method of time and scale, the anomalous energy fingerprint frame can comprehensively characterize the energy concentration state of brain oxygen signals in a short period of time and maintain a strict temporal correspondence with the time anchoring band, providing a traceable energy characteristic basis for subsequent disease assessment.

[0028] The generated abnormal energy fingerprint frames are continuously written as input to the end of the time anchor band to extend the time-series structure of low-frequency oscillations and continuously update energy characteristics. In this step, the tail of the time anchor band is used as the energy input end, and the abnormal energy fingerprint frames are embedded sequentially in chronological order, making them part of the time anchor band. In this way, the time anchor band not only preserves the temporal rhythm information of the original low-frequency oscillations but also contains the corresponding abnormal energy distribution structure, thus forming a composite time band with both time and energy dimensions. During the writing process, the start time of the abnormal energy fingerprint frame is seamlessly connected to the end time of the previous stage of the time anchor band, enabling continuous splicing of energy information and temporal rhythm. When new brain oxygen signal data arrives, the time anchor band will automatically extend, and the abnormal energy fingerprint frames, as historical energy features, are retained at the beginning of the extended band for continuous comparison and trend analysis of subsequent disease states. In this way, as the time anchor band extends, it gradually accumulates abnormal energy distribution information of brain oxygen signals in different time periods, providing a continuous, traceable, and structurally complete temporal reference base for subsequent assessment.

[0029] The peak-valley sequence within the abnormal energy fingerprint frame is rearranged to generate an inverse phase traction sequence, and a time boundary is marked at the end of the inverse phase traction sequence to determine the temporal range of subsequent energy reconstruction. The peak-valley sequences within the anomalous energy fingerprint frame are rearranged to generate an inverse-phase pulling sequence. Time boundaries are marked at the end of the inverse-phase pulling sequence to determine the temporal range for subsequent energy reconstruction. The specific implementation steps are as follows: Based on the generated anomalous energy fingerprint frames, the energy distribution within the fingerprint frames is structurally identified to determine the spatial order of the peak-valley sequence. The anomalous energy fingerprint frame is a two-dimensional energy structure of time and scale formed by the superposition of multiple synchronous collapse traces, containing multiple energy concentration points and energy sparse bands. By continuously scanning the anomalous energy fingerprint frame along the time direction, the peak and trough positions of energy can be identified on the time axis, and these positions are arranged in chronological order to form a preliminary peak-valley sequence. In this process, the time anchoring band provides a basic temporal rhythm reference, enabling the identification of the peak-valley sequence to be consistent with the low-frequency oscillation rhythm. Each energy peak represents a phase contraction interval of low-frequency oscillations in the brain oxygen signal, while each energy trough represents an energy release interval. Therefore, the formation of the peak-valley sequence essentially characterizes the concentration and diffusion pattern of energy in the time dimension, providing a clear temporal coordinate system for subsequent sequence rearrangement.

[0030] After obtaining the initial peak-valley sequence, the sequence is rearranged temporally to generate a reverse-phase traction structure. The purpose of this rearrangement is to reorganize the temporal relationship between peaks and valleys, transforming the energy distribution along the time axis from a concentrated state to a traction state with opposite phases. Specifically, using the rhythmic nodes of the time anchor zone as a reference, adjacent peak and valley regions are sequentially exchanged along the time direction, resulting in an alternating arrangement of high and low energy regions along the time axis. This rearrangement redistributes energy that was originally concentrated in a short period into adjacent time intervals, thus establishing a reverse energy traction relationship in the time dimension. During this process, each peak-valley rearrangement is constrained by the adjacent boundaries of the time anchor zone, ensuring that the rearranged temporal sequence maintains overall rhythmic consistency while achieving local energy distribution balance. Through this time anchor zone-constrained rearrangement, the temporal structure of energy changes from unidirectional concentration to bidirectional traction, forming a temporal distribution sequence with reverse-phase characteristics.

[0031] After the sequence rearrangement is completed, the rearranged peak-valley sequences are integrated to form an inverse-phase traction sequence. The inverse-phase traction sequence is an energy distribution structure that achieves reversed peak-valley phase pairing in the time dimension. Essentially, it achieves temporal energy diffusion balance through the interchange of peak and valley positions. In this step, based on the rearranged peak-valley alternation sequence, adjacent peak-valley pairs are considered as traction units, each reflecting a dynamic energy transfer from high to low. Multiple traction units are sequentially connected along the time axis to form a complete inverse-phase traction sequence, causing the energy flow throughout the time period to exhibit a periodic stretching and retraction. This inverse-phase traction sequence not only retains the energy intensity characteristics of the original anomalous energy fingerprint frame but also establishes a dynamic channel for energy release and redistribution through sequential reverse connections. Simultaneously, the rhythm nodes of the time anchoring band from the previous step continue to serve as time reference points, ensuring that the temporal extension of the inverse-phase traction sequence remains synchronized with the original low-frequency rhythm. In this way, the anomalous energy and low-frequency rhythm are rebalanced in the time dimension, allowing subsequent energy reconstruction to proceed on the basis of phase coordination.

[0032] A time boundary is marked at the end of the reverse-phase traction sequence to determine the temporal range of subsequent energy reconstruction. The time boundary is set to clarify the termination position and extension range of the reverse-phase traction sequence on the time axis, thus providing a clear time limit for the energy reconstruction process. Specifically, the termination time of the last traction unit in the reverse-phase traction sequence is used as the time boundary point, and this boundary position is simultaneously marked in the time anchoring band, ensuring that the endpoint of energy diffusion is consistent with the rhythmic structure of the time anchoring band. At this time boundary, the direction of energy flow shifts from the traction phase to the equilibrium phase, forming a transition point for energy redistribution. By marking the time boundary, the time range of the reverse-phase traction sequence can be limited to a controllable interval, thereby avoiding cross-interval energy superposition during subsequent energy reconstruction. At this point, the reverse-phase traction sequence, as the energy temporal structure after time rearrangement and phase reversal, not only defines the start and end range of reconstruction but also provides a reference framework for subsequent energy adjustment. In this way, the energy redistribution process on the time axis can be extended based on the reverse-phase traction sequence, ensuring the continuity of the reconstruction timing and the coordination of energy adjustment.

[0033] The side suppression of the wavelet time window is initiated based on the time boundary of the reverse phase traction sequence, and a time slot misalignment grid is established in conjunction with the central release structure to output rhythmic traction commands to control the time diffusion of energy distribution. The wavelet time window is laterally suppressed based on the time boundary at the tail of the inverse traction sequence. A time slot misalignment grid is established in conjunction with the central release structure, and a rhythmic traction command is output to control the temporal spread of energy distribution. The specific steps are as follows: With the inverse-phase traction sequence already generated and its tail time boundary defined, a lateral suppression structure for the wavelet time window is initiated based on this time boundary. By identifying the tail time interval of the inverse-phase traction sequence, it is used as the starting point for wavelet time window regulation, causing local suppression within this time period. Lateral suppression limits excessive energy diffusion on both sides of the wavelet time window, thus preventing energy spikes or anomalous superposition near the time boundary. In implementation, two symmetrical lateral suppression regions are established centered on the tail of the inverse-phase traction sequence, corresponding temporally to the start and end points of the energy pullback phase. In this way, the time window forms a gradually contracting energy constraint in the tail region, effectively controlling the release rate of the high-energy segment in the inverse-phase traction sequence. The introduction of the lateral suppression structure enables the time window to have adaptive energy limiting capabilities during its extension, allowing low-frequency oscillations to maintain a smooth rhythmic transition within this time range, providing a foundation for the subsequent construction of the central release structure.

[0034] After completing the lateral suppression of the wavelet time window, a central release structure is established near the time boundary using the energy retreat direction within the inverse-phase traction sequence. The central release structure serves to create a slow-release energy channel at the center of the time window, allowing the energy restricted by lateral suppression to gradually diffuse in the central region, thus preventing secondary energy accumulation within the window. In this process, with the time boundary as the center of symmetry, the time interval of the last traction unit in the inverse-phase traction sequence is used as the initial segment for central release, and forward and backward slow-release energy pathways are established along the time direction. Energy is released gradually in this central region, balancing with the lateral suppression of the previous stage, resulting in a symmetrical diffusion of energy distribution from both sides towards the center throughout the time window. In this way, the central release structure achieves flexible energy transfer in the time dimension, preventing the high energy at the tail of the inverse-phase traction sequence from accumulating at the boundary, instead achieving a balanced distribution at the center of the time window. At this point, the energy distribution of the wavelet time window transforms from a concentrated form to a diffused form of central release, forming a stable energy flow channel and laying the foundation for energy flow in the establishment of the time slot misalignment grid.

[0035] After establishing the lateral suppression and central release structure of the wavelet time window, a time-slot misalignment grid is constructed using the rhythm nodes of the time anchoring band as the time reference and in conjunction with the time boundaries of the inverse-phase traction sequence. The time-slot misalignment grid is an energy distribution adjustment structure with periodic intervals on the time axis. Its function is to prevent energy diffusion in different time periods from overlapping and causing instantaneous concentration by using minute time misalignments. When constructing the time-slot misalignment grid, the rhythm nodes of the time anchoring band are used as the starting reference point, and the time ranges of adjacent traction units in the inverse-phase traction sequence are staggered by a certain time interval, so that the energy release of each time unit presents an interleaved distribution on the time axis. Simultaneously, the energy release channel provided by the central release structure is extended within the time-slot misalignment grid, creating a flexible connection between energy transfers in different time slots. In this way, the energy release process within the time window is no longer concentrated at a single rhythm node, but unfolds sequentially between multiple adjacent time slots, forming a periodically diffused energy distribution pattern. The establishment of the time-slot misalignment grid enables energy to achieve orderly misalignment and balanced diffusion in the time dimension, thereby effectively reducing energy overlap in a short period of time and improving the stability of low-frequency oscillations of brain oxygen signals in terms of temporal continuity.

[0036] After the time-slot misalignment grid is established, a rhythmic traction command is output based on its temporal distribution structure to control the diffusion rhythm of energy in the temporal dimension. The rhythmic traction command is an extension instruction of the rhythmic nodes of the time-anchored band, used to coordinate the energy release sequence of different misaligned time slots within the time window. When outputting the rhythmic traction command, the energy release sequence is kept consistent with the rhythmic changes of the time-anchored band, based on the periodic interval of the time-slot misalignment grid, ensuring that the energy diffusion process within each rhythmic cycle is uniformly regulated by temporal traction. In this way, the rhythmic traction command makes the energy release within the time window exhibit a periodic rhythm on a macroscopic level, while maintaining relative independence between time slots on a microscopic level, thus achieving hierarchical control of temporal diffusion. After the rhythmic traction command is output, the energy flow of the wavelet time window will form an energy traction field that gradually diffuses outward from the center on the time axis, based on the time-slot misalignment grid and terminated by the time boundary of the inverse traction sequence. This energy traction field exhibits continuous rhythmic propagation characteristics in the temporal dimension, ensuring that the energy distribution of brain oxygen signals in the low-frequency range remains balanced, preventing assessment bias caused by short-term aggregation.

[0037] Based on the rhythmic traction command and combined with the reverse phase traction sequence, the breathing flow gate with phase-displaced dome is activated to implement micro-amplitude periodic expansion and contraction adjustment of the analysis channel, suppressing short-term energy accumulation and maintaining the continuity of the disease assessment rhythm. Based on the rhythmic traction command and combined with the reverse phase traction sequence, the breathing flow gate with phase-shifted dome is activated to implement micro-amplitude periodic expansion and contraction adjustments to the analysis channel, in order to suppress short-term energy accumulation and maintain the continuity of the patient assessment rhythm. The specific steps are as follows: Based on the output of rhythmic traction commands and the construction of time-slot misalignment grids, the rhythmic traction commands serve as time control signals to synchronize the temporal distribution and phase change patterns of the reverse-phase traction sequence, initiating the formation process of the phase misalignment dome. The phase misalignment dome is an enveloping time control structure that provides an inclusive boundary for energy flow in the time dimension, allowing energy to obtain a flexible buffer space based on its misaligned distribution. In specific implementation, the temporal distribution of the time-slot misalignment grid serves as the underlying framework, and the phase change trend of the reverse-phase traction sequence is superimposed on this time framework, forming a series of arched phase misalignment coverage surfaces. These coverage surfaces are interconnected on the time axis, collectively forming a complete phase misalignment dome. Each arched structure represents a process of energy diffusion and phase retraction, making energy transfer within time no longer linear but achieving a flexible transition through the arc-shaped path of the misalignment dome. In this way, energy forms an inclusive cyclical flow in the extension of the time anchoring zone, avoiding concentrated superposition at time nodes and providing a spatial constraint basis for the subsequent activation of the flow gate.

[0038] After the phase-displacement dome is formed, a rhythmic traction command is used as the driving signal to activate the periodic operation of the breathing flow gate, which then performs micro-amplitude expansion and contraction adjustments to the analysis channel within the envelope of the dome structure. The core function of the breathing flow gate is to control the rhythm of energy flow within the time channel, allowing energy to be transferred alternately in the form of "absorption-release" at different time periods. In specific implementation, the arc-shaped coverage surface of the phase-displacement dome serves as the energy transfer boundary, confining the expansion and contraction of the flow gate within the dome. After each rhythmic traction command is triggered, the flow gate first performs a slight contraction, causing the energy flow to briefly stagnate within the time window, thereby weakening the instantaneous accumulation caused by the rhythmic misalignment. Subsequently, the flow gate slowly opens at a rhythm synchronized with the rhythmic traction command, gradually releasing the stagnant energy and forming a smooth energy transition. Through this periodic expansion and contraction adjustment, energy forms a cyclical distribution state on the time axis similar to a breathing rhythm, ensuring that low-frequency oscillations maintain continuous flow during energy transfer without concentrated abrupt changes. The introduction of this structure creates a dynamic adjustment mechanism between temporal continuity and energy balance in the analysis channel.

[0039] During the periodic opening and closing of the respiratory flow gate, the reverse energy distribution relationship between adjacent traction units in the reverse-phase traction sequence is utilized to dynamically coordinate the opening and closing rhythm of the flow gate, ensuring synchronization and coordination between energy release and rhythm changes. Specifically, when the preceding traction unit in the reverse-phase traction sequence is in the energy release phase, the flow gate contracts, buffering the released energy within the dome; when the following traction unit enters the energy absorption phase, the flow gate opens, redistributing the buffered energy along the time axis to the next rhythmic cycle. In this way, the phase misalignment characteristics of the reverse-phase traction sequence and the respiratory movements of the flow gate complement each other, jointly forming a self-balancing mechanism of energy in the time dimension. Throughout the process, the rhythmic traction command continuously outputs adjustment signals to control the frequency and amplitude of the flow gate's movements, ensuring synchronization between energy release and phase traction. In this way, energy forms a periodic flow during the extension of the time anchoring zone, maintaining phase coordination in the low-frequency oscillations of the brain oxygen signal and flexible diffusion in energy distribution, providing stable support for assessing the continuity of the rhythm.

[0040] After the breathing flow gate completes its periodic expansion and contraction cycle, the energy regulation effect generated by the flow gate is fed back to the end of the time anchoring zone through the containment structure of the phase-displacement dome, achieving continuous time control throughout the process. Specifically, an energy release zone is set in the outer edge of the dome to receive the residual energy released by the breathing flow gate, forming a smooth energy transition layer at the end of the time anchoring zone. This energy transition layer extends the range of the rhythmic traction command in the time dimension, allowing the time anchoring zone to smoothly connect to the next stage of data input. Through this feedback mechanism, the breathing flow gate not only regulates the current energy transfer state but also affects the rhythmic characteristics of subsequent time windows, thereby achieving continuous stability of the evaluation rhythm. During this process, the arched covering layer of the phase-displacement dome continuously expands and contracts dynamically to adapt to the periodic changes in energy flow, keeping the entire energy channel in a balanced state of alternating release and absorption cycles. In this way, the phenomenon of short-term energy accumulation is effectively weakened, and the low-frequency oscillation of brain oxygen signals maintains the continuity of flow over time, so that the disease assessment process can continue to run in a rhythmic state without being disturbed by short-term energy fluctuations.

[0041] This invention introduces a continuous synergistic mechanism of time anchoring bands, scale phase lists, reverse phase traction sequences, and rhythmic traction commands during the low-frequency oscillation analysis of brain oxygen signals. This enables flexible regulation and slow release of energy across multiple scales and time periods, effectively avoiding energy concentration phenomena in brain oxygen signals within a short period. By periodically guiding energy flow and suppressing phase misalignment, the disease assessment process maintains a stable rhythm over time, ensuring that the results are not disturbed by instantaneous fluctuations. This guarantees the continuity and stability of the assessment output and improves the sensitivity and reliability of the assessment to real physiological changes.

[0042] This invention achieves adaptive regulation of energy release and containment rhythm by incorporating a breathing-type flow gate and a phase-shifting dome at the energy flow end, ensuring a coordinated balance of low-frequency oscillations in cerebral oxygen signals across both time and phase dimensions during assessment. Through this energy buffering and redistribution mechanism, the system maintains stable response characteristics when dealing with complex cerebral blood flow regulation states, effectively preventing misjudgments of sudden changes in condition, and guaranteeing the temporal consistency and physiological accuracy of assessment results, thereby providing a more precise quantitative basis for assessing the severity of the condition.

[0043] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the condition of moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygenation signals, characterized in that, Includes the following steps: A time anchoring band is established around the low-frequency oscillation of brain oxygen signal. A scale phase list is generated at the end of the time anchoring band. The scale phase list is used to record the phase change sequence of each wavelet scale, providing a unified time anchoring benchmark for subsequent analysis. Multi-scale synchronous collapse traces are extracted based on the scale phase list in the time anchor band. The detected synchronous collapse traces are collected to form an anomalous energy fingerprint frame, and the anomalous energy fingerprint frame is written as a continuous input to the end of the time anchor band. The peak-valley sequence within the abnormal energy fingerprint frame is rearranged to generate an inverse phase traction sequence, and a time boundary is marked at the end of the inverse phase traction sequence to determine the temporal range of subsequent energy reconstruction. The side suppression of the wavelet time window is initiated based on the time boundary of the reverse phase traction sequence, and a time slot misalignment grid is established in conjunction with the central release structure to output rhythmic traction commands to control the time diffusion of energy distribution. Based on the rhythmic traction command and combined with the reverse phase traction sequence, the breathing flow gate with phase-displaced dome is activated to implement micro-amplitude periodic expansion and contraction adjustment of the analysis channel, suppressing short-term energy accumulation and maintaining the continuity of the disease assessment rhythm.

2. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 1, characterized in that, The steps for generating the scale phase list are as follows: The acquired brain oxygen signal is processed to make the oxyhemoglobin and deoxyhemoglobin form a uniform fluctuation background in the time dimension, and the time-dominant rhythm of low-frequency oscillation is extracted based on the overall energy distribution trend to establish time nodes. A continuously extending time anchoring zone is constructed with time nodes as the center, and the energy distribution ratio between adjacent time nodes is calculated to determine the energy gradient change trend of the time anchoring zone; A scale phase list is generated at the end of the time anchor band, and the phase change sequence of each wavelet scale is recorded with the end of the time anchor band as a unified reference starting point. By mapping the scale phase list to the time anchoring bands one by one, the low-frequency oscillation state is uniformly anchored in both time and phase dimensions, providing a stable and continuous time reference for subsequent analysis.

3. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 2, characterized in that, The steps for extracting multi-scale synchronous collapse traces and forming anomalous energy fingerprint frames based on the scale phase list in the time anchor band are as follows: Each time segment within the time anchor band is scanned segment by segment to identify the time overlap intervals of phase changes at each wavelet scale, and the rhythm nodes of the time anchor band are used as alignment references to capture phase convergence segments. Within the time region of phase convergence, the direction of phase change is tracked along the time axis, and the continuous convergence regions are connected to form a complete synchronous collapse trace. The obtained synchronous collapse traces are collected according to the correspondence between time and scale to construct anomaly energy fingerprint frames that are interwoven in two dimensions of time and scale. By writing the abnormal energy fingerprint frame as a continuous input to the end of the time anchor band, the energy characteristics are continuously updated and the timing is connected as the time anchor band is extended.

4. The method for assessing the condition of moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 3, characterized in that, Abnormal energy fingerprint frames are embedded in chronological order when written to the end of the time anchor band. The start time of the abnormal energy fingerprint frame is continuously connected with the end time of the time anchor band, so that the energy information and time rhythm are seamlessly spliced ​​together, thereby realizing the continuous accumulation of energy distribution and the traceable recording of disease characteristics during the extension of the time anchor band.

5. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 3, characterized in that, The steps for rearranging the peak-valley sequences within the anomalous energy fingerprint frame and generating an inverse phase traction sequence are as follows: The energy distribution within the abnormal energy fingerprint frame is structurally identified, and the peak and valley positions are determined along the time direction and arranged in chronological order to form a peak-valley sequence. Using the rhythm nodes of the time anchoring zone as a reference, adjacent peak and valley regions are exchanged sequentially along the time direction to redistribute energy and form a time distribution sequence with inverse phase characteristics. The rearranged peak-valley sequence is integrated to generate a reverse-phase traction sequence, and adjacent peak-valley pairs are connected as traction units to form a continuous energy flow. Marking time boundaries at the tail of the reverse-phase traction sequence determines the temporal range of energy reconstruction, ensuring that the endpoint of energy diffusion aligns with the rhythmic structure of the time anchoring band.

6. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 5, characterized in that, When marking the time boundary at the end of the reverse-phase traction sequence, the termination time of the last traction unit in the reverse-phase traction sequence is used as the time boundary point, and the boundary position is marked synchronously in the time anchoring zone. This allows the energy flow to transition from the traction stage to the equilibrium stage at the time boundary, thereby forming a transition zone for energy redistribution to maintain the continuity of energy transfer.

7. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 5, characterized in that, The steps for initiating side suppression of the wavelet time window and establishing a time slot misalignment grating based on the time boundary of the tail of the inverse phase traction sequence are as follows: Based on the time boundary of the tail of the inverse phase traction sequence, the side suppression of the wavelet time window is initiated, and a gradually shrinking energy constraint structure is formed in the tail region to limit excessive energy diffusion. After completing the lateral suppression, the energy pullback direction inside the reverse phase traction sequence is used to establish the central release structure, so that the energy of the time window diffuses symmetrically from both sides to the center. Using the rhythm nodes of the time anchoring zone as a reference, and combining the time boundary of the reverse phase traction sequence, a time slot misalignment grid is constructed so that energy release is staggered along the time axis; Based on the time distribution structure of the time slot misalignment grid, a rhythmic traction command is output to ensure that the energy diffusion rhythm is consistent with the rhythmic changes of the time anchoring band.

8. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 7, characterized in that, When the rhythmic traction command outputs, the periodic interval of the time slot misalignment grid is used as the time reference. The energy release sequence is synchronized with the rhythmic nodes of the time anchoring band, so that the energy diffuses from the center outward in the central release structure, forming a continuous rhythmic energy traction field to maintain the balance of low-frequency oscillation energy distribution and the stability of the evaluation rhythm.

9. The method for assessing the condition of moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 7, characterized in that, The steps for activating the breathing flow gate of the phase-displaced dome based on the rhythmic traction command and in combination with the reverse phase traction sequence are as follows: Using the rhythmic traction command as the time control signal, the time distribution and phase change law of the reverse phase traction sequence are synchronously connected to form an enveloping phase misalignment dome to establish an energy buffer boundary. Within the envelope of the phase-displacement dome, a breathing flow gate is activated, and the analysis channel is adjusted slightly by rhythmic traction as the driving signal to form a flexible energy cycle. The reverse energy distribution relationship between adjacent traction units in the reverse phase traction sequence is used to dynamically coordinate the opening and closing rhythm of the flow gate to keep the energy release synchronized with the rhythm changes; The energy regulation effect of the flow gate is fed back to the end of the time anchoring zone through the phase misalignment dome to form a continuous and stable time energy flow.

10. The method for assessing the condition of Moyamoya disease based on low-frequency oscillation wavelet analysis of brain oxygen signals according to claim 1, characterized in that, During the opening and closing adjustment of the breathing flow gate, the arc-shaped coverage surface of the phase-displaced dome serves as the energy transfer boundary. The flow gate is controlled to contract and open sequentially within a time period by rhythmic traction, so that energy is slowly released and redistributed inside the dome. This creates a periodic balanced energy flow state within the time anchoring zone to maintain the rhythmic continuity of the disease assessment.

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