A method and system for gastric electrical monitoring assessment

Abstract

The application discloses a gastric electrical monitoring and evaluation method, comprising the following steps: determining a monitoring original signal and a lead signal corresponding to maximum power according to a power spectrum of a gastric electrical original signal; performing filtering processing to obtain a pretreated first signal and a pretreated second signal; determining amplitude phase information and artifact information according to the pretreated first signal and the pretreated second signal; determining a target analytical phase and a target gastric electrical signal according to the amplitude phase information, the pretreated second signal and the artifact information; determining a signal time-frequency domain index, a slow wave propagation direction and a gastric electrical slow wave phase coupling degree according to the target analytical phase and the target gastric electrical signal; and determining a gastrointestinal motility evaluation result according to the signal time-frequency domain index, the slow wave propagation direction and the gastric electrical slow wave phase coupling degree. The application further provides a gastric electrical monitoring and evaluation system. The application monitors from a time-frequency domain feature of a signal and a time-space feature of a stomach, obtains monitoring data for gastric evaluation, and improves the accuracy of gastric state evaluation.

Description

Technical Field

[0001] This invention belongs to the field of gastric electrical monitoring technology, specifically relating to a gastric electrical monitoring and assessment method and system. Background Technology

[0002] Gastric peristalsis and muscle contraction are fundamental to the propagation of gastric electrical activity. When slow waves pass through, the adjacent smooth muscle cell network depolarizes. If this depolarization is combined with humoral, mechanical, and extrinsic neural input, exceeding a threshold, contractions occur to produce rhythmic peristaltic movements. Gastric motility is primarily coordinated by slow waves generated and propagated by Cajal interstitial cells (ICCs). Disorders of slow wave rhythms are commonly associated with gastric motility disorders, including gastroparesis, unexplained nausea and vomiting, and functional dyspepsia. Patchy ICC loss may be one contributing factor. The pathophysiological significance of gastric rhythm disorders remains uncertain, and their role in clinical investigation and treatment is still unclear. A key issue is the lack of accurate assessment methods.

[0003] The limitations of traditional electrogastrography (EGG) are now becoming clearer, including the discovery that anatomical variability of the stomach and slow wave frequencies themselves are not reliable discriminators of gastric rhythm disorders. Instead, the focus should be shifted to altered spatial patterns. High-resolution electrical signal acquisition improves the accuracy of assessing gastric rhythm disorders. By analyzing high-resolution gastric electrical signals measured on the body surface, the physiological parameters and spatial propagation characteristics of the gastric electrical signals can be evaluated, providing new auxiliary means for the assessment of some gastric diseases.

[0004] While EGG electrode placement typically attempts to cover the gastric antrum, the location of the human stomach and the geometry of the torso are highly diverse. This variability means that a typical EGG structure usually does not directly cover the stomach. The amplitude of the bioelectrical signal on the gastric surface is relatively weak (interpeak reference value is approximately 50-250 μV). Even if the electrode directly covers the stomach, the amplitude of the EGG potential decreases exponentially with increasing distance from the source. This means that even a relatively modest deviation of the electrode position from the stomach can lead to misleading data and inaccurate assessment results. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a gastric electrical activity monitoring and assessment method and system. The technical problem to be solved by this invention is achieved through the following technical solution:

[0006] A first aspect of this invention provides a method for monitoring and evaluating gastric electrical activity, comprising the following steps:

[0007] The monitoring raw signal and the lead signal corresponding to the maximum power are determined based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes.

[0008] The lead signal and the original monitoring signal are filtered to obtain a pre-processed first signal and a pre-processed second signal;

[0009] The first analytical phase, the second amplitude phase information, the first phase artifact information, and the second artifact information are determined based on the preprocessed first signal and the preprocessed second signal.

[0010] The first target analytical phase, the second target analytical phase, and the target gastric electrical signal are determined based on the first analytical phase, the first phase artifact information, the second amplitude phase information, the preprocessed second signal, and the second artifact information.

[0011] Based on the first target analytical phase, the second target analytical phase, and the target gastric electrical signal, the time-frequency domain index of the gastric electrical signal, the slow wave propagation direction, and the gastric electrical slow wave phase coupling degree are determined.

[0012] The gastrointestinal motility assessment results are determined based on the time-frequency domain indices of the gastric electrical signal, the slow wave propagation direction, and the phase coupling degree of the gastric electrical slow wave.

[0013] In one embodiment of the present invention, determining the monitoring raw signal and the lead signal corresponding to the maximum power based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes includes:

[0014] The surface projection power thermogram is determined based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes.

[0015] The raw monitoring signals of multiple target acquisition electrodes covering the stomach area were determined based on the surface projection power thermogram.

[0016] The lead signal corresponding to the maximum power is determined based on multiple power spectra.

[0017] In one embodiment of the present invention, determining the first analytical phase, the second amplitude phase information, the first phase artifact information, and the second artifact information based on the preprocessed first signal and the preprocessed second signal includes:

[0018] The first analytical phase is determined based on the preprocessed first signal;

[0019] The second amplitude phase information is determined based on the preprocessed second signal;

[0020] The first phase artifact information is determined based on the first analytical phase;

[0021] The second artifact information is determined based on the second amplitude phase information.

[0022] In one embodiment of the present invention, the second amplitude phase information includes: a second analytical phase and a second instantaneous amplitude;

[0023] The step of determining the second phase artifact information based on the second analytical phase includes:

[0024] The second phase artifact information is determined based on the second analytical phase;

[0025] The second amplitude artifact information is determined based on the second instantaneous amplitude.

[0026] In one embodiment of the present invention, determining the second phase artifact information based on the second analyzed phase includes:

[0027] Obtain multiple edges where the phase derivative change of the second analytical phase is greater than -1, and calculate the period length between each edge;

[0028] The period threshold is determined based on the mean and standard deviation of the multiple period lengths;

[0029] The first time points corresponding to the period lengths greater than the period threshold and less than the period threshold are determined based on the period threshold.

[0030] Obtain the second time point corresponding to the non-monotonic increasing period length among the multiple period lengths;

[0031] The first time point and the second time point are extracted as the second phase artifact information.

[0032] In one embodiment of the present invention, determining the second amplitude artifact information based on the second instantaneous amplitude includes:

[0033] Obtain multiple third time points where the second instantaneous amplitude is greater than the instantaneous threshold;

[0034] Identify the positions of multiple peaks between multiple third time points;

[0035] Obtain the time interval between adjacent peak positions;

[0036] The time interval is extracted as the second amplitude artifact information.

[0037] In one embodiment of the present invention, determining the first target analytical phase, the second target analytical phase, and the target gastric electrical signal based on the first analytical phase, the first phase artifact information, the second amplitude phase information, the preprocessed second signal, and the second artifact information includes:

[0038] Remove the phase corresponding to the first phase artifact information from the first resolved phase to obtain the first target resolved phase;

[0039] Remove the phase corresponding to the second phase artifact information from the second analytical phase to obtain the second target analytical phase;

[0040] The target gastric electrical signal is obtained by removing the amplitude corresponding to the second amplitude artifact information from the preprocessed second signal.

[0041] In one embodiment of the present invention, determining the time-frequency domain index of the gastric electrical signal, the slow wave propagation direction, and the gastric electrical slow wave phase coupling degree based on the first target analytical phase, the second target analytical phase, and the target gastric electrical signal includes:

[0042] The time-frequency domain index of the gastric electrical signal is determined based on the first target analytical phase and the target gastric electrical signal;

[0043] The slow wave propagation direction and the gastric electrical slow wave phase coupling degree are determined based on the second target analytical phase.

[0044] A second aspect of the present invention provides a gastric electrical monitoring and evaluation system, comprising: a data acquisition strap, multiple preset data acquisition electrodes, a data acquisition module, and a monitoring and evaluation module;

[0045] The plurality of preset acquisition electrodes are disposed on the acquisition strap;

[0046] The acquisition module is electrically connected to the plurality of preset acquisition electrodes;

[0047] The data acquisition module is electrically connected to the monitoring and evaluation module;

[0048] The monitoring and evaluation module is used to perform a gastric electrical monitoring and evaluation method as described in the first aspect of the present invention.

[0049] The beneficial effects of this invention are:

[0050] This invention uses the power spectrum of raw gastric electrical signals acquired by multiple preset acquisition electrodes to accurately locate the stomach, thereby obtaining a more accurate raw gastric electrical signal for monitoring gastric activity. This invention monitors the time-frequency domain characteristics of the signal and the spatiotemporal characteristics of the stomach, obtaining monitoring data for stomach assessment, thus improving the accuracy of stomach state evaluation.

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the structure of a gastric electrical monitoring and assessment system provided in an embodiment of the present invention;

[0053] Figure 2 A surface projection power thermogram of raw gastric electrical signals collected by multiple preset acquisition electrodes in a gastric electrical monitoring and assessment method provided in this embodiment of the invention;

[0054] Figure 3A schematic flowchart of a gastric electrical monitoring and assessment method provided in an embodiment of the present invention;

[0055] Figure 4 A vector diagram showing the slow wave propagation direction provided in an embodiment of the present invention. Detailed Implementation

[0056] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0057] Example 1

[0058] like Figure 1 As shown, this embodiment provides a gastric electrical monitoring and evaluation system, including: a data acquisition strap, multiple preset data acquisition electrodes, a data acquisition module, and a monitoring and evaluation module. The multiple preset data acquisition electrodes are mounted on the data acquisition strap. The data acquisition module is electrically connected to the multiple preset data acquisition electrodes. The data acquisition module is electrically connected to the monitoring and evaluation module.

[0059] Preferably, the preset acquisition electrodes are a 10*10 high-resolution electrode array, with a total of 100 electrodes arranged at equal intervals on the strap to form a square array. The spacing between the preset acquisition electrodes can be 2cm, but of course, the spacing can also be set as needed. During monitoring, the array of preset acquisition electrodes is located above the navel and below the chest, with the reference electrode and the ground electrode placed on the left and right sides of the body, respectively.

[0060] like Figure 3 As shown, the monitoring and assessment module is used to perform a gastric electrical monitoring and assessment method, including the following steps:

[0061] Step 10: Determine the monitoring raw signal and the lead signal corresponding to the maximum power based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes. The raw signal is a time-domain signal. Step 10 includes steps 11-13:

[0062] Step 11: Determine the surface projection power heatmap based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes. In this step, the power spectrum of the raw gastric electrical signals acquired by all preset acquisition electrodes is calculated, and the maximum power value within the range of 0.033-0.067Hz is selected. A third-order spline interpolation with an upsampling rate of 10Hz is performed between the maximum power value corresponding to each preset acquisition electrode and the maximum power value of the adjacent electrode. Then, visualization is performed to generate the surface projection power heatmap, as shown below. Figure 2 As shown.

[0063] In this step, the power spectrum is calculated using a multi-window spectrum estimation method to obtain an approximate power spectrum. The basic idea of ​​the multi-window spectrum estimation method is to obtain a family of data window functions by minimizing frequency leakage outside half the bandwidth, based on the Rayleigh-Ritz minimization problem. This family of window functions replaces the single window function. This family of window functions is mutually orthogonal, and each window function samples the signal differently. Information lost by one window function can be recovered by another window function, thus maintaining the offset at an acceptable level. Using a family of orthogonal window functions to process random signals can reduce spectral leakage caused by the limited data length.

[0064] When using a computer to perform direct signal processing, it is impossible to measure and calculate infinitely long signals. Therefore, this embodiment analyzes a finite time segment of the signal. Specifically, a time segment is extracted from the signal, and then this segment is periodically extended to obtain a virtual infinitely long signal. Finally, a Fourier transform is performed on this signal. Truncation of an infinitely long signal causes spectral distortion. The energy originally concentrated at f(0) is dispersed into two wider frequency bands. To reduce this spectral energy leakage, this embodiment uses different truncation functions, i.e., window functions, to truncate the signal.

[0065] The specific calculation steps are as follows:

[0066] (1) Parameter settings

[0067] Based on the length N of the original gastric electrical signal and the sampling rate f s Determine the frequency resolution f that satisfies the Nyquist theorem. n .

[0068] (2) Generate data window

[0069] A Slepian sequence is a data window used in multi-window spectral estimation methods. It is a set of orthogonal functions, also known as a discrete spherical sequence. Let represent the k-th raw gastric electrical signal sequence sample, abbreviated as This sequence satisfies the following two conditions.

[0070] 1) Each window function has a unit energy, i.e.

[0071]

[0072] 2) These window functions are mutually orthogonal, that is...

[0073]

[0074] To satisfy the above two conditions, the Slepian sequence can be generated using the following formula:

[0075]

[0076] Where n represents the starting point of the data window in the original gastric electrical signal, and k represents the number of data windows, which is determined by the bandwidth parameter p of the data windows.

[0077] k = |p+1|

[0078]

[0079] (3) Calculation of characteristic coefficients

[0080] The raw gastric electrical signal is multiplied by the Slepian sequence to obtain a windowed data sequence X(t), and the characteristic coefficients are obtained by performing the following discrete Fourier transform:

[0081]

[0082] (4) Adaptive weighting

[0083] For the characteristic coefficient y k (f) Perform adaptive average weighting, using the ratio of the characteristic coefficients of each characteristic coefficient to b. k (f) can be used as weighting coefficients to obtain the power spectrum estimate p. x (f).

[0084]

[0085]

[0086] Step 12: Determine the raw monitoring signals of multiple target acquisition electrodes covering the stomach area based on the surface projection power heatmap. Locations with power exceeding a threshold in the heatmap are displayed in red; these red locations correspond to the stomach area, and the preset acquisition electrodes at these red locations are the target acquisition electrodes. Acquiring the signals collected by these target acquisition electrodes is equivalent to acquiring the raw monitoring signals. Figure 2 As shown.

[0087] Preferably, signals from 15-25 channels (target acquisition electrodes) covering the stomach area are selected for analysis of gastric electrocardiogram (EGG) signals (monitoring raw signals). This process requires manual selection by the operator. Since the amplitude of the EGG signal is affected by the distance between the stomach and the skin surface, and EGG signals acquired while seated typically have lower amplitudes, to obtain a higher signal-to-noise ratio, the user can recline at a 45° angle on a reclining chair. This makes it easier to access the gastric pacing area, and the user should avoid any voluntary movement. Before testing, skin abrasion, alcohol disinfection, and removal of excess hair are necessary. Recording can begin when the impedance is less than 5kΩ.

[0088] Step 13: Determine the lead signal corresponding to the maximum power based on multiple power spectra.

[0089] Under normal circumstances, high-quality electrogastrography (EGG) recordings exhibit unique spectral characteristics, with similar peak frequencies at most recording locations. The lead signal with the highest power corresponding to the peak frequency is selected for subsequent analysis.

[0090] Step 20: Filter the lead signal and the original monitoring signal to obtain a pre-processed first signal and a pre-processed second signal. The phase characteristics of the gastric electrical signal reflect the slow wave rhythm and propagation pattern generated by the pacemaker cells in the gastrointestinal tract. To ensure no phase distortion, in this embodiment, a zero-phase filter is used for bidirectional filtering in the gastric electrical signal processing. That is, the signal is first forward filtered, and then the forward-filtered signal is reverse-filtered. This not only effectively removes high-frequency noise and DC offset, but also does not affect the phase characteristics of the gastric electrical signal, maintaining signal integrity. A third-order FIR filter and a zero-phase filter are used for bidirectional filtering. The filtering frequency band is generally set to 0.033-0.067Hz, and the transition bandwidth is 0.15.

[0091] Step 30: Determine the first analytical phase, the second amplitude phase information, the first phase artifact information, and the second artifact information based on the preprocessed first signal and the preprocessed second signal. Step 30 includes steps 31-34:

[0092] Step 31: Determine the first analytical phase based on the preprocessed first signal.

[0093] Step 32: Determine the second amplitude phase information based on the preprocessed second signal. The second amplitude phase information includes: the second analytical phase and the second instantaneous amplitude.

[0094] To better analyze the amplitude and phase changes of the gastric electrocardiogram (ECG) signal, the Hilbert transform is used to convert the one-dimensional ECG signal into an analytic signal in the two-dimensional complex plane. Specifically, the filtered ECG signal is transformed to the frequency domain using a Discrete Fourier Transform (DFT), multiplied by the Hilbert filter coefficients, and then transformed back to the time domain using an Inverse Fourier Transform (IFT), yielding an analytic representation of the ECG signal. The magnitude and phase of the analytic signal represent the amplitude and phase of the signal, respectively. Therefore, the envelope (instantaneous amplitude) and analytic phase of the analytic signal can be calculated, and the instantaneous frequency can be determined. The Hilbert transform is widely used in signal processing, and its physical meaning is very clear: it delays the phase of all frequency components of the signal by 90 degrees. The Hilbert transform of the preprocessed ECG signal is calculated using the following formula:

[0095]

[0096] The analytical process of the Hilbert transform is as follows:

[0097]

[0098] in, This represents an analytic signal; the process has the following characteristics: the power spectra of the real and imaginary parts are the same, and the autocorrelation functions are the same; the cross-correlation function of the real and imaginary parts is an odd function; the power spectrum of the analytic signal only has a positive frequency band, and the intensity is 4 times that of the original (the amplitude is 2 times that of the original).

[0099] The formula for calculating the analytical phase is as follows:

[0100]

[0101] The formula for calculating instantaneous amplitude is as follows:

[0102]

[0103] The formula for calculating instantaneous frequency is as follows:

[0104]

[0105] Step 33: Determine the first phase artifact information based on the first analytical phase.

[0106] Step 34: Determine the second artifact information based on the second amplitude phase information. Specifically, artifact identification can be based on two parts: the non-monotonic phase change introduced by nonlinear interference and the large amplitude caused by body motion. Step 34 includes steps 341-342:

[0107] Step 341: Determine the second phase artifact information based on the second analytical phase. Nonlinear interference introduces a non-monotonic change in the phase:

[0108] Because the physiological processes such as contraction and relaxation of gastrointestinal smooth muscle are relatively stable, the amplitude and frequency of gastric electrical signals also remain relatively stable, and therefore can be approximated as linear. The Hilbert transform is essentially a linear operator, and its analytic phase is calculated based on the amplitude and phase of the new function. Since linear operators satisfy the superposition principle, the analytic phase of the Hilbert transform can also be obtained by calculating the analytic phase of each frequency component separately and then summing them up to obtain the analytic phase of the entire function, thus satisfying the linear property. When the analytic phase of a signal undergoes a nonlinear change, it indicates the presence of a nonlinear component in the signal. This nonlinear component may originate from various factors, such as the nonlinear dynamic mechanism of the signal source, the influence of noise or interference, etc. Step 341 includes steps 3411-3415:

[0109] Step 3411: Obtain multiple edges where the phase derivative change of the second analytical phase is greater than -1, and calculate the period length between each edge. Calculate the distribution of period lengths based on the second analytical phase to determine the period threshold. Obtain multiple edges in the signal where the derivative change of the second analytical phase is greater than -1, and calculate the time difference between each edge, i.e., the period length.

[0110] Step 3412: Determine the period threshold based on the mean and standard deviation of multiple period lengths. Calculate the mean and standard deviation of all period lengths, and set the period threshold to the mean minus or plus three times the standard deviation.

[0111] Step 3413: Determine the first time point corresponding to the period lengths greater than and less than the period threshold based on the period threshold.

[0112] Step 3414: Obtain the second time point corresponding to the non-monotonic increasing period length among multiple period lengths.

[0113] Based on phase time series detection, artifacts are identified, periods that are too short or too long (period lengths greater than or less than the period threshold) and non-monotonic increasing periods in the signal are marked with red areas in the graph, and the time point information corresponding to the artifacts is extracted.

[0114] Step 3415: Extract the first time point and the second time point as the second phase artifact information.

[0115] The process of determining the first phase artifact information in step 33 is the same as that in step 341.

[0116] Step 342: Determine the second amplitude artifact information based on the second instantaneous amplitude. Large amplitude caused by body movement: Under normal circumstances, the amplitude of the slow wave of gastric electrical activity is about 50-250uV. When the user moves or accidentally touches the electrode wire, causing the electrode wire to be disturbed, there will be a large change in the amplitude of the gastric electrical signal. Usually, single periodic waves with an amplitude greater than 500uV are identified and removed. The specific implementation steps of step 342 include steps 3421-3424: Identify the position of each peak through the peak detection algorithm, find the slow wave period of the gastric electrical activity in which it is located, that is, the interval between the previous peak and the next peak, denoted as I(t), and extract the part corresponding to I(t).

[0117] Step 3421: Obtain multiple third time points where the second instantaneous amplitude is greater than the instantaneous threshold.

[0118] Step 3422: Identify the positions of multiple peaks between multiple third time points.

[0119] Step 3423: Obtain the time interval between adjacent peak positions.

[0120] Step 3424: Extract the time interval as the second amplitude artifact information.

[0121] Step 40: Determine the first target resolved phase, the second target resolved phase, and the target gastric electrical signal based on the first resolved phase, the first phase artifact information, the second amplitude phase information, the preprocessed second signal, and the second artifact information. Step 40 includes steps 41-43:

[0122] Step 41: Remove the phase corresponding to the first phase artifact information from the first resolved phase to obtain the first target resolved phase.

[0123] Step 42: Remove the phase corresponding to the second phase artifact information from the second analytical phase to obtain the second target analytical phase.

[0124] Step 43: Remove the amplitude corresponding to the second amplitude artifact information from the preprocessed second signal to obtain the target gastric electrical signal.

[0125] To remove artifacts, simply remove the signal information (phase and amplitude) from the phase and amplitude that corresponds to the time point indicated by the artifact information.

[0126] Step 50: Determine the time-frequency domain indices, slow wave propagation direction, and slow wave phase coupling degree of the gastric electrical signal based on the first target resolved phase, the second target resolved phase, and the target gastric electrical signal. Step 50 includes steps 51-52:

[0127] Step 51: Determine the time-frequency domain index of the gastric electrical signal based on the first target analytical phase and the target gastric electrical signal.

[0128] Analysis of time-frequency domain indices of gastric electrical signals can reflect the characteristics and patterns of the signals. Different parameters can reflect different aspects. For example, the periodicity refers to the proportion of the effective time of the gastric electrical signal within one cycle to the total time, reflecting the intensity and duration of gastric muscle contraction; the dominant frequency refers to the strongest frequency component in the gastric electrical signal, reflecting the rhythm and stability of gastric muscle contraction. When conducting a gastric dietary standard experiment, the operator can manually mark the start time of each test segment, including the end time of the pre-meal baseline and the end time of the meal. The system calculates gastric electrical-related indices according to different time periods.

[0129] Gastric electrical signals are bioelectrical phenomena generated by gastric muscle cells. Their waveform and frequency are influenced by various factors, such as nerves, hormones, food, and medications. Therefore, by analyzing gastric electrical signals in both the time and frequency domains, we can gain a general understanding of the state and function of gastric muscle contraction. Table 1 lists the reference ranges for normal values ​​of some commonly used calculation indicators for gastric electrical signals. These time-frequency domain indicators include, but are not limited to, those shown in Table 1. Each indicator can be calculated using the first target analytical phase and / or the target gastric electrical signal.

[0130]

[0131]

[0132] The average amplitude of the gastric electrical rhythm is calculated based on the amplitude of the target gastric electrical signal. The standard deviation of the gastric electrical rhythm amplitude is also calculated based on the amplitude of the target gastric electrical signal. The response area of ​​the gastric electrical waveform is calculated by integrating the waveform of the target gastric electrical signal to obtain the area of ​​each cycle, and the average area of ​​each cycle is calculated. The dominant frequency is the frequency value corresponding to the maximum power of the power spectrum of the first target analytical phase. The postprandial / preprandial power ratio is the ratio of the maximum power of the power spectrum of the signal before and after the meal time point in the target gastric electrical signal.

[0133] The spectral width is the bandwidth containing 90% of the energy centered on the dominant frequency in the power spectrum corresponding to the first target resolution phase. The power distribution percentage is the ratio of the integral of the power in the dominant frequency band to the integral of the power in the total frequency band in the power spectrum corresponding to the first target resolution phase. The normal period percentage is the ratio of the number of waves within the normal period corresponding to the normal frequency band range in the first target resolution phase to the total number of waves. The standard deviation of the period duration is the standard deviation of all individual wave periods in the first target resolution phase, and the average period duration is the average of all individual wave periods in the first target resolution phase. The period stability coefficient is the ratio of the standard deviation of the period duration to the average period duration.

[0134] Step 52: Determine the slow wave propagation direction and the gastric electrical slow wave phase coupling degree based on the second target analytical phase. Step 52 includes steps 521-522:

[0135] Step 521: Determine the slow wave propagation direction based on the second target resolving phase. Although some features in the time-frequency domain can characterize the relevant activity state of gastric slow waves, new biomarkers of gastric electrophysiological changes appear to exist in the form of gastric rhythm disorder patterns, but these biomarkers cannot be determined using previous EGG time-frequency domain indices. The second target resolving phase includes the target resolving phase of multiple channels.

[0136] Specifically, the phase gradient between adjacent channels is calculated, i.e., the vertical and horizontal phase differences between adjacent target acquisition electrodes. Based on the direction and magnitude of the phase gradient, a vector diagram of the slow wave propagation direction is drawn, such as... Figure 4 As shown.

[0137] The method for estimating the direction of slow wave propagation can be expressed by the following formula:

[0138]

[0139]

[0140]

[0141]

[0142] Among them, E i and θ i Let v and H represent the magnitude and angle of the slow wave propagation direction at the position of the i-th target acquisition electrode, respectively. i Let represent the phase gradient in the vertical direction and the phase gradient in the horizontal direction at the position of the i-th target acquisition electrode, respectively.

[0143] Step 522: Determine the phase coupling degree of the slow gastric electrical wave based on the second target analytical phase.

[0144] In addition, the degree of phase locking of slow waves in gastric electrical signals at different locations can also reflect gastric activity. Gastric electrical signals can be viewed as periodic or quasi-periodic signals composed of multiple superimposed sine waves, thus possessing distinct frequency and phase characteristics. There is a causal relationship or information transmission between signals from different channels, leading to phase consistency or amplitude modulation within certain frequency ranges or across frequencies. Phase coupling indices can quantitatively describe the degree of phase correlation between different channels of gastric electrical signals, thereby reflecting the interaction of gastrointestinal motility and the continuity and synchronicity of slow wave conduction. The continuity and synchronicity of slow wave conduction are closely related to gastric muscle contraction and gastric emptying.

[0145] Phase coupling refers to the consistency between the phases of two signals at the same or different frequencies. This embodiment uses the phase-locked value calculated based on the phase synchronization of circular statistical methods to quantify the phase coupling degree of slow-wave gastric electrical signals using the following formula:

[0146]

[0147] Where N is the length of time, and φ1(n) and φ2(n) are the target analytical phases between different channels in the second target analytical phase, respectively. The slow wave phase coupling degree of gastric electrical activity can be compared with a reference threshold. If the slow wave phase coupling degree of gastric electrical activity reaches the reference threshold, it is judged as normal; if it is greater than or less than the reference threshold, it is judged as abnormal.

[0148] Step 60: Determine the gastrointestinal motility assessment results based on the time-frequency domain indices of the gastric electrical signal, the slow wave propagation direction, and the phase coupling degree of the gastric electrical slow wave.

[0149] To assess gastric motility, this step calculates and analyzes several indicators of slow-wave gastric electrical activity (SWE). These indicators include, but are not limited to, the aforementioned time-frequency domain indicators of SWE, slow-wave propagation direction, and phase coupling of the slow-wave gastric signal. For the recorded SWE signals, these indicators are assessed for normality. If the corresponding result falls within the normal range, it is assigned a value of 1, indicating normal gastrointestinal motility; if the indicator exceeds the normal range, it is assigned a value of 0, indicating abnormal gastrointestinal motility. Then, different weighting coefficients are assigned to each indicator based on their importance; these coefficients reflect the degree of influence of the indicator on gastrointestinal motility. By multiplying each indicator by its corresponding weighting coefficient and summing the results, a comprehensive score is obtained to reflect the overall condition of gastrointestinal motility. The calculation formula for the score is as follows:

[0150]

[0151] Among them, a n x is the weighting coefficient. n The value of the indicator is 0 or 1, and E is the difference between the number of leads with anterograde propagation and the number of leads with retrograde propagation. I is the total number of leads.

[0152] Generally, under normal circumstances, the propagation direction of slow waves in gastric electrical activity should be anterograde, from the gastric body to the antrum. If retrograde propagation occurs from the antrum to the gastric body, it indicates a possible abnormality in the stomach. Based on the score, gastrointestinal motility is divided into three levels. A score between 85 and 100 indicates good gastric function with no abnormalities; a score between 70 and 85 indicates moderate gastrointestinal motility, suggesting acceptable gastric function but the possibility of minor abnormalities; a score below 70 indicates poor gastrointestinal motility, suggesting possible impaired gastric function and the potential for significant abnormalities.

[0153] This invention uses the power spectrum of raw gastric electrical signals acquired by multiple preset acquisition electrodes to accurately locate the stomach, thereby obtaining more accurate raw and lead signals for monitoring. This invention monitors the time-frequency domain characteristics of the signal and the spatiotemporal characteristics of the stomach, obtaining monitoring data for stomach assessment, thus improving the accuracy of stomach condition evaluation.

[0154] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0155] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0156] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring and evaluating gastric electrical activity, characterized in that, Includes the following steps: The monitoring raw signal and the lead signal corresponding to the maximum power are determined based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes. The lead signal and the original monitoring signal are filtered to obtain a pre-processed first signal and a pre-processed second signal; The first analytical phase and the second amplitude phase information are determined based on the preprocessed first signal and the preprocessed second signal, respectively. The second amplitude phase information includes the second analytical phase and the second instantaneous amplitude. The first phase artifact information and the second artifact information are determined based on the first analytical phase information and the second amplitude phase information, respectively, wherein the second artifact information includes the second phase artifact information and the second amplitude artifact information. The first target analytical phase is determined based on the first analytical phase and the first phase artifact information; the second target analytical phase is determined based on the second analytical phase and the second phase artifact information; and the target gastric electrical signal is determined based on the preprocessed second signal and the second amplitude artifact information. The gastric electrical signal time-frequency domain index is determined based on the first target analytical phase and the target gastric electrical signal, and the slow wave propagation direction and gastric electrical slow wave phase coupling degree are determined based on the second target analytical phase. The gastrointestinal motility assessment results are determined based on the time-frequency domain indices of the gastric electrical signal, the slow wave propagation direction, and the phase coupling degree of the gastric electrical slow wave.

2. The gastric electrical monitoring and assessment method according to claim 1, characterized in that, The step of determining the monitoring raw signal and the lead signal corresponding to the maximum power based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes includes: The surface projection power thermogram is determined based on the power spectrum of the raw gastric electrical signals acquired by multiple preset acquisition electrodes. The raw monitoring signals of multiple target acquisition electrodes covering the stomach area were determined based on the surface projection power thermogram. The lead signal corresponding to the maximum power is determined based on multiple power spectra.

3. The gastric electrical monitoring and assessment method according to claim 1, characterized in that, The second artifact information is determined based on the second amplitude and phase information, including: The second phase artifact information is determined based on the second analytical phase; The second amplitude artifact information is determined based on the second instantaneous amplitude.

4. The gastric electrical monitoring and assessment method according to claim 3, characterized in that, The step of determining the second phase artifact information based on the second resolved phase includes: Obtain multiple edges where the phase derivative change of the second analytical phase is greater than -1, and calculate the period length between each edge; The period threshold is determined based on the mean and standard deviation of the multiple period lengths; The first time points corresponding to the period lengths greater than the period threshold and less than the period threshold are determined based on the period threshold. Obtain the second time point corresponding to the non-monotonic increasing period length among the multiple period lengths; The first time point and the second time point are extracted as the second phase artifact information.

5. The gastric electrical monitoring and assessment method according to claim 3, characterized in that, The step of determining the second amplitude artifact information based on the second instantaneous amplitude includes: Obtain multiple third time points where the second instantaneous amplitude is greater than the instantaneous threshold; Identify the positions of multiple peaks between multiple third time points; Obtain the time interval between adjacent peak positions; The time interval is extracted as the second amplitude artifact information.

6. The gastric electrical monitoring and assessment method according to claim 3, characterized in that, The step of determining the first target analytical phase based on the first analytical phase and the first phase artifact information, determining the second target analytical phase based on the second analytical phase and the second phase artifact information, and determining the target gastric electrical signal based on the preprocessed second signal and the second amplitude artifact information includes: Remove the phase corresponding to the first phase artifact information from the first resolved phase to obtain the first target resolved phase; Remove the phase corresponding to the second phase artifact information from the second analytical phase to obtain the second target analytical phase; The target gastric electrical signal is obtained by removing the amplitude corresponding to the second amplitude artifact information from the preprocessed second signal.

7. A gastric electrical monitoring and assessment system, characterized in that, include: The system includes a data acquisition strap, multiple preset data acquisition electrodes, a data acquisition module, and a monitoring and evaluation module. The plurality of preset acquisition electrodes are disposed on the acquisition strap; The acquisition module is electrically connected to the plurality of preset acquisition electrodes; The data acquisition module is electrically connected to the monitoring and evaluation module; The monitoring and evaluation module is used to perform a gastric electrical monitoring and evaluation method as described in any one of claims 1-6.

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