Dynamic synchronous tracking detection method and system based on biological wave resonance

Abstract

The invention relates to the technical field of biological wave resonance, provides a dynamic synchronous tracking detection method and system based on biological wave resonance, and aims to solve the problem of low dynamic synchronous detection precision of heart and brain coupled biological wave signals in the prior art. The method comprises the following steps: acquiring a mixed oscillation waveform feature formed by coupling an electrocardiosignal and a brain wave signal; identifying an electrocardiogram wave oscillation component and a brain wave rhythm fluctuation component from the mixed oscillation waveform features; determining a resonance correlation interval according to the electrocardiowave oscillation component and the brain wave rhythm fluctuation component; performing interference elimination processing on a composite oscillation waveform in a resonance correlation interval in the mixed oscillation waveform characteristics; and based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in the continuous time sequence, generating a tracking detection atlas representing heart-brain coupling resonance. According to the invention, the dynamic synchronous detection precision of the heart-brain coupled biological wave signal is improved.

Description

Technical Field

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

[0002] In the field of health monitoring, real-time tracking of the dynamic coupling characteristics of heart and brain waves is crucial for the early diagnosis of neurological diseases. Due to the frequency overlap and strong noise interference of heart and brain signals, a technical solution is needed that can accurately separate the resonant components of these two physiological signals and enable dynamic, synchronous analysis.

[0003] Currently, a coupling analysis method based on fixed-frequency band decomposition is used. First, the electrocardiogram and brain wave signals are separated by a preset frequency band filter, and then the linear coherence coefficient of the two signals in the fixed frequency band is calculated. Finally, the heart-brain coupling state is judged based on the coherence threshold.

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

[0005] The present application provides a dynamic synchronous tracking detection method and system based on biowave resonance, which is used to solve the problem of low accuracy of dynamic synchronous detection of brain-brain coupled biowave signals in the prior art.

[0006] In a first aspect, the present application provides a dynamic synchronous tracking detection method based on biowave resonance, comprising:

[0007] Acquire the mixed oscillation waveform characteristics formed by coupling the electrocardiogram signal and the brain wave signal;

[0008] identifying a cardiac wave oscillation component and a brain wave rhythm fluctuation component from the mixed oscillation waveform characteristics;

[0009] determining a resonance correlation interval according to the electrocardiogram oscillation component and the brainwave rhythm fluctuation component;

[0010] performing interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform feature;

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

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

[0013] Constructing a time dimension distribution framework of the eliminated composite oscillation waveform in N consecutive time windows, where N is greater than or equal to 3;

[0014] Determining a characteristic evolution path of the eliminated composite oscillation waveform based on the time dimension distribution framework;

[0015] According to the feature evolution path, a tracking detection map is generated.

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

[0017] According to the feature evolution path, a tracking detection map is generated, including:

[0018] Combining and encoding the first motion trajectory and the second motion trajectory to form time-coded data;

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

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

[0021] Optionally, 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:

[0022] Establishing a mapping relationship between the turning point and the critical offset point according to the collaborative association rule;

[0023] In each time window, according to the mapping relationship, the amplitude change rate corresponding to the turning 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 time order to form a heart-brain resonance dynamic trajectory marker;

[0025] A tracking detection map is generated according to the spatial distribution of the heart-brain resonance dynamic trajectory markers in the time-frequency domain.

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

[0027] Dividing the mixed oscillation waveform characteristics into a plurality of candidate components according to a preset frequency band distribution;

[0028] Calculating a first correlation coefficient between each candidate component and a preset electrocardiogram feature template, and a second correlation coefficient between each candidate component and an electroencephalogram rhythm feature template;

[0029] When the first correlation coefficient is greater than the second correlation coefficient, the corresponding candidate component is determined as a cardiac wave candidate component; when the second correlation coefficient is greater than the first correlation coefficient, the corresponding candidate component is determined as a brain wave candidate component;

[0030] The candidate cardiac wave components that meet the preset cardiac wave frequency constraint conditions are determined as cardiac wave oscillation components, and the candidate brain wave components that meet the preset brain wave rhythm constraint conditions are determined as brain wave rhythm fluctuation components.

[0031] Optionally, determining the resonance correlation interval according to the electrocardiogram oscillation component and the brainwave rhythm fluctuation component includes:

[0032] Calculating synchronization indicators between the electrocardiogram oscillation component and the brainwave rhythm fluctuation component in the time-frequency domain, the synchronization indicators including: phase locking value and energy coupling coefficient;

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

[0034] The candidate resonance intervals that are continuous in time and have overlapping frequency bands are merged into resonance correlation intervals.

[0035] Optionally, performing interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform feature includes:

[0036] Decomposing the composite oscillation waveform into a resonance component and an interference component;

[0037] Attenuating the resonance component and eliminating the interference component through a dynamic suppression mechanism corresponding to the resonance correlation interval to obtain a processed resonance component;

[0038] The processed resonance component is subjected to waveform reconstruction to obtain a waveform reconstruction result, wherein the waveform reconstruction result is a composite oscillation waveform after elimination.

[0039] In a second aspect, the present application provides a dynamic synchronous tracking detection system based on biowave resonance, comprising:

[0040] An acquisition module is used to acquire the mixed oscillation waveform characteristics formed by coupling the electrocardiogram signal and the brain wave signal;

[0041] an identification module, configured to identify the electrocardiogram oscillation component and the brainwave rhythm fluctuation component from the mixed oscillation waveform characteristics;

[0042] a determination module, configured to determine a resonance correlation interval based on the electrocardiogram oscillation component and the brainwave rhythm fluctuation component;

[0043] an elimination module, configured to perform interference elimination processing on the composite oscillation waveform within the resonance correlation interval of the hybrid oscillation waveform feature;

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

[0045] In a third aspect, the present application provides a computing device comprising 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 biowave resonance as described in any one of the first aspects.

[0046] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a dynamic synchronous tracking and detection method based on biowave resonance as described in any one of the first aspects.

[0047] In the present application, a dynamic synchronous tracking detection method based on biowave resonance is provided, which includes: obtaining a mixed oscillation waveform feature formed by the coupling of an electrocardiogram signal and an electroencephalogram signal; identifying an electrocardiogram oscillation component and an electroencephalogram rhythm fluctuation component from the mixed oscillation waveform feature; determining a resonance association interval based on the electrocardiogram oscillation component and the electroencephalogram rhythm fluctuation component; performing interference elimination processing on a composite oscillation waveform within the resonance association interval in the mixed oscillation waveform feature; and generating a tracking 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.

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

[0049] This application captures the time-frequency domain coupling characteristics of ECG and 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 the sensitive frequency bands where energy interacts between heart and brain signals, narrowing the scope of targeted analysis. It enhances the signal-to-noise ratio of homologous resonance signals and retains the effective components reflecting physiological coupling. It visualizes the dynamic evolution of heart-brain coupling, supporting the identification of pathological features.

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

[0051] In addition, the dynamic trajectory of the heart-brain resonance signal in long-term monitoring can be visualized, and the detection rate of weak coupling features can be improved through multi-window joint analysis, providing a continuous quantitative basis in the time dimension for early warning of diseases.

[0052] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 A flow chart of a dynamic synchronous tracking detection method based on biowave resonance provided in an embodiment of the present application;

[0055] Figure 2 A schematic structural diagram of a dynamic synchronous tracking and detection system based on biowave resonance provided in an embodiment of the present application;

[0056] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0058] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0059] Existing methods for analyzing brain-heart coupling based on fixed-band decomposition have limitations: their pre-defined frequency bands struggle to adapt to dynamic frequency shifts caused by individual physiological differences, leading to missed detection of key resonance features; linear coherence analysis captures only amplitude correlations while ignoring nonlinear coupling modes such as phase synchronization, resulting in distortion in the analysis of weak interaction signals; and fixed threshold strategies cannot distinguish between valid resonances and random interference in strong noise environments, severely limiting the sensitivity and reliability of dynamic tracking. These shortcomings fundamentally stem from a static and one-sided modeling of the brain-heart signal coupling mechanism, which fails to meet the needs of clinical precision monitoring.

[0060] In response to the above problems, this application proposes a dynamic synchronous tracking detection method based on biowave resonance. By extracting the characteristics of the mixed oscillation waveform formed by the coupling of electrocardiogram and brain waves, the resonance correlation interval of the two is dynamically identified, and targeted interference elimination is implemented within this interval. Its innovation is reflected in: first, the resonance frequency band is adaptively determined based on the overlapping characteristics of the time-frequency domain, breaking through the fixed frequency band limitation; second, by decomposing the homologous resonance components in the composite oscillation waveform, the amplitude change and phase drift characteristics are synchronously analyzed to achieve complete modeling of nonlinear coupling; finally, a tracking map is generated based on the dynamic evolution law in the continuous time series to avoid the rigidity of threshold judgment. This method fundamentally solves the three core problems of poor frequency band adaptability, single coupling mode analysis and insufficient noise suppression in the existing technology, and provides a high-precision and high-robustness dynamic analysis tool for the study of cardio-brain function interaction.

[0061] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0062] Figure 1 A flow chart of a dynamic synchronous tracking detection method based on biowave resonance provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0063] Step 101: Acquire the mixed oscillation waveform characteristics formed by coupling the electrocardiogram signal and the brain wave signal.

[0064] In step 101, the ECG signal represents a periodic electrical signal generated by the electrical activity of the heart, mainly including characteristic waveforms such as P wave, QRS complex, and T wave, reflecting the heart beat state. The EEG signal represents a rhythmic electrical signal generated by the electrical activity of brain neurons, including different frequency bands (such as α wave, β wave, γ wave, etc.), reflecting the functional state of the brain. The mixed oscillation waveform feature represents a composite signal formed by the superposition of ECG waves and EEG waves in the time-frequency domain, including the coupling characteristics of the two, such as frequency band overlap and phase synchronization.

[0065] In an embodiment of the present application, electrocardiogram and brain wave signals are synchronously recorded by a multi-channel bioelectric signal acquisition device, time-frequency analysis is performed on the original signals, the spectral distribution characteristics of the two in the same time window are extracted, and the energy interaction relationship between the electrocardiogram and brain waves in each frequency band is calculated to form mixed oscillation waveform characteristic data including amplitude, phase, and frequency band coupling characteristics.

[0066] For example, when monitoring the heart-brain coupling state of patients with anxiety disorders, their ECG signals (electrodes attached to the chest) and EEG signals (electrodes attached to the scalp) are collected synchronously, and wavelet transform is used to analyze the spectral distribution of the two within a 5-second time window. It is found that there is energy overlap between the ECG R wave and the EEG gamma wave (above 30Hz), forming a mixed oscillation waveform feature, which provides a data basis for subsequent analysis.

[0067] Step 102: Identify the electrocardiogram oscillation component and the brainwave 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 feature 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 α waves (8-13Hz) or γ waves (>30Hz).

[0069] In an embodiment of the present application, a blind source separation algorithm is used to decompose the mixed oscillation waveform characteristics. According to the typical frequency band distribution characteristics of electrocardiogram (ECG) and brain waves, components that conform to ECG low-frequency oscillations (0.5-40 Hz) and brain wave rhythm fluctuations (such as α waves, β waves, and γ waves) are screened out, and noise interference is removed to obtain pure ECG oscillation components and brain wave rhythm components.

[0070] For example, in the mixed oscillation 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 the patient was nervous, the ECG RR interval shortened and the EEG gamma wave energy increased, indicating that the heart-brain coupling was enhanced.

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

[0072] In step 103 , the resonance correlation interval represents an interval in which energy interaction exists between the electrocardiogram and the brainwaves within a specific frequency band and time, reflecting the physiological coupling state between the two.

[0073] In an embodiment of the present application, the coherence of the ECG oscillation component and the EEG rhythm component is calculated, and the frequency bands in which the energy of the two changes synchronously in the time-frequency domain are found. A dynamic threshold is set to screen out the resonance interval that meets the physiological coupling characteristics, such as the high-coherence frequency bands of the ECG R wave and the EEG gamma wave within a specific time window.

[0074] For example, in the data of patients with anxiety disorders, the coherence between the ECG R wave and the EEG gamma wave was calculated, and it was found that when the patient was in a tense state, the two resonated in the 30-40Hz frequency band, and this interval was marked as the key resonance correlation interval.

[0075] Step 104: performing interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform feature.

[0076] In step 104, the mixed oscillation waveform features refer to the time-frequency domain coupling characteristics extracted from the original collected 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 ECG and EEG coupling components and noise interference. The two are in a containment relationship—the "mixed oscillation waveform features" represent the overall coupling characteristics of the original signal, while the "composite oscillation waveform" is the actual signal to be processed after filtering for a specific resonant frequency band. Interference elimination processing removes non-physiological noise to preserve the true heart-brain coupling signal.

[0077] In the embodiment of the present application, an adaptive filtering algorithm is used to construct a noise reference model based on the characteristics of the ECG and EEG signals in the resonance correlation interval, dynamically adjust the filtering parameters, suppress irrelevant interference, and enhance the homologous resonance component.

[0078] For example, in the resonance range (30-40Hz) of patients with anxiety disorders, an adaptive filter is used to remove eye artifacts and myoelectric interference, retaining pure EKG-EEG 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, a tracking detection spectrum representing the heart-brain coupling resonance is generated.

[0080] In step 105, the dynamic evolution characteristics represent the temporal trend of the heart-brain coupling signal, such as energy fluctuations, phase synchronization changes, etc. The tracking detection map represents a temporal and spatial distribution diagram that visualizes the heart-brain coupling state.

[0081] In an embodiment of the present application, a time-frequency analysis is performed on the signal after interference elimination, and features such as energy and phase synchronization within a continuous time window are extracted. A three-dimensional visualization technology is used to generate a dynamic evolution graph, with the horizontal axis being time, the vertical axis being frequency, and the color depth representing the coupling strength.

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

[0083] This method dynamically captures the coupling characteristics of ECG and EEG signals, accurately identifies the resonance interval, effectively filters out interference, and generates intuitive tracking detection maps, achieving high-sensitivity and high-reliability monitoring of heart-brain functional interactions, providing an objective evaluation method for emotional disorders, neurological diseases, etc.

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

[0085] Step 201: Construct a time dimension distribution framework of the eliminated composite oscillation waveform in 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 according to a fixed time length. Each window contains complete waveform period information. The framework structure consists of three dimensions: time axis, frequency axis and energy intensity.

[0087] In an embodiment of the present application, the time window length is first set to ensure that it covers at least three complete ECG cycles, the composite oscillation waveform in each window is transformed into a time-frequency transform, the energy distribution of each frequency point is calculated, and the data are arranged in chronological order to form a three-dimensional data matrix, where the rows of the matrix correspond to time points, the columns correspond to frequency points, and the numerical values ​​represent the 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 in the continuous time window, which is composed of key turning points and trend segments, and reflects the dynamic change law of the heart-brain coupling strength.

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

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

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

[0093] Here's a specific example:

[0094] When monitoring the heart-brain coupling state of patients with anxiety disorders, the researchers first synchronously collected the patient's ECG and EEG signals, using electrodes attached to the chest and scalp, respectively. Wavelet transform analysis of the spectral distribution of both signals within a 5-second time window revealed significant energy overlap between the ECG R wave and the EEG gamma wave in the frequency range above 30 Hz, forming a mixed oscillation 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 stressed, the ECG R-R 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 between the two signals revealed that the coherence value in the 30-40 Hz frequency range reached 0.85, higher than in other frequency ranges. Therefore, this interval was designated as a critical resonance correlation interval. After applying an adaptive filter to remove electrooculographic artifacts and electromyographic interference within this interval, a time dimension distribution framework was constructed for the 30-minute monitoring data at 5-second intervals, forming a total of 360 continuous time windows. In each window, the product of the mean γ wave energy and the R wave amplitude was calculated as the coupling strength index. This index rose to 450 μV in the pre-speech period. 2 , the rest period drops to 120μV 2 Finally, these data points are connected to form a characteristic evolution path and mapped into a three-dimensional dynamic graph. The horizontal axis represents time, the vertical axis shows the 30-40Hz frequency band, and the color depth reflects the coupling strength. The graph clearly shows the complete change trajectory of the coupling strength showing a pulsed rise during the tense period and a slow decline during the relaxation period, providing a visual basis for assessing the degree of anxiety.

[0095] In the embodiment of the present application, the method captures the dynamic evolution of the heart-brain coupling signal by establishing a time-continuous analysis framework, generates a visual map that intuitively reflects the changing trend of pathological characteristics, and provides an objective basis for the course monitoring and efficacy evaluation of neurological diseases.

[0096] In order to solve the problem of insufficient multi-dimensional feature fusion in dynamic monitoring of heart-brain coupling, in some embodiments, step 203: the feature evolution path includes a first motion trajectory with an amplitude change trend within N consecutive time windows and a second motion trajectory with a frequency drift trend within N consecutive time windows.

[0097] According to the feature evolution path, a tracking detection map is generated, including:

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

[0099] In step 301 , the time code data refers to a data structure in which the amplitude change information of the first motion trajectory and the frequency change information of the second motion trajectory are uniformly formatted.

[0100] In an embodiment of the present application, the two trajectories are aligned according to the same time reference, the amplitude and frequency values ​​at each time point are normalized, and then a specific encoding rule is used to integrate the two types of data into a joint feature vector sequence with a time mark.

[0101] Step 302: Constructing collaborative association rules between turning points in the first motion trajectory and critical offset points in the second motion trajectory based on the time code data.

[0102] In step 302, turning points are defined as extreme values ​​and inflection points in the amplitude variation trend curve. By calculating the peak amplitude change rate of the composite oscillation waveform within each time window, turning points are identified when the absolute value of the change rate exceeds a preset threshold, reflecting the moment of a sudden change in the strength of the heart-brain coupling. Critical offset points are defined as characteristic points in the frequency drift trend curve where the frequency change direction suddenly changes. By detecting the offset of the dominant frequency within a continuous time window, critical offset points are identified when the offset exceeds the normal physiological fluctuation range, marking the moment of transition in the heart-brain rhythm coupling state. Collaborative association rules refer to the temporal correspondence between the turning points of the first motion trajectory and the critical offset points of the second motion trajectory, as well as their changing patterns.

[0103] In an embodiment of the present application, the extreme points of amplitude change in the first motion trajectory are first identified as turning points, and the inflection points of frequency mutation 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 an association mapping relationship based on time alignment is established.

[0104] Step 303: According to the collaborative association rule, the turning point and the critical offset point are dynamically parameterized and fused to generate a tracking detection map.

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

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

[0107] Here is an example with:

[0108] When monitoring the heart-brain coupling state of patients with anxiety disorders, the data of 360 established 5-second time windows are first analyzed, and the mean value of the γ wave energy in the 30-40Hz frequency band in each window is extracted as the first motion trajectory data, and the R wave amplitude is extracted as the second motion trajectory data. By calculating the difference value of the γ wave energy in adjacent windows, when the absolute value of the difference exceeds 100μV, the heart-brain coupling state is detected. 2 The amplitude turning point is determined to be the moment when the patient's mood suddenly changes. The sliding window Fourier transform is used to calculate the dominant frequency of the frequency feature. When the frequency offset of the adjacent window exceeds 5Hz, it is marked as a critical offset point, indicating a change in the EEG rhythm. After aligning the two types of feature points in time, it is found that the amplitude turning point and the frequency critical offset point appear 2 minutes before the start of the speech. At this time, the γ wave energy increases from 200μV to 400μV. 2 Jumps to 450μV 2 , while the dominant frequency shifted from 32Hz to 38Hz. Based on the time matching relationship between these two types of feature points, a collaborative association rule is established, and when the time difference between the two is less than 3 seconds, it is determined to be a valid coupling event. For each valid coupling event, the product of the amplitude change rate and the frequency offset is taken as the dynamic fusion parameter, where the change rate is equal to the amplitude difference divided by the time interval, and the offset is directly taken in Hz. In the final tracking detection map, solid lines are used to connect points with fusion parameters greater than 300 to form a high coupling intensity trajectory, and dotted lines are used to connect points between 100-300 to form a medium intensity trajectory, which clearly shows the dynamic change characteristics of the patient's heart-brain coupling in the three stages of speech preparation, speech, and rest recovery.

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

[0110] To address the accuracy issue of heart-brain coupling dynamic trajectory marking, 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: Establishing 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 the amplitude mutation and the frequency jump.

[0113] In an embodiment of the present application, before establishing a mapping relationship, the timestamp of the turning point and the phase timestamp of the critical offset point need to be aligned on a unified time axis. This is achieved by comparing the time tags of the two: when the timestamp of the turning point falls within the physiological rhythm cycle corresponding to the critical offset point (such as the ECG RR interval or the EEG α rhythm cycle), it is determined to be effectively aligned. After determining alignment, 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 that reflects the coordinated changes of the heart and brain.

[0114] Step 402: Within each time window, according to the mapping relationship, the amplitude change rate corresponding to the turning 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 turning point by the time interval. This is calculated by calculating the difference between the peak amplitude of the current window and the peak amplitude of the previous window, and then dividing it by the window time length of 5 seconds. It reflects the speed of instantaneous changes in the intensity of heart-brain coupling. 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 calculated by comparing the energy center frequency of the current window in the 30-40Hz frequency band with the patient's resting state reference frequency of 35Hz. It reflects the degree of EEG rhythm deviation. The dynamic coupling parameter vector is a multidimensional feature vector formed by the combination of the amplitude change rate and the frequency offset. It characterizes the strength and pattern of heart-brain coupling in a specific time window.

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

[0117] Step 403: 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 time sequence to form a heart-brain resonance dynamic trajectory marker.

[0118] In step 403, the joint distribution density refers to the degree of aggregation of the dynamic coupling parameter vectors of 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, namely the nth window and the n+1th window. There is a preset overlap area between the two windows (typically 30%-50%), and a smooth transition of the parameter vectors is achieved through the overlap area. The heart-brain resonance dynamic trajectory mark refers to a continuous trajectory line formed by connecting the dynamic coupling features of multiple time windows in chronological order. This mark reflects the continuous changes in the coupling strength and frequency characteristics in the time dimension through the depth of color and the thickness of the line.

[0119] In this embodiment of the present application, the cosine similarity of feature vectors in 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. The specific process is to first calculate the cosine similarity between the dynamic coupling parameter vectors of N consecutive windows and establish a similarity matrix; then, linear interpolation and smoothing are performed on adjacent window vectors with 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, where the width of the connecting line is set as a function of similarity, with the higher the similarity, the wider the line. For example, when monitoring the heart-brain coupling in an anxious state: 5 seconds is selected as the time window length, with an overlap of 2 seconds. Window 1 (0-5 seconds) detects the coupling parameter vector A of the heart rate acceleration turning point (amplitude change rate +15%) and the γ brain wave frequency shift critical point (frequency offset +8Hz), and window 2 (3-8 seconds) detects the coupling parameter vector B of the heart rate fluctuation turning point (amplitude change rate ±5%) and the β brain wave frequency shift critical point (frequency offset +4Hz). The cosine similarity of A and B is calculated to be 0.82 (>threshold 0.8) during the overlapping period (3-5 seconds), and the trajectories of A and B are linearly interpolated and connected.

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

[0121] In step 404, the spatial distribution form refers to the geometric distribution characteristics of the dynamic trajectory markers in the three-dimensional time-frequency energy space. This is obtained through the following three-dimensional parameter mapping: time axis: the central time point of each window; amplitude axis: the normalized value of the turning point change rate; frequency axis: the frequency shift of the critical offset point. The spatial distribution form is formed by Delaunay triangulation of the projection points of the dynamic trajectory markers in this three-dimensional space, and the resulting surface mesh is the spatial distribution form.

[0122] In an embodiment of the present application, the continuous trajectory markers are projected into a time-frequency coordinate system, the characteristic vectors of adjacent time points are connected by curves, the trajectory thickness is set according to the vector modulus, and the vertical position is determined according to the frequency value to form a three-dimensional dynamic trajectory network.

[0123] Here's a specific example:

[0124] In the process of monitoring the heart-brain coupling state of patients with anxiety disorders, the data of 360 consecutive 5-second time windows were first analyzed. When the average energy of the γ wave in the 150th window was detected to be 200μV, 2 The 151st window suddenly increases to 450μV 2 When the amplitude change rate is calculated to be (450-200) / 5=50μV 2 / second, exceeds the threshold 30μV 2 / second is determined as the amplitude turning point. At the same time, the dominant frequency is observed to shift from 32Hz to 38Hz in window 152. The frequency shift of 6Hz exceeds the 5Hz threshold and is marked as a critical shift point. Since the time difference between the turning point and the critical shift point is 2 seconds, which is less than the 3-second threshold, a valid mapping relationship is established. The amplitude change rate corresponding to this turning point is 50μV 2 / second was parameterized with a frequency offset of 6Hz corresponding to the critical offset point. The eigenvector value for this window was calculated using the formula dynamic coupling parameter = rate of change × offset, resulting in a value of 300. The eigenvector values ​​for the next five consecutive windows were 320, 350, 380, 400, and 420, respectively. The cosine similarity between adjacent vectors was greater than 0.9, indicating high continuity. These vectors were concatenated chronologically to form a 30-second high-coupling intensity trajectory segment. The window corresponding to vector value 400 coincided with the onset of speech, at which point the trajectory width increased to three times the baseline value to highlight the critical node. In the resulting tracking detection map, this trajectory segment appears 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 captures the enhanced heart-brain coupling caused by speech stress. The synergistic pattern of gamma wave energy and heart rate changes is visualized as a simultaneous increase in trajectory density and frequency band position, providing a visual basis for physicians to assess anxiety levels.

[0125] In the embodiment of the present application, the method achieves refined labeling of the dynamic trajectory of heart-brain coupling by establishing an accurate feature point mapping relationship and a parametric fusion mechanism. The generated map can clearly distinguish the differences in coupling patterns under different psychological states, providing a more reliable and objective basis for clinical evaluation.

[0126] In order to solve the problem of insufficient accuracy in identifying characteristic components in mixed biosignals, in some embodiments, step 102: identifying the electrocardiogram oscillation component and the brainwave rhythm fluctuation component from the mixed oscillation waveform features includes:

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

[0128] In step 501, the candidate components refer to sub-band signals 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 an embodiment of the present application, wavelet packet decomposition is used to divide the mixed oscillation waveform into multiple sub-bands according to the characteristic frequency band of the physiological signal, each sub-band covers a specific frequency range, and a set of candidate components containing complete oscillation characteristics is formed.

[0130] Step 502: Calculate a first correlation coefficient between each candidate component and a preset electrocardiogram feature template, and a second correlation coefficient between each candidate component and an electroencephalogram 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 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 an embodiment of the present application, the Pearson correlation coefficient between the time domain waveform of each candidate component and the preset electrocardiogram template (including a typical PQRST waveform) is calculated as the first correlation coefficient, and the correlation coefficient between the component and the preset EEG rhythm template (including α / β / γ rhythm features) is calculated as the second correlation coefficient.

[0133] Step 503: When the first correlation coefficient is greater than the second correlation coefficient, the corresponding candidate component is determined as a cardiac wave candidate component; when the second correlation coefficient is greater than the first correlation coefficient, the corresponding candidate component is determined as a brain wave candidate component.

[0134] In step 503, the candidate components of the electrocardiogram (ECG) wave refer to sub-band signals whose waveform characteristics are closer to those of the ECG signal. The candidate components of the electroencephalogram (EBG) wave refer to sub-band signals whose characteristics are closer to those of the EEG signal.

[0135] In the embodiment of the present application, the first and second correlation coefficients of each candidate component are compared. When the first correlation coefficient is greater than the second correlation coefficient and exceeds 0.7, it is classified as a candidate component for the electrocardiogram. When the second correlation coefficient is dominant and exceeds 0.7, it is classified as a candidate component for the brain wave.

[0136] Step 504: Determine the candidate cardiac wave components that meet the preset cardiac wave frequency constraint as cardiac wave oscillation components, and determine the candidate brain wave components that meet the preset brain wave rhythm constraint as brain wave rhythm fluctuation components.

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

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

[0139] Here's a specific example:

[0140] In monitoring the heart-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 0.15, and the correlation coefficient with the EEG gamma-wave template reached 0.88. Because 0.88 was greater than 0.15 and exceeded the threshold of 0.7, this component was preliminarily classified as a candidate EEG component. Further analysis revealed that the dominant frequency of this component was 35Hz, falling within the characteristic EEG gamma-wave frequency range of 30-50Hz, ultimately confirming it as a EEG rhythmic fluctuation component. Furthermore, 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 matched the characteristic ECG range of 0.5-40Hz, confirming it as a cardiac oscillation component. During the patient's tense speech period, the RR interval of this ECG component shortened to 0.65 seconds, and the energy of the EEG γ 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, Sxx(f) and Syy(f) are the autospectral densities. The calculated value is 0.87, which is higher than that of other frequency bands.

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

[0142] In order to solve the problem of insufficient accuracy in identifying the heart-brain coupling resonance interval, in some embodiments, step 103: determining the resonance correlation interval based on the electrocardiogram oscillation component and the electrocardiogram rhythm fluctuation component includes:

[0143] Step 601: Calculate the synchronization index of the electrocardiogram oscillation component and the brainwave rhythm fluctuation component in the time-frequency domain, where the synchronization index includes: a phase locking value and an energy coupling coefficient.

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

[0145] In an embodiment of the present application, the instantaneous phase of the ECG oscillation component and the EEG rhythm component is extracted by Hilbert transform, the inverse of the phase difference standard deviation within the sliding time window is calculated as the phase locking value, and the correlation coefficient of the time-frequency energy matrix of the two signals is calculated as the energy coupling coefficient.

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

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

[0148] In an embodiment of the present application, the time-frequency plane is divided into several units, and each unit is simultaneously checked to see whether the phase locking value and the energy coupling coefficient reach their respective thresholds, and the units that meet both indicators are marked as candidate resonance intervals.

[0149] Step 603: Merge candidate resonance intervals that are continuous in time and have overlapping frequency bands into resonance-related intervals.

[0150] In an embodiment of the present application, a morphological dilation algorithm is used to process the candidate resonance interval signature map, merge candidate intervals that are adjacent in time and overlap in frequency, remove isolated small blocks, and retain coherent areas that meet the minimum duration and bandwidth requirements as the final resonance association intervals.

[0151] Here's a specific example:

[0152] To monitor the heart-brain coupling of patients with anxiety disorders, a time-frequency analysis of the separated ECG R-wave and EEG gamma-wave components was first performed. The Hilbert transform was used to calculate the phase difference between the two signals in the 30-40 Hz frequency band. When the patient was preparing for a speech, the inverse standard deviation of the phase difference detected in the 32-38 Hz frequency band reached 0.92 for 25 seconds, exceeding the phase lock threshold of 0.85. The calculation formula is phase lock value = 1 / σ(Δφ), where σ(Δφ) represents the standard deviation of the phase difference. 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), resulting in a value of 0.89, exceeding the energy coupling threshold of 0.8, where cov represents the covariance and σ represents the standard deviation. The time-frequency region where both metrics met the criteria was marked as a candidate resonance interval. This interval contained three consecutive candidate blocks: 32-35 Hz in the first 5 seconds, 34-37 Hz in the middle 10 seconds, and 35-38 Hz in the last 10 seconds. Using a morphological dilation algorithm, these three overlapping and temporally adjacent blocks were merged, ultimately forming a resonance-related interval in the 32-38 Hz frequency range that lasted 25 seconds.

[0153] In the embodiment of the present application, the method achieves precise locking of the heart-brain resonance characteristic interval through dual-index collaborative analysis and spatiotemporal continuity verification, provides a reliable time-frequency positioning basis for dynamic coupling tracking, and improves the accuracy of physiological state assessment.

[0154] In order to solve the problem of incomplete signal purification in the resonance interval, in some embodiments, step 104: performing interference elimination processing on the composite oscillation waveform in the resonance-related interval of the hybrid oscillation waveform feature includes:

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

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

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

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

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

[0160] In the embodiment of the present application, a bandpass filter group 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 technology is adopted to perform real-time cancellation processing using a reference model of the interference component.

[0161] Step 703: reconstructing the waveform of the processed resonance component to obtain a waveform reconstruction result, wherein the waveform reconstruction result is a 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 the embodiment of the present application, the enhanced resonance component and the background signal that has undergone noise cancellation processing are resynthesized according to the original time-frequency relationship, thereby retaining the effective coupling characteristics while eliminating the interference components.

[0164] Here's a specific example:

[0165] In monitoring the heart-brain coupling of patients with anxiety disorders, empirical mode decomposition (EMD) was first used to decompose the complex oscillating waveform within the identified 30-40 Hz resonant correlation interval into five eigenmode components. The third component, with an energy concentration of 85% at 35 Hz and a calculated phase lock value of 0.91 with the ECG R wave, was identified as a significant resonant component. Phase lock value = 1 / σ(Δφ), where σ(Δφ) represents the standard deviation of the phase difference. The first component, exhibiting periodic fluctuations with constant amplitude at 50 Hz and an energy contribution of 12%, was identified as a power frequency interference component. A Chebyshev filter with a center frequency of 35 Hz and a bandwidth of 5 Hz was then constructed to enhance the resonant component, improving the signal-to-noise ratio (SNR) of the filtered component to 28 dB. Adaptive noise cancellation was also employed, using a 50 Hz sine wave as a reference signal. The filter coefficients were iteratively updated using a least mean square algorithm, ultimately suppressing the power frequency interference to less than 5% of its original energy. The processed resonance component and the remaining purified background components are reconstructed according to the original time relationship to obtain a pure signal with a signal-to-noise ratio of 42dB.

[0166] In the embodiment of the present application, the method achieves effective purification of signals in the resonance range 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 structural diagram of a dynamic synchronous tracking detection system based on biowave resonance provided in an embodiment of the present application is shown as follows: Figure 2As shown, the system includes:

[0168] The acquisition module 21 is used to acquire the mixed oscillation waveform characteristics formed by coupling the electrocardiogram signal and the brain wave signal.

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

[0170] The determination module 23 is configured to determine a resonance correlation interval according to the electrocardiogram oscillation component and the brainwave rhythm fluctuation component.

[0171] The elimination module 24 is configured to perform interference elimination processing on the composite oscillation waveform within the resonance correlation interval of the hybrid oscillation waveform feature.

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

[0173] Figure 2 The dynamic synchronous tracking detection system based on bio-wave resonance can be performed Figure 1 The implementation principle and technical effects of the dynamic synchronous tracking detection method based on biowave resonance described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the dynamic synchronous tracking detection system based on biowave resonance in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0174] In one possible design, Figure 2 The embodiment shown is a dynamic synchronous tracking detection system based on bio-wave resonance that 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 called and executed by the processing component 32 .

[0176] The processing component 32 is used to perform the above Figure 1 The embodiment provides a dynamic synchronous tracking detection method based on biowave 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 method. Of course, the processing component may also 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 method.

[0178] The storage component 31 is configured to store various types of data to support operations in the terminal. The storage component can be implemented by any type of volatile or non-volatile memory 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 memory, flash memory, magnetic disk, or optical disk.

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

[0180] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0181] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

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

[0183] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a dynamic synchronous tracking detection method based on biowave resonance.

[0184] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned 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, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0186] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A dynamic synchronous tracking detection method based on biowave resonance, characterized in that: include: Acquire the mixed oscillation waveform characteristics formed by coupling the electrocardiogram signal and the brain wave signal; identifying a cardiac wave oscillation component and a brain wave rhythm fluctuation component from the mixed oscillation waveform characteristics; determining a resonance correlation interval according to the electrocardiogram oscillation component and the brainwave rhythm fluctuation component; performing interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform feature; Based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in the continuous time series, a tracking detection map representing the heart-brain coupling resonance is generated.

2. The method according to claim 1, characterized in that The method generates a tracking detection spectrum representing the heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in a continuous time series, including: Constructing a time dimension distribution framework of the eliminated composite oscillation waveform in N consecutive time windows, where N is greater than or equal to 3; Determining a characteristic evolution path of the eliminated composite oscillation waveform based on the time dimension distribution framework; According to the feature evolution path, a tracking detection map is generated.

3. The method according to claim 2, characterized in that The characteristic evolution path includes a first motion trajectory of an amplitude change trend within N consecutive time windows and a second motion trajectory of a frequency drift trend within N consecutive time windows; According to the feature evolution path, a tracking detection map is generated, including: Combining and encoding the first motion trajectory and the second motion trajectory to form time-coded data; constructing, based on the time-coded data, a collaborative association rule between a turning point in the first motion trajectory and a critical offset point in the second motion trajectory; According to the collaborative association rule, the turning point and the critical offset point are dynamically parameterized and fused to generate a tracking detection map.

4. The method according to claim 3, characterized in that 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 detection map includes: Establishing a mapping relationship between the turning point and the critical offset point according to the collaborative association rule; In each time window, according to the mapping relationship, the amplitude change rate corresponding to the turning 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 time order to form a heart-brain resonance dynamic trajectory marker; A tracking detection map is generated according to the spatial distribution morphology of the heart-brain resonance dynamic trajectory markers in the time-frequency domain.

5. The method according to claim 1, characterized in that The step of identifying the electrocardiogram oscillation component and the brainwave rhythm fluctuation component from the mixed oscillation waveform characteristics includes: Dividing the mixed oscillation waveform characteristics into a plurality of candidate components according to a preset frequency band distribution; Calculating a first correlation coefficient between each candidate component and a preset electrocardiogram feature template, and a second correlation coefficient between each candidate component and an electroencephalogram rhythm feature template; When the first correlation coefficient is greater than the second correlation coefficient, the corresponding candidate component is determined as a cardiac wave candidate component; when the second correlation coefficient is greater than the first correlation coefficient, the corresponding candidate component is determined as a brain wave candidate component; The candidate cardiac wave components that meet the preset cardiac wave frequency constraint conditions are determined as cardiac wave oscillation components, and the candidate brain wave components that meet the preset brain wave rhythm constraint conditions are determined as brain wave rhythm fluctuation components.

6. The method according to claim 1, characterized in that The determining of the resonance correlation interval according to the electrocardiogram oscillation component and the brainwave rhythm fluctuation component includes: Calculating synchronization indicators between the electrocardiogram oscillation component and the brainwave rhythm fluctuation component in the time-frequency domain, the synchronization indicators including: phase locking value and energy coupling coefficient; The time-frequency region where the synchronization index exceeds the threshold is marked as a candidate resonance interval; The candidate resonance intervals that are continuous in time and have overlapping frequency bands are merged into resonance correlation intervals.

7. The method according to claim 1, characterized in that The performing interference elimination processing on the composite oscillation waveform within the resonance correlation interval in the hybrid oscillation waveform feature includes: Decomposing the composite oscillation waveform into a resonance component and an interference component; Attenuating the resonance component and eliminating the interference component through a dynamic suppression mechanism corresponding to the resonance correlation interval to obtain a processed resonance component; The processed resonance component is subjected to waveform reconstruction to obtain a waveform reconstruction result, wherein the waveform reconstruction result is a composite oscillation waveform after elimination.

8. A dynamic synchronous tracking detection system based on bio-wave resonance, characterized in that: include: An acquisition module is used to acquire the mixed oscillation waveform characteristics formed by coupling the electrocardiogram signal and the brain wave signal; an identification module, configured to identify the electrocardiogram oscillation component and the brainwave rhythm fluctuation component from the mixed oscillation waveform characteristics; a determination module, configured to determine a resonance correlation interval based on the electrocardiogram oscillation component and the brainwave rhythm fluctuation component; an elimination module, configured to perform interference elimination processing on the composite oscillation waveform within the resonance correlation interval of the hybrid oscillation waveform feature; The generation module is used to generate a tracking detection map representing the heart-brain coupling resonance based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in the continuous time series.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a dynamic synchronous tracking detection method based on biowave resonance as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a dynamic synchronous tracking detection method based on biowave resonance as claimed in any one of claims 1 to 7 is implemented.

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