A Dynamic Synchronous Tracking and Detection Method and System Based on Bio-Wave Resonance

Abstract

This application relates to the field of bio-wave resonance technology, providing a dynamic synchronous tracking and detection method and system based on bio-wave resonance to address the problem of low dynamic synchronous detection accuracy of existing heart-brain coupled bio-wave signals. The method includes: acquiring the characteristics of a mixed oscillating waveform formed by the coupling of electrocardiogram (ECG) and brainwave (EEG) signals; identifying the ECG oscillation component and the EEG rhythm fluctuation component from the mixed oscillating waveform characteristics; determining the resonance correlation interval based on the ECG oscillation component and the EEG rhythm fluctuation component; performing interference cancellation processing on the composite oscillation waveform within the resonance correlation interval in the mixed oscillating waveform characteristics; and generating a tracking and detection atlas characterizing heart-brain coupled resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series. This application improves the dynamic synchronous detection accuracy of heart-brain coupled bio-wave signals.

Description

Technical Field

[0001] This application relates to the field of bio-wave resonance technology, and in particular to a dynamic synchronous tracking and detection method and system based on bio-wave resonance. Background Technology

[0002] In the field of health monitoring, real-time tracking of the dynamic coupling characteristics of electrocardiogram (ECG) and electroencephalogram (EEG) is of great significance for the early diagnosis of neurological diseases. Due to the overlapping frequency bands and strong noise interference between cardiac and brain signals, a technical solution is needed that can accurately separate the resonant components of the two physiological signals and achieve dynamic synchronous analysis.

[0003] Currently, a coupling analysis method based on fixed frequency band decomposition is adopted. First, the ECG and EEG signals are separated by a preset frequency band filter. Then, the linear coherence coefficient of the two signals in a fixed frequency band is calculated. Finally, the heart-brain coupling state is determined based on the coherence threshold.

[0004] This method relies on a preset frequency band division, which makes it difficult to adapt to frequency band shifts caused by individual differences; linear coherence analysis only reflects the correlation of signal amplitude and ignores nonlinear coupling characteristics such as phase synchronization; the fixed threshold strategy causes weak resonant signals to be submerged by noise, affecting the sensitivity of dynamic tracking. Summary of the Invention

[0005] This application provides a dynamic synchronous tracking and detection method and system based on bio-wave resonance to solve the problem of low dynamic synchronous detection accuracy of brain-coupled bio-wave signals in the prior art.

[0006] In a first aspect, this application provides a dynamic synchronous tracking and detection method based on bio-wave resonance, comprising:

[0007] To acquire the characteristics of a hybrid oscillatory waveform formed by the coupling of electrocardiogram (ECG) and electroencephalogram (EEG) signals;

[0008] The electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component are identified from the hybrid oscillation waveform characteristics;

[0009] The resonance correlation interval is determined based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component;

[0010] Interference cancellation processing is performed on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform characteristics;

[0011] Based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series, a tracking and detection map characterizing the heart-brain coupling resonance is generated.

[0012] Optionally, the step of generating a tracking and detection atlas characterizing heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series includes:

[0013] Construct a time-dimensional distribution framework of the eliminated composite oscillation waveform within N consecutive time windows, where N is greater than or equal to 3;

[0014] Based on the aforementioned time-dimensional distribution framework, the characteristic evolution path of the eliminated composite oscillation waveform is determined;

[0015] Based on the feature evolution path, a tracking and detection map is generated.

[0016] Optionally, the feature evolution path includes a first trajectory of amplitude change trend within N consecutive time windows and a second trajectory of frequency drift trend within N consecutive time windows.

[0017] Based on the aforementioned feature evolution path, a tracking and detection map is generated, including:

[0018] The first motion trajectory and the second motion trajectory are combined and encoded to form time-coded data;

[0019] Based on the time-coded data, a collaborative association rule is constructed between the turning point in the first motion trajectory and the critical offset point in the second motion trajectory;

[0020] Based on the collaborative association rules, the turning point and the critical offset point are dynamically parameterized and fused to generate a tracking and detection map.

[0021] Optionally, the step of dynamically parameterizing and fusing the inflection point and the critical offset point according to the collaborative association rule to generate a tracking and detection map includes:

[0022] Based on the collaborative association rules, a mapping relationship is established between the turning point and the critical offset point;

[0023] Within each time window, based on the mapping relationship, the amplitude change rate corresponding to the inflection point and the frequency offset corresponding to the critical offset point are parameterized and combined to generate a dynamic coupling parameter vector for each time window.

[0024] Based on the joint distribution density of the dynamic coupling parameter vectors of the N time windows, the dynamic coupling parameter vectors of adjacent time windows are connected in chronological order to form a dynamic trajectory marker for cardiac-cerebral resonance.

[0025] Based on the spatial distribution of the cardiac-brain resonance dynamic trajectory markers in the time-frequency domain, a tracking and detection map is generated.

[0026] Optionally, identifying the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the mixed oscillation waveform features includes:

[0027] The hybrid oscillation waveform features are divided into multiple candidate components according to a preset frequency band distribution;

[0028] Calculate the first correlation coefficient between each candidate component and the preset ECG wave feature template, and the second correlation coefficient between each candidate component and the EEG rhythm feature template;

[0029] If the first correlation coefficient is greater than the second correlation coefficient, the corresponding candidate component is determined as an electrocardiogram (ECG) candidate component; if the second correlation coefficient is greater than the first correlation coefficient, the corresponding candidate component is determined as an electroencephalogram (EEG) candidate component.

[0030] Candidate ECG components that meet the preset ECG frequency constraints are identified as ECG oscillation components, and candidate EEG components that meet the preset EEG rhythm constraints are identified as EEG rhythm fluctuation components.

[0031] Optionally, determining the resonance correlation interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component includes:

[0032] Calculate the synchronization index of the ECG oscillation component and the EEG rhythm fluctuation component in the time-frequency domain. The synchronization index includes: phase lock value and energy coupling coefficient.

[0033] The time-frequency region where the synchronicity index exceeds the threshold is marked as a candidate resonance interval;

[0034] Candidate resonance intervals that are time-continuous and have overlapping frequency bands are merged into resonance correlation intervals.

[0035] Optionally, the interference cancellation processing of the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform features includes:

[0036] The composite oscillation waveform is decomposed into a resonance component and a disturbance component;

[0037] By using the dynamic suppression mechanism corresponding to the resonance correlation interval, the resonance component is attenuated and the interference component is eliminated to obtain the processed resonance component.

[0038] The processed resonant components are reconstructed to obtain the waveform reconstruction result, which is the composite oscillation waveform after elimination.

[0039] Secondly, this application provides a dynamic synchronous tracking and detection system based on bio-wave resonance, comprising:

[0040] The acquisition module is used to acquire the characteristics of the mixed oscillation waveform formed by the coupling of electrocardiogram (ECG) signals and electroencephalogram (EEG) signals;

[0041] The identification module is used to identify the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the hybrid oscillation waveform features;

[0042] The determination module is used to determine the resonance correlation interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component;

[0043] An interference elimination module is used to perform interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the features of the hybrid oscillation waveform.

[0044] The generation module is used to generate a tracking and detection map characterizing the heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series.

[0045] Thirdly, this application provides a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a dynamic synchronous tracking and detection method based on bio-wave resonance as described in any of the first aspects.

[0046] Fourthly, this application provides a computer storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the dynamic synchronous tracking and detection method based on bio-wave resonance as described in any one of the first aspects.

[0047] This application provides a dynamic synchronous tracking and detection method based on bio-wave resonance. The method includes: acquiring the characteristics of a mixed oscillating waveform formed by the coupling of electrocardiogram (ECG) signals and electroencephalogram (EEG) signals; identifying the ECG oscillation component and the EEG rhythm fluctuation component from the mixed oscillating waveform characteristics; determining the resonance correlation interval based on the ECG oscillation component and the EEG rhythm fluctuation component; performing interference cancellation processing on the composite oscillating waveform within the resonance correlation interval in the mixed oscillating waveform characteristics; and generating a tracking and detection atlas characterizing the heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillating waveform in a continuous time series.

[0048] The technical solution provided in this application has the following beneficial effects:

[0049] This application achieves the capture of time-frequency domain coupling features of electrocardiogram (ECG) and electroencephalogram (EEG) signals, providing high-fidelity raw data for subsequent analysis. It accurately separates the core components of ECG oscillations and EEG rhythms, avoiding feature confusion caused by frequency band overlap. It locks onto sensitive frequency bands of energy interaction between heart and brain signals, narrowing the scope of targeted analysis. It enhances the signal-to-noise ratio of homologous resonance signals, retaining effective components reflecting physiological coupling. It visualizes the dynamic evolution of heart-brain coupling, supporting the identification of pathological features.

[0050] Furthermore, this application also constructs a distribution framework of the composite oscillation waveform after interference elimination within a continuous time window (N≥3), extracts its cross-window characteristic evolution path, and finally generates a high-resolution tracking and detection map reflecting the dynamic process of heart-brain coupling.

[0051] Furthermore, it enables the visualization of the dynamic trajectory of cardiac and cerebral resonance signals during long-term monitoring, and improves the detection rate of weak coupling features through multi-window joint analysis, providing continuous quantitative evidence in the time dimension for early disease warning.

[0052] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart illustrating a dynamic synchronous tracking and detection method based on bio-wave resonance, provided for an embodiment of this application;

[0055] Figure 2 A schematic diagram of the structure of a dynamic synchronous tracking and detection system based on bio-wave resonance provided in an embodiment of this application;

[0056] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0058] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0059] Existing cardio-brain coupling analysis methods based on fixed-band decomposition have limitations: their preset band divisions are difficult to adapt to dynamic frequency shifts caused by individual physiological differences, resulting in missed detection of key resonance features; linear coherence analysis only captures amplitude correlation while ignoring nonlinear coupling modes such as phase synchronization, leading to distortion in the analysis of weak interaction signals; fixed threshold strategies cannot distinguish between effective resonance and random interference in strong noise environments, severely limiting the sensitivity and reliability of dynamic tracking. These shortcomings essentially stem from the static and one-sided modeling of the cardio-brain signal coupling mechanism, which cannot meet the needs of precise clinical monitoring.

[0060] To address the aforementioned issues, this application proposes a dynamic synchronous tracking and detection method based on bio-wave resonance. By extracting the characteristics of the hybrid oscillation waveform formed by the coupling of ECG and EEG, it dynamically identifies the resonance correlation interval between the two and implements targeted interference cancellation within this interval. Its innovation lies in: firstly, adaptively determining the resonance frequency band based on the time-frequency domain overlap characteristics, breaking through the limitations of fixed frequency bands; secondly, by decomposing the homologous resonance components in the composite oscillation waveform, synchronously analyzing amplitude changes and phase drift characteristics, achieving complete modeling of nonlinear coupling; and finally, generating a tracking spectrum based on the dynamic evolution law in the continuous time series, avoiding the rigidity of threshold determination. This method fundamentally solves the three core problems of poor frequency band adaptability, single coupling mode analysis, and insufficient noise suppression in existing technologies, providing a high-precision and robust dynamic analysis tool for research on heart-brain functional interaction.

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Figure 1 A flowchart of a dynamic synchronous tracking and detection method based on bio-wave resonance provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0063] Step 101: Obtain the characteristics of the hybrid oscillation waveform formed by the coupling of electrocardiogram (ECG) and electroencephalogram (EEG) signals.

[0064] In step 101, the electrocardiogram (ECG) signal represents the periodic electrical signal generated by the electrical activity of the heart, mainly including characteristic waveforms such as the P wave, QRS complex, and T wave, reflecting the heart's pulsating state. The electroencephalogram (EEG) signal represents the rhythmic electrical signal generated by the electrical activity of brain neurons, including different frequency bands (such as alpha waves, beta waves, and gamma waves), reflecting the brain's functional state. The mixed oscillating waveform characteristics represent the composite signal formed by the superposition of ECG and EEG in the time-frequency domain, including their coupling characteristics, such as frequency band overlap and phase synchronization.

[0065] In this embodiment, electrocardiogram (ECG) and electroencephalogram (EEG) signals are simultaneously recorded using a multi-channel bioelectric signal acquisition device. Time-frequency analysis is performed on the original signals to extract the spectral distribution characteristics of the two signals within the same time window. The energy interaction relationship between ECG and EEG in each frequency band is calculated to form hybrid oscillation waveform feature data containing amplitude, phase, and frequency band coupling characteristics.

[0066] For example, when monitoring the heart-brain coupling state of patients with anxiety disorders, their electrocardiogram (electrodes attached to the chest) and electroencephalogram (electrodes attached to the scalp) signals were collected simultaneously. Wavelet transform analysis was used to analyze the spectral distribution of the two signals within a 5-second time window. It was found that the R wave of the electrocardiogram and the gamma wave (above 30Hz) of the electroencephalogram had overlapping energy, forming a mixed oscillating waveform feature, which provided a data basis for subsequent analysis.

[0067] Step 102: Identify the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the mixed oscillation waveform characteristics.

[0068] In step 102, the ECG oscillation component represents the dominant ECG component separated from the mixed signal, such as the low-frequency oscillation characteristic corresponding to the R-wave peak. The EEG rhythm fluctuation component represents the dominant EEG component extracted from the mixed signal, such as the rhythmic changes of alpha waves (8-13Hz) or gamma waves (>30Hz).

[0069] In this embodiment, a blind source separation algorithm is used to decompose the characteristics of the mixed oscillation waveform. Based on the typical frequency band distribution characteristics of ECG and EEG, components that conform to low-frequency oscillations of ECG (0.5-40Hz) and rhythmic fluctuations of EEG (such as alpha, beta, and gamma waves) are selected, noise interference is removed, and pure ECG oscillation components and EEG rhythmic components are obtained.

[0070] For example, in the mixed oscillatory waveforms of patients with anxiety disorders, independent component analysis (ICA) was used to separate the ECG R-wave component (low-frequency band) and the EEG gamma-wave component (high-frequency band). It was found that when patients were stressed, the ECG RR interval shortened, while the EEG gamma-wave energy increased, indicating enhanced cardio-brain coupling.

[0071] Step 103: Determine the resonance correlation interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component.

[0072] In step 103, the resonance correlation interval represents the interval in which electrocardiogram waves and brain waves interact with each other at a specific frequency and time, reflecting the physiological coupling state between the two.

[0073] In the embodiments of this application, the coherence of the electrocardiogram oscillation component and the brain electrical rhythm component is calculated, and the frequency bands in which the energy of the two changes synchronously in the time and frequency domain are found. A dynamic threshold is set to screen out the resonance intervals that meet the physiological coupling characteristics, such as the high coherence frequency bands of the electrocardiogram R wave and the brain electrical gamma wave within a specific time window.

[0074] For example, in data on patients with anxiety disorders, the coherence between ECG R waves and EEG Gamma waves was calculated, and it was found that when patients were in a state of tension, the two resonated in the 30-40 Hz frequency band, which was marked as the key resonance correlation interval.

[0075] Step 104: Perform interference cancellation processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform characteristics.

[0076] In step 104, the hybrid oscillation waveform feature refers to the time-frequency domain coupling features extracted from the original acquired ECG and EEG signals, while the composite oscillation waveform specifically refers to the signal to be processed within the identified "resonance correlation interval," containing the coupled components of ECG and EEG and noise interference. The two have an inclusion-containing relationship—the "hybrid oscillation waveform feature" represents the overall coupling characteristics of the original signal, while the "composite oscillation waveform" is the signal entity to be processed after filtering for a specific resonance frequency band. Interference cancellation processing means filtering out non-physiological noise and retaining the true heart-brain coupling signal.

[0077] In this embodiment, an adaptive filtering algorithm is used to construct a noise reference model based on the ECG and EEG signal characteristics within the resonance correlation interval, dynamically adjust the filtering parameters, suppress irrelevant interference, and enhance the homologous resonance components.

[0078] For example, within the resonance range (30-40Hz) of anxiety patients, an adaptive filter is used to remove electrooculogram artifacts and electromyography interference, preserving pure electrocardiogram-electroencephalogram coupled signals and improving the accuracy of subsequent analysis.

[0079] Step 105: Based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in the continuous time series, generate a tracking and detection map characterizing the heart-brain coupling resonance.

[0080] In step 105, the dynamic evolution features represent the trend of the heart-brain coupling signal changing over time, such as energy fluctuations and phase synchronization changes. The tracking and detection map represents a visual representation of the spatiotemporal distribution of the heart-brain coupling state.

[0081] In this embodiment, time-frequency analysis is performed on the signal after interference elimination to extract features such as energy and phase synchronization within a continuous time window. A dynamic evolution spectrum is generated using three-dimensional visualization technology, with the horizontal axis representing time, the vertical axis representing frequency, and the color depth representing coupling strength.

[0082] For example, in a 30-minute monitoring data of patients with anxiety disorder, the intensity of the heart-brain coupling was calculated every 5 seconds to generate a dynamic graph. It was found that the coupling intensity increased during periods of tension (such as before a speech) and decreased during periods of relaxation (such as during rest), which directly reflects changes in psychological state.

[0083] This method dynamically captures the coupling characteristics of ECG and EEG signals, accurately identifies resonance intervals, effectively filters out interference, and generates intuitive tracking and detection maps, achieving highly sensitive and reliable monitoring of the interaction between heart and brain functions, and providing an objective assessment method for emotional disorders, nervous system diseases, etc.

[0084] To address the insufficient temporal continuity analysis in dynamic monitoring of cardio-brain coupling, in some embodiments, step 105: generating a tracking and detection spectrum characterizing cardio-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series includes:

[0085] Step 201: Construct a time dimension distribution framework of the eliminated composite oscillation waveform within N consecutive time windows, where N is greater than or equal to 3.

[0086] In step 201, the time dimension distribution framework refers to dividing the composite oscillation waveform after interference elimination into multiple continuous time windows with a fixed duration. Each window contains complete waveform period information, and the framework structure consists of three dimensions: time axis, frequency axis, and energy intensity.

[0087] In this embodiment, the time window length is first set to ensure coverage of at least three complete ECG cycles. The composite oscillation waveform within each window is then subjected to time-frequency transformation to calculate the energy distribution at each frequency point. The data are then arranged in chronological order to form a three-dimensional data matrix, where rows correspond to time points, columns correspond to frequency points, and values ​​represent energy amplitudes.

[0088] Step 202: Based on the time dimension distribution framework, determine the characteristic evolution path of the eliminated composite oscillation waveform.

[0089] In step 202, the characteristic evolution path refers to the energy change trajectory of the composite oscillation waveform within a continuous time window, which consists of key turning points and trend segments, reflecting the dynamic change law of the heart-brain coupling strength.

[0090] In this embodiment, the energy peak point of the dominant frequency band of each window is extracted from the time dimension distribution framework as the key node, the energy change slope between adjacent window nodes is calculated, the nodes that satisfy continuous monotonicity are connected to form the main trend path, and the energy mutation point is marked as the auxiliary feature point.

[0091] Step 203: Generate a tracking and detection map based on the feature evolution path.

[0092] In this embodiment, the key nodes of the main trend path are projected onto a three-dimensional coordinate system in chronological order, energy intensity is represented by gradient colors, continuous nodes are connected by solid lines, and auxiliary feature points are marked by dashed lines, ultimately generating a three-dimensional trajectory map that can be interactively rotated and observed.

[0093] Here is a specific example:

[0094] In monitoring the cardio-brain coupling state of patients with anxiety disorders, electrocardiogram (ECG) and electroencephalogram (EEG) signals were first acquired simultaneously, with electrodes placed on the chest and scalp, respectively. Wavelet transform analysis was used to analyze the spectral distribution of both signals within a 5-second time window. Significant energy overlap was observed between the ECG R-wave and EEG gamma wave in the frequency band above 30 Hz, forming a mixed oscillating waveform. Independent component analysis was then used to separate the ECG R-wave and EEG gamma wave components from the mixed signal. When the patient was in a state of tension, the ECG RR interval was shortened to 0.6 seconds, while the EEG gamma wave energy increased to 2.5 times the baseline value. Calculation of the coherence coefficient revealed a coherence value of 0.85 in the 30-40 Hz frequency band, higher than other frequency bands; therefore, this interval was designated as the key resonance correlation interval. Within this interval, after using an adaptive filter to remove electrooculography (EOG) artifacts and electromyography (EMG) interference, a time-dimensional distribution framework was constructed from 30 minutes of monitoring data at 5-second intervals, resulting in 360 continuous time windows. Within each window, the product of the mean γ-wave energy and the R-wave amplitude was calculated as a coupling strength index, which rose to 450 μV before the presentation. 2 During the rest period, the voltage dropped to 120 μV. 2 Ultimately, these data points are connected to form a feature evolution path and mapped into a three-dimensional dynamic map. The horizontal axis represents time, the vertical axis displays the 30-40Hz frequency band, and the color depth reflects the coupling strength. The map clearly shows the complete trajectory of the coupling strength, which rises in a pulse-like manner during periods of tension and slowly declines during periods of relaxation, providing a visual basis for assessing anxiety levels.

[0095] In the embodiments of this application, the method establishes a time-continuous analysis framework to capture the dynamic evolution of heart-brain coupling signals and generate a visual atlas that intuitively reflects the changing trends of pathological features, providing an objective basis for monitoring the course of neurological diseases and evaluating their efficacy.

[0096] To address the issue of insufficient fusion of multi-dimensional features in dynamic monitoring of heart-brain coupling, in some embodiments, step 203 states that the feature evolution path includes a first motion trajectory of amplitude change trend within N consecutive time windows and a second motion trajectory of frequency drift trend within N consecutive time windows.

[0097] Based on the aforementioned feature evolution path, a tracking and detection map is generated, including:

[0098] Step 301: Combine and encode the first motion trajectory and the second motion trajectory to form time-coded data.

[0099] In step 301, time-coded data refers to a data structure that uniformly formats the amplitude change information of the first motion trajectory and the frequency change information of the second motion trajectory.

[0100] In this embodiment of the application, the two trajectories are aligned with the same time base, the amplitude and frequency values ​​at each time point are normalized, and then the two types of data are integrated into a joint feature vector sequence with time stamps using a specific encoding rule.

[0101] Step 302: Based on the time-coded data, construct a collaborative association rule between the turning point in the first motion trajectory and the critical offset point in the second motion trajectory.

[0102] In step 302, the inflection point refers to the extreme point and inflection point in the amplitude change trend curve. It is determined by calculating the rate of change of the peak amplitude of the composite oscillation waveform within each time window. When the absolute value of the rate of change exceeds a preset threshold, it is identified as an inflection point, reflecting the moment of abrupt change in the cardio-brain coupling strength. The critical offset point refers to the characteristic point in the frequency drift trend curve where the direction of frequency change abruptly changes. It is determined by detecting the offset of the dominant frequency within a continuous time window. When the offset exceeds the normal physiological fluctuation range, it is identified as a critical offset point, marking the moment of transition in the cardio-brain rhythm coupling state. The cooperative association rule refers to the correspondence and changing pattern between the inflection point of the first motion trajectory and the critical offset point of the second motion trajectory in the time dimension.

[0103] In this embodiment, the extreme points of amplitude change in the first motion trajectory are first identified as turning points, and the inflection points of frequency change in the second motion trajectory are located as critical offset points. Then, the matching degree of the two types of feature points on the time axis is calculated, and a time-aligned association mapping relationship is established.

[0104] Step 303: Based on the collaborative association rules, dynamically parameterize and fuse the turning point and the critical offset point to generate a tracking and detection map.

[0105] In step 303, dynamic parameterized fusion refers to the process of combining the features of the matched inflection points and critical offset points according to the collaborative association rules.

[0106] In this embodiment, for each matched feature point pair, its amplitude change rate and frequency offset are extracted as basic parameters. A comprehensive index characterizing the heart-brain coupling strength is generated by weighted fusion. Finally, the comprehensive indexes of all time points are connected to form a complete dynamic trajectory map.

[0107] Here is an example:

[0108] When monitoring the heart-brain coupling state of patients with anxiety disorders, the data from 360 established 5-second time windows were first analyzed. The average gamma wave energy in the 30-40Hz frequency band within each window was extracted as the first motion trajectory data, and the R-wave amplitude was extracted as the second motion trajectory data. The difference in gamma wave energy between adjacent windows was calculated; when the absolute value of the difference exceeded 100μV... 2 The timing was determined as an amplitude inflection point, reflecting the moment of sudden change in the patient's emotions. A sliding window Fourier transform was used to calculate the dominant frequency of the frequency characteristics. A critical shift point was marked when the frequency shift between adjacent windows exceeded 5 Hz, indicating a change in the EEG rhythm. After aligning the two types of feature points by time, it was found that an amplitude inflection point accompanied by a critical frequency shift point occurred 2 minutes before the start of the speech, at which point the gamma wave energy decreased from 200 μV. 2 Jump to 450μV 2 Simultaneously, the dominant frequency shifted from 32Hz to 38Hz. Based on the temporal matching relationship between these two types of feature points, a collaborative association rule was established. A valid coupling event was defined as a time difference of less than 3 seconds between the two. For each valid coupling event, the product of the amplitude change rate and the frequency shift was used as the dynamic fusion parameter, where the change rate equals the amplitude difference divided by the time interval, and the shift was directly taken in Hz. In the final generated tracking and detection map, points with a fusion parameter greater than 300 were connected by solid lines to form a high-coupling-strength trajectory, and points between 100 and 300 were connected by dashed lines to form a medium-strength trajectory, clearly showing the dynamic changes in cardio-brain coupling during the patient's presentation preparation, presentation, and rest / recovery phases.

[0109] In this embodiment, the method achieves a refined representation of the heart-brain coupling characteristics through the collaborative analysis and dynamic fusion of multi-dimensional motion trajectories. The generated tracking and detection map can intuitively display the dynamic evolution of physiological state, providing a more comprehensive and objective basis for the assessment of mood disorders.

[0110] To address the accuracy issue of dynamic trajectory marking in heart-brain coupling, in some embodiments, step 303: dynamically parameterizing and fusing the turning point and the critical offset point according to the collaborative association rule to generate a tracking detection map, includes:

[0111] Step 401: Establish a mapping relationship between the turning point and the critical offset point according to the collaborative association rule.

[0112] In step 401, the mapping relationship refers to the corresponding matching relationship between the turning point and the critical offset point in the time dimension, reflecting the physiological correlation between amplitude mutation and frequency jump.

[0113] In this embodiment, before establishing the mapping relationship, the timestamps of the turning point and the phase timestamps of the critical offset point need to be aligned on a unified time axis. This is achieved by comparing their time labels: when the timestamp of the turning point falls within the physiological rhythm cycle (such as the RR interval of ECG or the alpha rhythm cycle of EEG) corresponding to the critical offset point, it is determined to be a valid alignment. After alignment is determined, the time difference between the turning point and the critical offset point is calculated. When the difference is less than a preset threshold, a mapping relationship is established to ensure that the two types of feature changes have temporal consistency, forming a feature point pair reflecting the coordinated changes of the heart and brain.

[0114] Step 402: Within each time window, based on the mapping relationship, the amplitude change rate corresponding to the inflection point and the frequency offset corresponding to the critical offset point are parameterized and combined to generate a dynamic coupling parameter vector for each time window.

[0115] In step 402, the amplitude change rate refers to the instantaneous rate of change obtained by dividing the amplitude difference between adjacent time windows at the inflection point by the time interval. It is calculated by dividing the difference between the peak amplitude of the current window and the peak amplitude of the previous window by the window duration of 5 seconds, reflecting the instantaneous change rate of the heart-brain coupling strength. The frequency offset refers to the absolute difference between the dominant frequency and the reference frequency of the current window at the critical offset point. It is obtained by comparing the energy centroid frequency of the 30-40Hz band in the current window with the patient's resting reference frequency of 35Hz, reflecting the degree of deviation in the brain rhythm. The dynamic coupling parameter vector is a multidimensional feature vector formed by the combination of the amplitude change rate and the frequency offset, characterizing the strength and pattern of heart-brain coupling within a specific time window.

[0116] In this embodiment of the application, for each successfully mapped feature point pair, the slope of the amplitude change at the inflection point is extracted as the rate of change, and the absolute value of the frequency offset at the critical offset point is obtained as the offset. The two are combined according to a preset weight to form a feature vector with a time stamp.

[0117] Step 403: Based on the joint distribution density of the dynamic coupling parameter vectors of the N time windows, connect the dynamic coupling parameter vectors of adjacent time windows in chronological order to form a dynamic trajectory marker for cardiac-cerebral resonance.

[0118] In step 403, the joint distribution density refers to the degree of aggregation of dynamic coupling parameter vectors from multiple time windows in the feature space, reflecting the stability of the heart-brain coupling state. Adjacent time windows refer to two adjacent analysis windows in a continuous time series, i.e., the nth window and the (n+1)th window, with a predetermined overlap ratio between them (typically 30%-50%). This overlap achieves a smooth transition of parameter vectors. The heart-brain resonance dynamic trajectory marker refers to a continuous trajectory line formed by connecting the dynamic coupling features of multiple time windows in chronological order. This marker reflects the continuous changes in coupling strength and frequency characteristics over time through color intensity and line thickness.

[0119] In this embodiment, the cosine similarity of the feature vectors of adjacent windows is calculated. When the similarity exceeds a set standard, it is determined to be a continuous coupling event. The vectors of these windows are then smoothly connected in chronological order to form a continuous trajectory marker with physiological significance. Specifically, the process involves first calculating the cosine similarity between each pair of the dynamic coupling parameter vectors of N consecutive windows to establish a similarity matrix; then, linear interpolation smoothing is performed on the vectors of adjacent windows with a similarity exceeding 0.85; finally, all vectors that meet the continuity condition are connected in chronological order to form a trajectory marker with temporal coherence. The width of the connecting line is set as a function of the similarity, with higher similarity resulting in a larger line width. For example, when monitoring heart-brain coupling in an anxious state: a time window of 5 seconds is selected, with an overlap of 2 seconds. In window 1 (0-5 seconds), the coupling parameter vector A is detected at the turning point of heart rate acceleration (amplitude change rate +15%) and the critical point of γ brain wave frequency shift (frequency shift +8Hz). In window 2 (3-8 seconds), the coupling parameter vector B is detected at the turning point of heart rate fluctuation (amplitude change rate ±5%) and the critical point of β brain wave frequency shift (frequency shift +4Hz). During the overlap period (3-5 seconds), the cosine similarity between A and B is calculated to be 0.82 (>threshold 0.8). Then, the trajectories of A and B are linearly interpolated and connected.

[0120] Step 404: Generate a tracking and detection map based on the spatial distribution pattern of the cardiac-brain resonance dynamic trajectory markers in the time-frequency domain.

[0121] In step 404, the spatial distribution morphology refers to the geometric distribution characteristics of the dynamic trajectory markers in the three-dimensional space of time-frequency energy. It is obtained by mapping the following three-dimensional parameters: time axis: the center time point of each window; amplitude axis: the normalized value of the rate of change of the inflection point; frequency axis: the frequency shift of the critical offset point; the projection points of the dynamic trajectory markers in the above three-dimensional space are subjected to Delaunay triangulation, and the resulting curved mesh is the spatial distribution morphology.

[0122] In this embodiment, continuous trajectory markers are projected onto a time-frequency coordinate system, feature vectors of adjacent time points are connected by curves, trajectory thickness is set according to vector magnitude, and vertical position is determined based on frequency value to form a three-dimensional dynamic trajectory network.

[0123] Here is a specific example:

[0124] In monitoring the heart-brain coupling state of anxiety disorder patients, data from 360 consecutive 5-second time windows were first analyzed. When the average gamma wave energy of the 150th window was detected to be 200 μV... 2 The voltage in window 151 suddenly increased to 450 μV. 2 At that time, the calculated amplitude change rate was (450-200) / 5 = 50μV. 2 / second, exceeding the threshold of 30μV 2 The amplitude inflection point was determined to be 6 / second. Simultaneously, a shift in the dominant frequency from 32Hz to 38Hz was observed in window 152; this frequency shift of 6Hz exceeded the 5Hz threshold and was marked as a critical shift point. Since the time difference between the inflection point and the critical shift point was 2 seconds, less than the 3-second threshold, an effective mapping relationship was established. The amplitude change rate corresponding to this inflection point was set to 50μV. 2 The frequency offset of 6Hz corresponding to the critical offset point was parameterized and combined, and the feature vector value of this window was calculated using the formula: Dynamic Coupling Parameter = Rate of Change × Offset, resulting in a value of 300. The feature vector values ​​of the subsequent five consecutive windows were 320, 350, 380, 400, and 420, respectively. The cosine similarity between adjacent vectors was calculated to be greater than 0.9, indicating high continuity. These vectors were connected in chronological order to form a high-coupling trajectory segment lasting 30 seconds. The window corresponding to the vector value of 400 was at the beginning of the speech, at which point the trajectory line width increased to three times the baseline value to highlight the key node. In the final generated tracking and detection map, this trajectory segment appeared as a thick solid line in the 35-38Hz frequency band, contrasting sharply with the thin dashed line in the 30-32Hz frequency band during the rest period. This accurately captured the enhanced cardio-brain coupling phenomenon caused by speech stress. The synergistic pattern of gamma wave energy and heart rate changes was visualized as a synchronous increase in trajectory density and frequency band position, providing doctors with an intuitive basis for judging the level of anxiety.

[0125] In this embodiment, the method establishes a precise feature point mapping relationship and a parameterized fusion mechanism to achieve refined labeling of the dynamic trajectory of heart-brain coupling. The generated map can clearly distinguish the differences in coupling patterns under different psychological states, providing a more reliable objective basis for clinical assessment.

[0126] To address the issue of insufficient accuracy in identifying feature components in mixed biological signals, in some embodiments, step 102: identifying the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the mixed oscillating waveform features includes:

[0127] Step 501: Divide the hybrid oscillation waveform features into multiple candidate components according to a preset frequency band distribution.

[0128] In step 501, the candidate component refers to the sub-band signal separated from the mixed oscillation waveform by frequency band division, and each candidate component contains waveform feature information within a specific frequency range.

[0129] In this embodiment, wavelet packet decomposition is used to divide the hybrid oscillation waveform into multiple sub-bands according to the physiological signal characteristic frequency band. Each sub-band covers a specific frequency range, forming a set of candidate components containing complete oscillation characteristics.

[0130] Step 502: Calculate the first correlation coefficient between each candidate component and the preset ECG wave feature template, and the second correlation coefficient between each candidate component and the EEG rhythm feature template.

[0131] In step 502, the first correlation coefficient refers to the degree of matching between the candidate component and the standard ECG waveform feature template in terms of waveform morphology. The second correlation coefficient refers to the degree of matching between the candidate component and the standard EEG rhythm feature template.

[0132] In this embodiment, the Pearson correlation coefficient between the time-domain waveform of each candidate component and a preset ECG waveform template (including typical PQRST waveform) is calculated as the first correlation coefficient, and the correlation coefficient between the component and a preset EEG rhythm template (including α / β / γ rhythm features) is calculated as the second correlation coefficient.

[0133] Step 503: If the first correlation coefficient is greater than the second correlation coefficient, the corresponding candidate component is determined as an ECG candidate component; if the second correlation coefficient is greater than the first correlation coefficient, the corresponding candidate component is determined as an EEG candidate component.

[0134] In step 503, candidate components of electrocardiogram (ECG) waves refer to sub-frequency band signals whose waveform characteristics are closer to those of ECG signals. Candidate components of electroencephalogram (EEG) waves refer to sub-frequency band signals whose characteristics are closer to those of EEG signals.

[0135] In this embodiment of the application, each candidate component is compared with its first and second correlation coefficients. When the first correlation coefficient is greater than the second correlation coefficient and exceeds 0.7, it is classified as an ECG candidate component. When the second correlation coefficient is dominant and exceeds 0.7, it is classified as an EEG candidate component.

[0136] Step 504: Determine the candidate ECG components that meet the preset ECG frequency constraints as ECG oscillation components, and determine the candidate EEG components that meet the preset EEG rhythm constraints as EEG rhythm fluctuation components.

[0137] In step 504, the ECG frequency constraint refers to the frequency range that conforms to the electrophysiological characteristics of the heart. The EEG rhythm constraint refers to the frequency range that conforms to the characteristics of the EEG rhythm.

[0138] In this embodiment of the application, the main frequency of the candidate components of the electrocardiogram is checked to see if it is within the range of 0.5-40Hz, and the main frequency of the candidate components of the electroencephalogram is checked to see if it is within a specific rhythm frequency band. Finally, the components that meet the conditions are selected as valid feature components.

[0139] Here is a specific example:

[0140] In monitoring the cardio-brain coupling state of patients with anxiety disorders, wavelet packet decomposition was first performed on the mixed oscillation waveform within a 5-second time window, dividing it into five candidate components: 0.5-4Hz, 4-8Hz, 8-13Hz, 13-30Hz, and 30-50Hz. For the 30-50Hz candidate component, the correlation coefficient between its waveform and the standard ECG R-wave template was calculated to be 0.15, and the correlation coefficient with the EEG gamma-wave template reached 0.88. Since 0.88 is greater than 0.15 and exceeds the threshold of 0.7, this component was initially classified as an EEG candidate component. Further analysis showed that the dominant frequency of this component was 35Hz, falling within the 30-50Hz range of the EEG gamma-wave characteristic frequency band, and it was ultimately confirmed as an EEG rhythmic fluctuation component. Simultaneously, the 0.5-4Hz component had a correlation coefficient of 0.92 with the ECG template and 0.25 with the EEG template, and its dominant frequency of 1.5Hz conformed to the ECG characteristic range of 0.5-40Hz, thus identifying it as an ECG oscillation component. During the patient's tense speaking period, the RR interval of the ECG component shortened to 0.65 seconds, and the energy of the EEG gamma component increased to 2.8 times that of the resting state. The coherence coefficient of the two in the 30-40Hz frequency band was calculated using the formula coherence coefficient = Sxy(f) / sqrt(Sxx(f)Syy(f)), where Sxy(f) is the cross spectral density, and Sxx(f) and Syy(f) are the self spectral densities. The calculated value was 0.87, which is higher than other frequency bands.

[0141] In this embodiment, the method achieves high-precision separation of feature components in mixed biological signals through multi-level correlation coefficient comparison and physiological frequency band verification, providing reliable feature input for subsequent heart-brain coupling analysis and improving the accuracy of resonance feature detection.

[0142] To address the issue of insufficient accuracy in identifying the cardiac-brain coupling resonance interval, in some embodiments, step 103: determining the resonance correlation interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component includes:

[0143] Step 601: Calculate the synchronization index of the ECG oscillation component and the EEG rhythm fluctuation component in the time-frequency domain. The synchronization index includes: phase lock value and energy coupling coefficient.

[0144] In step 601, the phase-lock value refers to the degree to which the phase difference between the electrocardiogram (ECG) and the electroencephalogram (EEG) remains stable at a specific time-frequency point. The energy coupling coefficient refers to the degree of coordination between the energy changes of the two signals in the time-frequency domain.

[0145] In this embodiment, the instantaneous phases of the electrocardiogram oscillation component and the electroencephalogram rhythm component are extracted by Hilbert transform, and the reciprocal of the standard deviation of the phase difference within the sliding time window is calculated as the phase locking value. At the same time, the correlation coefficient of the time-frequency energy matrices of the two signals is calculated as the energy coupling coefficient.

[0146] Step 602: Mark the time-frequency regions where the synchronization index exceeds the threshold as candidate resonance intervals.

[0147] In step 602, the candidate resonance interval refers to the region in the time-frequency matrix where the synchronicity index exceeds the preset standard, reflecting the time-frequency range where physiological coupling may exist.

[0148] In this embodiment, the time-frequency plane is divided into several units. For each unit, the phase lock value and energy coupling coefficient are checked simultaneously to see if they reach their respective thresholds. Units that meet both criteria are marked as candidate resonance intervals.

[0149] Step 603: Merge the candidate resonance intervals that are time-continuous and have overlapping frequency bands into a resonance correlation interval.

[0150] In this embodiment, a morphological dilation algorithm is used to process the candidate resonance interval marker map, merge candidate intervals that are temporally adjacent and frequency-overlapping, remove isolated small blocks, and retain the coherent region that meets the minimum duration and bandwidth requirements as the final resonance association interval.

[0151] Here is a specific example:

[0152] When monitoring the cardio-brain coupling state of patients with anxiety disorders, time-frequency analysis was first performed on the separated ECG R-wave component and EEG gamma-wave component. Hilbert transform was used to calculate the phase difference between the two in the 30-40Hz frequency band. When the patient was in the presentation preparation stage, a phase difference standard deviation of 0.92 was detected for 25 seconds in the 32-38Hz frequency band, exceeding the phase lock-in threshold of 0.85. The calculation formula is: Phase lock-in value = 1 / σ(Δφ), where σ(Δφ) represents the standard deviation of the phase difference. Simultaneously, the energy coupling coefficient of the two signals during this period was calculated using the formula: Energy coupling coefficient = cov(E1,E2) / σ(E1)σ(E2), yielding 0.89, exceeding the energy coupling threshold of 0.8. Here, cov represents the covariance, and σ represents the standard deviation. The time-frequency region where both indicators meet the criteria is marked as a candidate resonance interval. It is found that this interval contains three temporally continuous candidate blocks: 32-35Hz for the first 5 seconds, 34-37Hz for the middle 10 seconds, and 35-38Hz for the last 10 seconds. By merging these three overlapping and temporally adjacent blocks using a morphological dilation algorithm, a resonance correlation interval of 25 seconds in the 32-38Hz frequency band is finally formed.

[0153] In the embodiments of this application, the method achieves precise locking of the cardiac-brain resonance feature region through dual-index collaborative analysis and spatiotemporal continuity verification, providing a reliable time-frequency positioning basis for dynamic coupling tracking and improving the accuracy of physiological state assessment.

[0154] To address the issue of incomplete signal purification within the resonance interval, in some embodiments, step 104: the interference cancellation processing of the composite oscillation waveform within the resonance-related interval in the hybrid oscillation waveform features includes:

[0155] Step 701: Decompose the composite oscillation waveform into resonance components and interference components.

[0156] In step 701, the resonant component refers to the effective signal component in the composite oscillation waveform that is related to the heart-brain coupling. The interference component refers to non-physiological interference signals such as electromyography artifacts and power frequency noise.

[0157] In this embodiment, an adaptive decomposition algorithm is used to separate the composite oscillation waveform into several intrinsic mode functions. Based on the degree of matching between the spectral characteristics of each component and the resonance correlation interval, the resonant component containing heart-brain coupling characteristics and irrelevant interference components are identified.

[0158] Step 702: Attenuate the resonance component and eliminate the interference component through the dynamic suppression mechanism corresponding to the resonance correlation interval to obtain the processed resonance component.

[0159] In step 702, the dynamic suppression mechanism refers to a signal processing strategy designed based on the characteristics of the resonance correlation interval, which includes the dual functions of resonance enhancement and interference suppression.

[0160] In this embodiment, a bandpass filter bank based on the frequency band characteristics of the resonance interval is constructed to selectively enhance the resonance component. At the same time, an adaptive noise cancellation technique is adopted to perform real-time cancellation processing using a reference model of the interference component.

[0161] Step 703: Reconstruct the waveform of the processed resonance component to obtain the waveform reconstruction result, which is the composite oscillation waveform after elimination.

[0162] In step 703, waveform reconstruction refers to the process of recombining the processed signal components into a complete waveform.

[0163] In this embodiment, the enhanced resonant component and the background signal that has undergone noise cancellation processing are resynthesized according to the original time-frequency relationship, retaining the effective coupling characteristics while eliminating interference components.

[0164] Here is a specific example:

[0165] In monitoring the cardio-brain coupling state of anxiety patients, for the composite oscillation waveform within the established 30-40Hz resonance correlation range, empirical mode decomposition was first used to decompose it into five intrinsic mode components. The third component, with an energy concentration of 85% at 35Hz and a phase lock value of 0.91 with the ECG R-wave, was identified as an effective resonance component. The calculation formula is: phase lock value = 1 / σ(Δφ), where σ(Δφ) represents the standard deviation of the phase difference. The first component exhibits a periodic fluctuation with a constant amplitude at 50Hz, accounting for 12% of the energy, and was identified as a power frequency interference component. Subsequently, a Chebyshev filter with a center frequency of 35Hz and a bandwidth of 5Hz was constructed to enhance the resonance component, improving the signal-to-noise ratio of the filtered component to 28dB. Simultaneously, adaptive noise cancellation technology was employed, using a 50Hz sine wave as a reference signal, and the filter coefficients were iteratively updated using a least mean square algorithm, ultimately suppressing the power frequency interference to below 5% of the original energy. The processed resonance component and the remaining purified background component are reconstructed according to the original time relationship to obtain a clean signal with a signal-to-noise ratio of 42dB.

[0166] In this embodiment, the method achieves effective purification of signals within the resonance interval through targeted component separation and dynamic suppression processing, improves the extraction quality of heart-brain coupling features, and lays the foundation for accurate state assessment.

[0167] Figure 2 A schematic diagram of a dynamic synchronous tracking and detection system based on bio-wave resonance provided in this application embodiment is shown below. Figure 2As shown, the system includes:

[0168] The acquisition module 21 is used to acquire the characteristics of the mixed oscillation waveform formed by the coupling of electrocardiogram signals and electroencephalogram signals.

[0169] The identification module 22 is used to identify the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the mixed oscillation waveform features.

[0170] The determination module 23 is used to determine the resonance correlation interval based on the electrocardiogram oscillation component and the brain wave rhythm fluctuation component.

[0171] The interference elimination module 24 is used to perform interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform characteristics.

[0172] The generation module 25 is used to generate a tracking and detection map characterizing the heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series.

[0173] Figure 2 The aforementioned dynamic synchronous tracking and detection system based on bio-wave resonance can perform... Figure 1 The implementation principle and technical effects of the dynamic synchronous tracking and detection method based on bio-wave resonance described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the dynamic synchronous tracking and detection system based on bio-wave resonance in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0174] In one possible design, Figure 2 The illustrated embodiment of a dynamic synchronous tracking and detection system based on bio-wave resonance can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0175] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0176] The processing component 32 is used to perform the above. Figure 1 The embodiment describes a dynamic synchronous tracking and detection method based on bio-wave resonance.

[0177] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0178] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0179] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0180] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0181] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0182] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0183] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a dynamic synchronous tracking and detection method based on bio-wave resonance.

[0184] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0185] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0186] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A dynamic synchronous tracking and detection method based on bio-wave resonance, characterized in that, include: To acquire the characteristics of a hybrid oscillatory waveform formed by the coupling of electrocardiogram (ECG) and electroencephalogram (EEG) signals; The electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component are identified from the hybrid oscillation waveform characteristics; The resonance correlation interval is determined based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component; Interference cancellation processing is performed on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform characteristics; Based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series, a tracking and detection map characterizing the heart-brain coupling resonance is generated. The generation of a tracking and detection atlas characterizing heart-brain coupling resonance is based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series, including: Construct a time-dimensional distribution framework of the eliminated composite oscillation waveform within N consecutive time windows, where N is greater than or equal to 3; Based on the aforementioned time-dimensional distribution framework, the characteristic evolution path of the eliminated composite oscillation waveform is determined; Based on the feature evolution path, a tracking and detection map is generated; The characteristic evolution path includes a first trajectory of amplitude change trend within N consecutive time windows and a second trajectory of frequency drift trend within N consecutive time windows. Based on the aforementioned feature evolution path, a tracking and detection map is generated, including: The first motion trajectory and the second motion trajectory are combined and encoded to form time-coded data; Based on the time-coded data, a collaborative association rule is constructed between the turning point in the first motion trajectory and the critical offset point in the second motion trajectory; Based on the collaborative association rules, the turning point and the critical offset point are dynamically parameterized and fused to generate a tracking and detection map; The step of dynamically parameterizing and fusing the turning point and the critical offset point according to the collaborative association rule to generate a tracking and detection map includes: Based on the collaborative association rules, a mapping relationship is established between the turning point and the critical offset point; Within each time window, based on the mapping relationship, the amplitude change rate corresponding to the inflection point and the frequency offset corresponding to the critical offset point are parameterized and combined to generate a dynamic coupling parameter vector for each time window. Based on the joint distribution density of the dynamic coupling parameter vectors of the N time windows, the dynamic coupling parameter vectors of adjacent time windows are connected in chronological order to form a dynamic trajectory marker for cardiac-cerebral resonance. Based on the spatial distribution of the cardiac-brain resonance dynamic trajectory markers in the time-frequency domain, a tracking and detection map is generated.

2. The method according to claim 1, characterized in that, The process of identifying the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the mixed oscillation waveform features includes: The hybrid oscillation waveform features are divided into multiple candidate components according to a preset frequency band distribution; Calculate the first correlation coefficient between each candidate component and the preset ECG wave feature template, and the second correlation coefficient between each candidate component and the EEG rhythm feature template; If the first correlation coefficient is greater than the second correlation coefficient, the corresponding candidate component is determined as an electrocardiogram (ECG) candidate component; if the second correlation coefficient is greater than the first correlation coefficient, the corresponding candidate component is determined as an electroencephalogram (EEG) candidate component. Candidate ECG components that meet the preset ECG frequency constraints are identified as ECG oscillation components, and candidate EEG components that meet the preset EEG rhythm constraints are identified as EEG rhythm fluctuation components.

3. The method according to claim 1, characterized in that, The step of determining the resonance correlation interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component includes: Calculate the synchronization index of the ECG oscillation component and the EEG rhythm fluctuation component in the time-frequency domain. The synchronization index includes: phase lock value and energy coupling coefficient. The time-frequency region where the synchronicity index exceeds the threshold is marked as a candidate resonance interval; Candidate resonance intervals that are time-continuous and have overlapping frequency bands are merged into resonance correlation intervals.

4. The method according to claim 1, characterized in that, The interference cancellation process for the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform features includes: The composite oscillation waveform is decomposed into a resonance component and a disturbance component; By using the dynamic suppression mechanism corresponding to the resonance correlation interval, the resonance component is attenuated and the interference component is eliminated to obtain the processed resonance component. The processed resonant components are reconstructed to obtain the waveform reconstruction result, which is the composite oscillation waveform after elimination.

5. A dynamic synchronous tracking and detection system based on bio-wave resonance, characterized in that, include: The acquisition module is used to acquire the characteristics of the mixed oscillation waveform formed by the coupling of electrocardiogram (ECG) signals and electroencephalogram (EEG) signals; The identification module is used to identify the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component from the hybrid oscillation waveform features; The determination module is used to determine the resonance correlation interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component; An interference elimination module is used to perform interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the features of the hybrid oscillation waveform. The generation module is used to generate a tracking and detection map characterizing the heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series. The generation of a tracking and detection atlas characterizing heart-brain coupling resonance is based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series, including: Construct a time-dimensional distribution framework of the eliminated composite oscillation waveform within N consecutive time windows, where N is greater than or equal to 3; Based on the aforementioned time-dimensional distribution framework, the characteristic evolution path of the eliminated composite oscillation waveform is determined; Based on the feature evolution path, a tracking and detection map is generated; The characteristic evolution path includes a first trajectory of amplitude change trend within N consecutive time windows and a second trajectory of frequency drift trend within N consecutive time windows. Based on the aforementioned feature evolution path, a tracking and detection map is generated, including: The first motion trajectory and the second motion trajectory are combined and encoded to form time-coded data; Based on the time-coded data, a collaborative association rule is constructed between the turning point in the first motion trajectory and the critical offset point in the second motion trajectory; Based on the collaborative association rules, the turning point and the critical offset point are dynamically parameterized and fused to generate a tracking and detection map; The step of dynamically parameterizing and fusing the turning point and the critical offset point according to the collaborative association rule to generate a tracking and detection map includes: Based on the collaborative association rules, a mapping relationship is established between the turning point and the critical offset point; Within each time window, based on the mapping relationship, the amplitude change rate corresponding to the inflection point and the frequency offset corresponding to the critical offset point are parameterized and combined to generate a dynamic coupling parameter vector for each time window. Based on the joint distribution density of the dynamic coupling parameter vectors of the N time windows, the dynamic coupling parameter vectors of adjacent time windows are connected in chronological order to form a dynamic trajectory marker for cardiac-cerebral resonance. Based on the spatial distribution of the cardiac-brain resonance dynamic trajectory markers in the time-frequency domain, a tracking and detection map is generated.

6. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the dynamic synchronous tracking and detection method based on bio-wave resonance as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a dynamic synchronous tracking and detection method based on bio-wave resonance as described in any one of claims 1 to 4.

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