A stomach function regulating system based on stomach rhythm and stomach-brain coupling

Abstract

The application discloses a stomach function regulating system based on stomach rhythm and stomach-brain coupling, comprising a stomach function regulating device, a client and a cloud, wherein the client is used for controlling the start and end of the stomach function regulating device, and is also used for obtaining stomach electric signals and brain electric signals collected by the stomach function regulating device for processing and analysis to obtain stomach rhythm parameters and stomach-brain coupling parameters of a current period, and then obtaining vagus nerve stimulation parameters of a next period through the stomach rhythm parameters of the current period; the stomach function regulating device is used for applying vagus nerve stimulation according to the vagus nerve stimulation parameters, and is also used for collecting the stomach electric signals and the brain electric signals; and the cloud is used for storing historical use records, wherein the historical use records include the vagus nerve stimulation parameters, the stomach rhythm parameters and the change conditions of the stomach-brain coupling parameters. The application feeds back and guides the vagus nerve stimulation parameters through the change of the stomach rhythm, and controls the stomach function regulating process by monitoring the stomach-brain coupling parameters in real time.

Description

Technical Field

[0001] This invention belongs to the field of gastric function regulation technology, specifically relating to a gastric function regulation system based on gastric rhythm and gastrobrain coupling. Background Technology

[0002] Electrogastrogram (EGG) is a non-invasive technique that records gastric electrical signals from the abdominal surface using surface electrodes. It can reflect the functional state of gastric motility to some extent and has certain clinical value. Analyzing EGG signals reveals the rhythmic information of slow gastric waves—a major regulator of gastric motility—including frequency, amplitude, and propagation direction. Slow gastric waves originate in the proximal pacemaker region and exhibit uneven amplitude and velocity characteristics as they propagate towards the pylorus. The propagation pattern and rhythmic information of slow gastric waves are key components of human digestive function. Studies have found that abnormal slow gastric waves are associated with various digestive system diseases, including gastroparesis, dyspepsia, and gastroesophageal reflux.

[0003] However, existing techniques for gastrointestinal stimulation are either invasive systems requiring operation by a professional physician, which not only carry certain risks but also have limitations. Other techniques, while non-invasive acupoint stimulation, lack evaluation and feedback based on physiological indicators that truly reflect changes in gastric function. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a gastric function regulation system based on gastric rhythm and gastrobrain coupling. The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] This invention provides a gastric function regulation system based on gastric rhythm and gastrobrain coupling, comprising a gastric function regulation device, a client, and a cloud platform, wherein...

[0006] The client is used to control the start and end of the gastric function regulation device, and is also used to acquire gastric electrical signals and electroencephalogram signals collected by the gastric function regulation device for processing and analysis to obtain gastric rhythm parameters and gastrobrain coupling parameters for the current time period. Then, the vagus nerve stimulation parameters for the next time period are obtained through the gastric rhythm parameters for the current time period, and the user's gastrobrain balance state is obtained through the gastrobrain coupling parameters for the current time period. The system usage process is terminated when the gastrobrain balance state is unbalanced.

[0007] The gastric function regulation device is used to connect an external vagus nerve stimulation electrode to the ear, apply vagus nerve stimulation to the ear according to the vagus nerve stimulation parameters to regulate gastric function, and is also used to connect an external gastric electrical signal acquisition electrode to the abdomen to acquire gastric electrical signals, and an external electroencephalogram (EEG) signal acquisition electrode to the forehead to acquire EEG signals, and upload the gastric electrical signals and the EEG signals to the client.

[0008] The cloud is used to store historical usage records, which include changes in vagus nerve stimulation parameters, gastric rhythm parameters, and gastrobrain coupling parameters during the usage process.

[0009] In one embodiment of the present invention, the gastric function regulation device includes an internal controller, a stimulation current signal generator, a physiological signal amplifier, and a power supply module, wherein,

[0010] The internal controller is used to control the physiological signal acquisition amplifier to acquire gastric electroencephalogram (GEG) signals and electroencephalogram (EEG) signals for the current time period and transmit them to the client. It is also used to receive vagus nerve stimulation parameters for the next time period from the client and send them to the stimulation current signal generator.

[0011] The stimulation current signal generator is externally connected to a vagus nerve stimulation electrode, and is used to generate a vagus nerve stimulation signal according to the vagus nerve stimulation parameters and transmit it to the vagus nerve stimulation electrode.

[0012] The physiological signal acquisition amplifier is externally connected to gastric electroencephalogram (GEG) signal acquisition electrodes and electroencephalogram (EEG) signal acquisition electrodes, which are used to acquire gastric GEG and EEG signals during the regulation process and upload them to the client.

[0013] The power module is used to provide isolated power to the internal controller, the stimulation current signal generator, and the physiological signal acquisition amplifier.

[0014] In one embodiment of the present invention, the gastric function regulation system based on gastric rhythm and gastrobrain coupling further includes a wearable gastric electrical signal acquisition belt. The wearable gastric electrical signal acquisition belt is provided with a reference electrode, a ground electrode and multiple gastric electrical signal acquisition electrodes inside. The gastric electrical signal acquisition electrodes are used to acquire gastric electrical signals. The two ends of the wearable gastric electrical signal acquisition belt are provided with Velcro or buckles to fix the wearable gastric electrical signal acquisition belt to the abdomen.

[0015] In one embodiment of the present invention, the gastric function regulation system based on gastric rhythm and gastrobrain coupling further includes a wearable soft headband, the soft headband having three electrodes embedded in the part facing the forehead, namely Fp1, Fp2 and Fpz in a 10-20 lead system, wherein Fp1 and Fp2 are EEG signal acquisition electrodes, and Fpz is a reference electrode for Fp1 and Fp2.

[0016] In one embodiment of the present invention, the client includes a switch module, a communication module, a gastric electrical signal processing module, a gastrobrain coupling module, an analysis module, and a display module, wherein,

[0017] The switch module is used to turn the gastric function regulating device on and off;

[0018] The communication module is used to acquire gastric electrical signals and electroencephalogram (EEG) signals collected by the gastric function regulation device;

[0019] The gastric electrical signal processing module is used to process the gastric electrical signal of the current time period to obtain the gastric rhythm parameters of the current time period. The gastric rhythm parameters include the gastric electrical dominant frequency, gastric electrical amplitude and the proportion of normal slow gastric waves.

[0020] The gastrobrain coupling module is used to obtain gastrobrain coupling parameters based on the gastric electroencephalogram (GEG) and brain electroencephalogram (EEG) signals of the current time period. The gastrobrain coupling signals include phase-amplitude coupling values ​​and transfer entropy.

[0021] The analysis module is used to analyze and compare the gastric electrical frequency, gastric electrical amplitude, and the proportion of normal slow gastric waves with their normal range in the current time period to obtain the vagus nerve stimulation intensity in the next time period.

[0022] The display module is used to display the gastric electrical signal waveform, the electroencephalogram waveform, the gastric rhythm parameters, and the vagus nerve stimulation parameters in real time.

[0023] The communication module is also used to feed back the vagus nerve stimulation intensity for the next time period to the gastric function regulation device.

[0024] In one embodiment of the present invention, the gastric electrocardiogram (ECG) signal processing module includes a power spectrum analysis unit, a filtering unit, a gastric ECG signal parsing unit, an artifact detection unit, and a gastric rhythm feature extraction unit, wherein,

[0025] The power spectrum analysis unit is used to process the gastric electrical signal of the current time period, obtain the power spectrum of the gastric electrical signal of the current time period, and select the gastric electrical signal with the largest power spectrum among the gastric electrical signals collected by multiple gastric electrical signal acquisition electrodes.

[0026] The filtering unit is used to filter the gastric electrical signal with the largest power spectrum to obtain the filtered gastric electrical signal.

[0027] The gastric electrical signal analysis unit is used to analyze the filtered gastric electrical signal to obtain the analyzed phase information and instantaneous amplitude;

[0028] The artifact recognition unit is used to filter out large amplitude vibrations caused by body movement and non-monotonic phase changes introduced by linear interference, and obtain gastric electrical signals after artifact removal.

[0029] The gastric rhythm feature extraction unit is used to obtain the gastric rhythm parameters for the current time period based on the gastric electrical signal after artifact removal.

[0030] In one embodiment of the present invention, the EEG signal processing module includes an EEG signal preprocessing unit, a phase-amplitude coupling unit, and a transfer entropy acquisition unit, wherein,

[0031] The EEG signal preprocessing unit is used to filter, extract features and identify artifacts from the acquired EEG signals to obtain preprocessed EEG signals.

[0032] The phase-amplitude coupling unit is used to couple the analytical phase of the gastric electroencephalogram (GEG) signal with the instantaneous amplitude of the electroencephalogram (EEG) signal to obtain a phase-amplitude coupling value.

[0033] The transfer entropy acquisition unit is used to determine the transfer entropy based on the preprocessed gastric electroencephalogram (GEG) signal and the preprocessed electroencephalogram (EEG) signal.

[0034] In one embodiment of the present invention, the transfer entropy acquisition unit is specifically used for:

[0035] Determine the gastric electrical vector based on the gastric electrical signal after artifact removal;

[0036] The brainwave vector is determined based on the gastric electrical vector and the preprocessed brainwave signal;

[0037] The corresponding marginal probability distribution and joint probability distribution are determined based on the gastric electrical vector and the brain electrical vector;

[0038] The conditional entropy and joint entropy are determined based on the marginal probability distribution and the joint probability distribution. The formulas for calculating the joint entropy H(x) and the conditional entropy H(x|y) are as follows:

[0039]

[0040]

[0041] Where p(x) represents the marginal probability distribution of the last vector of the gastric electrical vector, p(y) represents the marginal probability distribution of the last vector of the electroencephalogram (EEG) vector, and p(x,y) represents the joint probability distribution of the last vector of the gastric electrical vector and the EEG vector and the current value.

[0042] Determine the transition entropy based on the conditional entropy and the joint entropy:

[0043]

[0044] in, It is the joint entropy of the current value of the EEG vector and the last vector of the Gastrointestinal vector. It is the conditional entropy of the current value of the EEG vector under the condition of the last vector of the given Gastroelectroencephalogram, where M represents the length of the vector.

[0045] In one embodiment of the present invention, the analysis module is specifically used for:

[0046] The gastric electrical frequency is compared with its corresponding normal range. When the gastric electrical frequency is within its normal range, d1 = 0; when the gastric electrical frequency is less than the minimum value of its normal range, d1 is equal to the difference between the minimum value of the normal range and the gastric electrical frequency; when the gastric electrical frequency is greater than the maximum value of its normal range, d1 is equal to the difference between the gastric electrical frequency and the maximum value of its normal range.

[0047] The gastric electrical amplitude is compared with its corresponding normal range. When the gastric electrical amplitude is within its normal range, d2 = 0. When the gastric electrical amplitude is less than the minimum value of its normal range, d2 is equal to the difference between the minimum value of the normal range and the gastric electrical amplitude. When the gastric electrical amplitude is greater than the maximum value of its normal range, d2 is equal to the difference between the gastric electrical amplitude and the maximum value of its normal range.

[0048] The normal proportion of slow gastric waves is compared with its corresponding normal range. When the normal proportion of slow gastric waves is within its normal range, d3 = 0; when the normal proportion of slow gastric waves is less than the minimum value of its normal range, d3 is equal to the difference between the minimum value of the normal slow gastric wave proportion and the normal slow gastric wave proportion; when the normal proportion of slow gastric waves is greater than the maximum value of its normal range, d3 is equal to the difference between the normal slow gastric wave proportion and the maximum value of its normal range.

[0049] Calculate the current intensity of vagal nerve stimulation in the next time period:

[0050]

[0051] Where m = 1, 2, 3, IT k α represents the current intensity of the vagus nerve stimulation used in the previous time period. m For d m The weight, K P This is the gain coefficient.

[0052] In one embodiment of the present invention, the analysis module is further configured to:

[0053] When the gastric electrical frequency, the gastric electrical amplitude, and the proportion of normal slow gastric waves are all within their normal ranges, and the gastrobrain coupling parameters are within their normal ranges, a command is sent to the switching module to turn off the gastric function regulation device.

[0054] When the phase-amplitude coupling value is detected to be lower than a preset threshold or the transfer entropy is close to 1 or -1, an instruction is sent to the switch module to make the switch module turn off the gastric function regulation device, or a prompt message is issued.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] 1. The gastric function regulation system based on gastric rhythm and gastrobrain coupling of this invention combines vagus nerve stimulation technology with gastric rhythm parameters reflecting human gastric function and gastrobrain coupling parameters reflecting human energy homeostasis and gastrobrain balance. By monitoring changes in gastric rhythm during stimulation, the system provides feedback to guide the vagus nerve stimulation parameters and monitors the gastrobrain coupling parameters in real time. The system automatically terminates the regulation process when human energy homeostasis is disrupted or when there is an imbalance between the gastrobrain and gastrobrain functions. This achieves more precise and individualized gastric function regulation while ensuring user safety, maximizing the regulation of the user's gastric function system.

[0057] 2. This invention is a device that combines vagal stimulation and physiological signal acquisition. The client-side interface enables control of the gastric function regulation device and visualization of gastric electrical signals, electroencephalogram (EEG) signals, gastric rhythm parameters, and stimulation parameters; the cloud-based interface stores user historical data and usage reports. The wearable gastric electrical signal acquisition band and EEG headband designed for the gastric and EEG signal acquisition sections enable a more portable and accurate acquisition process.

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of a gastric function regulation system based on gastric rhythm and gastrobrain coupling provided in an embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of a vagus nerve and gastric electroacupuncture acquisition device provided in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of vagus nerve stimulation of the ear pole provided in an embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram of a gastric electrical data acquisition device and a wearable gastric electrical data acquisition belt provided in an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram of an electroencephalogram (EEG) acquisition method provided in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of a client provided in an embodiment of the present invention;

[0065] Figure 7This is a flowchart illustrating the use of a gastric function regulation system based on gastric rhythm parameter feedback, provided in an embodiment of the present invention.

[0066] Figure 8 This is a power spectrum analysis diagram of a gastric electrical signal provided in an embodiment of the present invention;

[0067] Figure 9 This is an example diagram of the calculation result of the normal slow wave proportion provided by an embodiment of the present invention. Detailed Implementation

[0068] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following describes in detail a gastric function regulation system based on gastric rhythm and gastrobrain coupling proposed according to the present invention, in conjunction with the accompanying drawings and specific embodiments.

[0069] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a more in-depth and concrete understanding can be gained of the technical means and effects adopted by the present invention to achieve its intended purpose. However, the accompanying drawings are for reference and illustration only and are not intended to limit the technical solutions of the present invention.

[0070] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or apparatus that includes said element.

[0071] Please see Figure 1 , Figure 1This is a schematic diagram of a gastric function regulation system based on gastric rhythm and gastrobrain coupling provided by an embodiment of the present invention. The gastric function regulation system includes a gastric function regulation device 100, a client 200, and a cloud platform 300. The client 200 is used to control the start and stop of the gastric function regulation device 100, and also to acquire, process, and analyze the gastric electrical signals collected by the gastric function regulation device 100 to obtain the gastric rhythm and gastrobrain coupling parameters for the current time period. Furthermore, it obtains the vagus nerve stimulation parameters for the next time period through the gastric rhythm parameters for the current time period, and acquires the user's gastrobrain balance state through the gastrobrain coupling parameters for the current time period. The system terminates when the gastrobrain balance state is unbalanced. The gastric function regulation device 100 is used to connect to the external vagus nerve... A nerve stimulation electrode is placed on the ear to apply vagus nerve stimulation that can regulate gastric function. An external gastric electrical signal acquisition electrode is placed on the abdomen to collect gastric electrical signals, and an external electroencephalogram (EEG) signal acquisition electrode is placed on the forehead to collect EEG signals. The gastric electrical signals and EEG signals are uploaded to the client for real-time display and analysis, guiding changes in vagus nerve stimulation parameters. The cloud 300 is used to store the user's historical usage records, including changes in stimulation parameters, gastric rhythm parameters, and gastrobrain coupling parameters during use. Through repeated use and iteration, users can achieve more precise regulation of gastric function.

[0072] In other words, the gastric function regulation system of this embodiment comprises three parts: the first part is a gastric function regulation device for applying vagus nerve stimulation and collecting gastric and brain electrical signals; the second part is a client for interaction between the user and the gastric function regulation device; and the third part is a cloud platform for storing user data. These three parts work together to regulate the user's gastric function system and adjust appetite.

[0073] Further, please see Figure 2 , Figure 2This is a schematic diagram of a vagus nerve and gastric electroacupuncture acquisition device provided in an embodiment of the present invention. The gastric function regulation device 100 includes an internal controller 101, a stimulation current signal generator 102, a physiological signal amplifier 103, and a power module 104. The internal controller 101 controls the physiological signal amplifier 103 to collect gastric electrical signals and electroencephalogram (EEG) signals of the current time period and transmit them to the client 200. It also receives vagus nerve stimulation parameters for the next time period from the client 200 and sends them to the stimulation current signal generator 102. The stimulation current signal generator 102 is externally connected to vagus nerve stimulation electrodes to generate vagus nerve stimulation signals based on the vagus nerve stimulation parameters and transmit them to the electrodes. The physiological signal amplifier 103 is externally connected to gastric electrical signal acquisition electrodes and EEG signal acquisition electrodes to collect the user's gastric electrical signals and EEG signals during gastric function regulation and upload them to the client for analysis. The power module 104 provides isolated power to the internal controller 101, the stimulation current signal generator 102, and the physiological signal amplifier 103. In this embodiment, the power module 104 can be charged via an external charging port.

[0074] It is worth noting that the gastric function regulation device 100 is electrically connected to the vagus nerve stimulation electrode. One end of the vagus nerve stimulation electrode is fixed to the vagus nerve stimulation port of the gastric function regulation device 100, and the other end is fixed to the user's left ear. The electrode part connected to the ear is as follows: Figure 3 As shown. The positive electrode of this vagus nerve stimulation electrode is located at the cymba conchae (or medial side of the tragus) of the left ear, and the negative electrode is located at the medial side of the tragus (or cymba conchae) of the left ear. In addition, this gastric function modulation device 100 can also be connected to other vagus nerve stimulation electrodes, such as those for stimulating the vagus nerve in the neck. In principle, the stimulation electrodes are generally used to stimulate sites richly innervated by the vagus nerve, such as the aforementioned cymba conchae, tragus, or neck. The vagus nerve stimulation pulse is as follows: stimulation frequency of 25Hz; "on" phase with a duration of 30-60s; "off" phase with a duration of 60-120s; pulse width of 250-500μs; stimulation current intensity <5mA. The stimulation intensity during the "on" phase is the current intensity obtained from gastric rhythm parameters, and the stimulation current intensity during the "off" phase is zero. These stimulation parameter ranges are known and can enhance vagus nerve efferent activity.

[0075] Furthermore, the gastric function regulation device 100 is electrically connected to the gastric electrical signal acquisition electrode. One end of the gastric electrical signal acquisition electrode is fixed to the gastric electrical signal acquisition port of the gastric function regulation device, and the other end is fixed to the abdominal surface of the human body, located in the stomach area, such as... Figure 4As shown. The gastric electrocardiogram (GND) signal acquisition electrode consists of seven points, five of which are for acquiring GND signals, and the remaining two are a reference electrode and a ground electrode, respectively. The first GND acquisition point 1 is located at the xiphoid process; the second GND acquisition point 2 is located at the midpoint between the xiphoid process and the navel; the fourth GND acquisition point 4 is located at the intersection of the horizontal line of the first GND acquisition point 1 and the vertical line of the midclavicular line; the fifth GND acquisition point 5 is located at the intersection of the horizontal line of the second GND acquisition point 2 and the vertical line of the midclavicular line; and the third GND acquisition point 3 is located at the intersection of the diagonals of the rectangle formed by the remaining four GND acquisition points 1, 2, 4, and 5. The ground electrode (GND) is located in the left abdomen, above the iliac crest, and the reference electrode (REF) is placed symmetrically with the third GND acquisition point 3.

[0076] The positions of the gastric electrical signal acquisition electrodes described above are the validated optimal gastric electrical signal acquisition sites. The part connecting the gastric electrical signal acquisition electrodes to the human body is a wearable gastric electrical signal acquisition strap. Users can wear it themselves after cleaning their abdominal skin. Once the device is running, the impedance of each gastric electrical signal acquisition electrode can be used to check if the acquisition strap is worn correctly. The fixed connection part of this wearable gastric electrical signal acquisition strap can be as follows: Figure 4 The wearable gastric electrocardiogram (ECG) acquisition belt features Velcro closures at both ends, or a buckle that allows the user to adjust the length of the acquisition belt according to their body shape. Internally, it includes all the ECG acquisition electrodes, a reference electrode, and a ground electrode. Each electrode has a lead-out wire inside the belt, which converges outside the belt and connects to the ECG acquisition port of the gastric function regulation device 100. This wearable ECG acquisition belt comes in different models to provide more accurate and convenient ECG acquisition for users of different body types. In addition to the acquisition belt, the ECG signal acquisition electrodes can also be used via electrode bundles. One end of the electrode bundle is divided into seven single-ended connections that attach to the various ECG acquisition points on the abdomen. The other end of the electrode bundle connects to the ECG acquisition port of the device.

[0077] Similarly, one end of the EEG signal acquisition electrode is fixed to the EEG acquisition port of the gastric function regulation device 100, and the other end is fixed to the forehead of the human body as a wearable soft headband, such as... Figure 5As shown, the headband has three embedded electrodes in the section facing the forehead, corresponding to Fp1, Fp2, and Fpz in a 10-20 lead system. Fp1 and Fp2 are the acquisition electrodes, and Fpz is the reference electrode for Fp1 and Fp2. Users can wear the headband themselves after cleaning their forehead. Once the device starts operating, it automatically detects whether the headband is worn correctly by measuring the impedance of each acquisition electrode. This soft headband comes in different models to provide a more accurate and convenient EEG acquisition method for users with different head circumferences. In addition, EEG acquisition can also use electrode bundles. One end of the electrode bundle is divided into three single-ended connections that connect to the various brain potential points on the forehead, while the other end connects to the EEG acquisition port of the device.

[0078] Furthermore, the client 200 allows the user to control the gastric function regulation device 100; it displays real-time gastric electrical signal waveforms, electroencephalogram (EEG) waveforms, gastric rhythm parameters, and vagus nerve stimulation parameters during user operation. Importantly, the client can process and analyze the gastric electrical signals acquired by the gastric function regulation device 100 to obtain gastric rhythm parameters, including the dominant frequency of gastric electrical signals (the peak frequency within the power spectrum), gastric electrical amplitude, and the proportion of normal slow waves in the stomach. It can also analyze the acquired gastric and EEG signals to obtain gastrobrain coupling parameters, including phase-amplitude coupling (PAC) values ​​and transfer entropy. These gastric rhythm parameters and gastrobrain coupling parameters are important peripheral indicators of vagus nerve stimulation regulating gastric function. Therefore, the client analyzes the gastric electrical and electroencephalographic signals monitored during vagal nerve stimulation in the previous fixed time period to obtain the corresponding gastric rhythm parameters and gastrobrain coupling parameters. Based on the changes in these gastric rhythm parameters, the client guides the current intensity of vagal nerve stimulation in the next fixed time period. Based on the gastrobrain coupling parameters, the client monitors the user's homeostatic information and the balance between the gastrobrain in real time. In this feedback manner, the client maximizes the activation of the user's vagal nerve complex, regulates the user's gastric function system, and regulates appetite.

[0079] The client 200 in this embodiment includes a switch module, a communication module, a gastric electrical signal processing module, a gastrobrain coupling module, an analysis module, and a display module. The switch module is used to turn the gastric function regulation device on and off; the communication module is used to acquire the gastric electrical signals and electroencephalogram (EEG) signals collected by the gastric function regulation device; the gastric electrical signal processing module is used to process the gastric electrical signals of the current time period to obtain the gastric rhythm parameters of the current time period, including the gastric electrical dominant frequency, gastric electrical amplitude, and the proportion of normal slow gastric waves; the gastrobrain coupling module is used to obtain gastrobrain coupling parameters based on the gastric electrical signals and EEG signals of the current time period, including the phase-amplitude coupling value and transfer entropy; the analysis module is used to analyze and compare the gastric electrical dominant frequency, gastric electrical amplitude, and the proportion of normal slow gastric waves of the current time period with their corresponding normal ranges to obtain the vagus nerve stimulation intensity for the next time period; the display module is used to display the gastric electrical signal waveform, EEG signal waveform, gastric rhythm parameters, and vagus nerve stimulation parameters in real time, such as... Figure 6 As shown; the communication module is also used to feed back the intensity of vagal nerve stimulation in the next time period to the gastric function regulation device.

[0080] In this embodiment, the client 200 is a smart mobile device, which can be a tablet or a mobile phone, and is presented in the form of an APP;

[0081] Specifically, after the user turns on the gastric function regulation device 100 via the switch module, they can send a start command to the device 100 through the start button on the client 200. The internal processor 101 of the gastric function regulation device 100 receives the command and controls the device to start operating. Once operating, the device displays real-time waveforms of gastric electrical and electroencephalographic signals on the client 200, along with charts and graphs related to gastric rhythm parameters obtained from the internal gastric electrical signal processing module, for the user to view. In addition, the client 200 also displays vagus nerve stimulation parameters, which can be automatically obtained from the calculated gastric rhythm parameters or set by the user. When the user wants to end use, they can do so via the end button. After each use, the client 200 uploads and stores the gastric rhythm parameters, gastrobrain coupling parameters, and vagus nerve stimulation parameters recorded during the usage process to the cloud 300. The cloud 300 then generates a gastric function improvement report for the user to view. This report includes changes in gastric rhythm, gastrobrain coupling parameters, and vagus nerve stimulation intensity throughout the usage process. Furthermore, upon the next use, the client retrieves the previous usage record and sets the optimal stimulation parameters from the previous usage as the initial stimulation parameters for this use. This allows for more precise and individualized control for each user, regulating their gastric function and appetite.

[0082] The system's cloud storage is used to store each user's usage record, including gastric rhythm, gastrobrain coupling parameters, and vagus nerve stimulation parameters during use. When the client reads historical data, it automatically retrieves the parameter information from the user's most recent use and provides a detailed report on the user's usage process.

[0083] When using the gastric function regulation system, users need to wear vagus nerve stimulation electrodes (located on the ear or neck, or other points innervated by the vagus nerve), gastric electrical signal acquisition electrodes (wearable gastric electrical signal acquisition straps or electrode bundles), and electroencephalogram (EEG) signal acquisition electrodes (wearable EEG acquisition straps or electrode bundles). Before wearing, clean the areas of the body that will contact the gastric and EEG acquisition electrodes or stimulation electrodes with alcohol swabs and exfoliating scrub to reduce the impedance at each point, thereby achieving better stimulation and obtaining high-quality gastric and EEG signals. After wearing the electrodes, open the gastric function regulation device 100 through the client APP. The system will automatically detect the impedance of the stimulation and acquisition electrodes. If the impedance is too high, it will issue a prompt sound to remind the user to wear them again until the impedance meets the requirements, and then proceed with the subsequent operations.

[0084] Further, please see Figure 7 , Figure 7 This is a flowchart illustrating the usage of a gastric function regulation system based on gastric rhythm and gastrobrain coupling, as provided in this embodiment of the invention. After the system begins execution, the client controls the gastric function regulation device 100 to first collect the user's gastric electrical signals and electroencephalogram (EEG) signals during a time interval T (T being 5-10 minutes), and uploads them to the client in real time for display and analysis. For the gastric and EEG signals during time interval T, the system analyzes the gastric electrical dominant frequency, gastric electrical amplitude, proportion of normal slow waves, PAC, and transfer entropy, displaying and storing the results to provide the user with their initial gastric rhythm parameters. Subsequently, vagus nerve stimulation and physiological signal (including gastric and EEG signals) are collected during time interval T. If the user is using the system for the first time, the initial stimulation current intensity IT... k For IT ini (IT ini The initial stimulation current intensity is typically 0.5-1.5 mA (for vagus nerve stimulation). If the user has prior usage history, the initial stimulation current intensity should be IT. k The best stimulation current intensity IT will be used from the last usage record stored in the cloud. k+1 .

[0085] After synchronous acquisition of vagal nerve stimulation and physiological signals during the T time period, the gastric rhythm parameters and gastrobrain coupling parameters during the T time period were analyzed in real time to obtain the gastric electrical dominant frequency Df (cpm), gastric electrical amplitude Amp (uv), percentage of normal slow gastric waves Nsw (%), PAC, and transfer entropy.

[0086] In this embodiment, the gastric electrical signal processing module includes a power spectrum analysis unit, a filtering unit, a gastric electrical signal parsing unit, an artifact recognition unit, and a gastric rhythm feature extraction unit.

[0087] The power spectrum analysis unit is used to process the gastric electrical signals of the current time period, obtain the power spectrum of the gastric electrical signals of the current time period, and select the gastric electrical signals with the largest power spectrum among the gastric electrical signals collected by multiple gastric electrical signal acquisition electrodes.

[0088] Specifically, the power spectrum analysis unit of this embodiment of the invention uses a multi-window spectrum estimation method to obtain the power spectrum of the gastric electroencephalogram (GEG) signal. The basic idea of ​​the multi-window spectrum estimation method is to obtain a cluster of data window functions by minimizing the frequency leakage outside half the bandwidth, based on the Rayleigh-Ritz minimization problem. This cluster of window functions replaces the single window function. This cluster 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, thereby maintaining the offset at an acceptable level. Using a cluster of orthogonal window functions to process random signals can reduce spectral leakage caused by the limited data length.

[0089] Since it is impossible to directly measure and calculate an infinitely long signal, this embodiment analyzes a finite time segment of the gastric electrical signal. Specifically, time segments of 50-100 seconds are extracted from the gastric electrical signal over a time period T, with an 80%-90% overlap between each segment. Each segment is then periodically extended to obtain a virtual infinitely long signal, which is then subjected to a Fourier transform. Truncation of the gastric electrical signal distorts the spectrum; the energy originally concentrated at the signal's center frequency (fundamental frequency) f(0) is dispersed into two wider frequency bands. To reduce this spectral energy leakage, this patent employs different truncation functions, i.e., window functions, to truncate the signal.

[0090] The specific calculation steps are as follows:

[0091] (1) Determine the length N and sampling rate f of the gastric electrical signal collected during time interval T. s To determine the frequency resolution f that satisfies the Nyquist theorem. n .

[0092] (2) Generate data window

[0093] 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 N represent the k-th sequence sample, where N represents the length of the sequence sample of the k-th Slepian sequence, W represents the time-half bandwidth product, and n represents the n-th sample of the k-th Slepian sequence, abbreviated as . This sequence satisfies the following two conditions:

[0094] i. Each window function has a unit energy, that is:

[0095]

[0096] Where K represents the number of Slepian sequences.

[0097] ii. These window functions are mutually orthogonal, that is:

[0098]

[0099] To satisfy the above two conditions, the Slepian sequence can be generated using the following formula:

[0100]

[0101] Where n represents the start time of each 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.

[0102] K = |p+1|

[0103]

[0104] (3) Calculation of characteristic coefficients

[0105] The acquired raw gastric electroencephalogram (GEG) signal is multiplied by the generated Slepian data window sequence to obtain a k-windowed data sequence X(t), and then subjected to the following discrete Fourier transform to obtain the characteristic coefficients:

[0106]

[0107] The above formula is the formula for Discrete Fourier Transform, where ft represents the product of the time index and the frequency index. It is a modulation factor used to analyze the frequency components of the time-series signal X(t) in the frequency domain, and t represents time.

[0108] (4) Adaptive weighting

[0109] 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 a weighting coefficient to obtain the power spectrum p of gastric electrical activity. x (f):

[0110]

[0111] in,

[0112] Under normal circumstances, high-quality electrogastrography (EGG) recordings exhibit unique spectral characteristics. Therefore, this embodiment calculates the power spectrum of each EGG lead, such as... Figure 8 As shown, the power spectrum of the gastric electrical signal was obtained by selecting the lead with the highest power at the peak frequency for subsequent analysis.

[0113] Furthermore, the filtering unit is used to filter the gastric electrical signal with the largest power spectrum to obtain the filtered gastric electrical signal. Specifically, a third-order FIR filter and a zero-phase filter are used for bidirectional filtering to perform bandpass filtering of 0.033-0.067Hz on the gastric electrical signal with the largest power spectrum, with a transition band of 0.15.

[0114] Subsequently, the gastric electrical signal analysis unit is used to analyze the filtered gastric electrical signal to obtain the analyzed phase information and instantaneous amplitude.

[0115] Specifically, in order to better analyze the phase change of the gastric electrical signal, this embodiment uses Hilbert transform to transform the one-dimensional gastric electrical signal into an analytic signal on a two-dimensional complex plane. The complex modulus and argument of the signal represent the amplitude and phase of the signal, respectively. That is, the envelope (instantaneous amplitude) and analytic phase of the analytic signal can be calculated.

[0116] First, the filtered gastric electrical signal is transformed to the frequency domain using a Discrete Fourier Transform (DFT). Then, it is multiplied by the Hilbert filter coefficients in the frequency domain, and finally transformed back to the time domain using an Inverse Fourier Transform (IFT) to obtain an analytical representation of the gastric electrical signal. In the following formulas, x(τ) and x(t) both represent the gastric electrical signal with the highest power spectrum.

[0117] The Hilbert transform of the gastric electrical signal is calculated using the following formula:

[0118]

[0119] Where t represents time.

[0120] The analytical process of the Hilbert transform is as follows:

[0121]

[0122] in, Denotes the imaginary part of the Hilbert transform of x(t). This represents the obtained analytical signal.

[0123] The formula for calculating instantaneous amplitude is as follows:

[0124]

[0125] The formula for calculating the analytical phase is as follows:

[0126]

[0127] The artifact detection unit is used to filter out large amplitude vibrations caused by body movement and non-monotonic phase changes introduced by linear interference, so as to obtain the gastric electrical signal after artifact removal.

[0128] (1) Large amplitude caused by body movement

[0129] Under normal circumstances, the amplitude of slow waves in gastric electrical activity is around 50-250uV. When the subject moves or accidentally touches the electrode wire, causing disturbance, the amplitude of the gastric electrical signal will change significantly. Typically, single-cycle waves with amplitudes greater than 500uV are identified and removed. The specific steps are as follows:

[0130] First, the instantaneous amplitude of the gastric electrical signal is obtained according to the Hilbert transform. The position where the amplitude is greater than the threshold of 500uV is found and denoted as T. For each position t∈T, T represents the entire time period of the current gastric electrical signal. The position of each peak is identified by the peak detection algorithm, and the gastric electrical slow wave period in which it is located is found, that is, the interval between the previous peak and the next peak, denoted as I(t). All parts corresponding to I(t) in the gastric electrical signal are marked and extracted.

[0131] (2) Nonlinear interference introduces non-monotonic phase changes

[0132] 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 the 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 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. The specific implementation steps are as follows:

[0133] First, the distribution of cycle lengths is calculated based on the analytical phase to determine the threshold. Edges in the gastric electrical signal where the phase derivative change is greater than -1 are identified, and the time difference between each edge, i.e., the cycle length, is calculated. Then, the mean and standard deviation of the cycle lengths are calculated, and the threshold is set to the mean minus or plus three times the standard deviation. Next, artifacts are detected based on the phase time series. Cycles that are too short or too long, as well as non-monotonic increasing cycles in the signal, are identified and marked with red areas in the graph, and the time point information corresponding to the artifacts is extracted. Artifact labels are constructed based on the identified artifact information, and the parameters at artifact locations are not calculated in subsequent calculations of gastric rhythm parameters.

[0134] Furthermore, the gastric rhythm feature extraction unit is used to obtain the gastric rhythm parameters for the current time period based on the gastric electrical signal after artifact removal. Specifically, it needs to obtain the gastric electrical dominant frequency, gastric electrical amplitude, and the proportion of normal slow gastric waves for the current time period.

[0135] (1) Gastric electrical frequency and power

[0136] Within the defined normal gastric electrical frequency range (0.033–0.067 Hz), locate the local maximum value (peak value) in the power spectrum of each gastric electrical channel, and record the frequency and power corresponding to the peak value. This frequency is the dominant frequency of gastric electrical activity, and the power is the dominant power at the dominant frequency. The calculated results are as follows: Figure 9 As shown, the power spectrum information of the five gastric electrical signals can be seen. The part between the two vertical lines is the normal gastric electrical frequency range. Among them, EGG4 has the largest power spectrum, and its main frequency is 3.12 cpm.

[0137] (2) Amplitude

[0138] First, the filtered and artifact-detected gastric electroencephalogram (GEG) signals are differentially analyzed to obtain the rate of change, and a sign function is used to determine the sign of the rate of change. Then, a second-order difference is performed on the sign to obtain the sign variation. Positions where the sign changes to -2 are identified, corresponding to the maximum values ​​of the GEG signals, and positions where the sign changes to 2 are identified, corresponding to the minimum values. Next, it is determined whether the maximum and minimum value matrices of two GEG signals in each channel have the same length. If they do, the difference between the maximum and minimum value matrices is calculated, which represents the amplitude of the GEG signal. If they do not have the same length, it is determined which matrix has one more point, and the extra data point is then subjected to forward or backward difference analysis.

[0139] (3) Proportion of normal slow waves

[0140] To estimate the period from the analytic phase obtained by the Hilbert transform, the analytic phase is first differentially divided. The differentially divided data is stored in a logic array, where each element is true or false, indicating whether the signal has a falling edge. The falling edge is the point where the signal changes from a high value to a low value, representing the start of each period.

[0141] Next, based on the position of the falling edge, find the corresponding timestamp. The timestamp is the time point when the signal was sampled. Return the index of the element that is true in the logic array, which is the position of the falling edge; use these indices to access the timestamp array to get the time point corresponding to the falling edge.

[0142] Subsequently, the time points corresponding to the falling edge are differentially analyzed, i.e., the interval between two adjacent time points is calculated. This yields the duration of each cycle, i.e., the cycle length, and allows us to determine which cycle lengths fall within the normal range. First, the indices of elements in the cycle length array that are less than the lower limit or greater than the upper limit are returned, indicating the positions of abnormal cycles. This allows us to calculate the number of abnormal cycles. Then, the total number of cycles is subtracted from the number of abnormal cycles to obtain the number of normal cycles. Finally, the number of normal cycles is divided by the total number of cycles to obtain the percentage of normal cycles. Figure 9 The chart shows the calculated percentage of normal slow wave cycles. The dashed line represents the range of normal slow wave duration. As can be seen from the chart, the percentage of normal slow waves in this gastric electrical signal is 14.6%.

[0143] Furthermore, the EEG signal processing module of this embodiment includes an EEG signal preprocessing unit, a phase-amplitude coupling unit, and a transfer entropy acquisition unit. The EEG signal preprocessing unit is used to filter, extract features, and identify artifacts from the acquired EEG signals to obtain preprocessed EEG signals. The phase-amplitude coupling unit is used to couple the analytical phase of the gastric electroencephalogram (GEG) signal with the instantaneous amplitude of the EEG signal to obtain a coupling strength value. The transfer entropy acquisition unit is used to determine the transfer entropy based on the preprocessed GEG signal and the preprocessed EEG signal.

[0144] Specifically, for the gastric electroencephalogram (GEG) and electroencephalogram (EEG) signals in each T time period (the EGG signal has already undergone the aforementioned power spectrum analysis, filtering, signal analysis, and artifact removal processes), the specific processing procedure of this EEG signal preprocessing unit is as follows:

[0145] (1) Filtering

[0146] First, a notch filter of 49-51Hz is used to perform band-stop filtering on the original EEG signal to remove the 50Hz power frequency interference. Then, the 0.5-70Hz frequency band is divided into passbands of 1Hz, resulting in 0.5-1Hz, 1-2Hz, ... 69-70Hz. Bandpass filtering is then performed on each of these 70 frequency bands, followed by bidirectional filtering.

[0147] (2) Feature extraction

[0148] For the filtered EEG signal, Hilbert transform was used to extract the instantaneous amplitude and analytical phase of different frequency bands of the EEG signal, and the processing was the same as the feature extraction of the gastric electroencephalogram (GEG) signal.

[0149] (3) Forgery detection

[0150] This embodiment detects and extracts artifacts from non-neural sources such as outliers, eye movements, electromyography (EMG), and electrocardiograms within segmented frequency bands. Artifact identification is achieved by calculating the average amplitude difference and Z-score for each frequency band. The specific implementation steps are as follows:

[0151] First, the selected EEG signal is segmented into 10-second segments. The average amplitude difference of each segment is calculated by averaging the absolute values ​​of the differences between the data points in each segment and their mean. The maximum absolute value of the data points in each segment is also calculated. Next, a Z-transform is performed on the average amplitude difference of each segment. This is done by subtracting the mean from the average amplitude difference of each segment and dividing by its standard deviation, resulting in a Z-score matrix. Time points where the Z-score exceeds a predefined threshold are identified as EEG signal artifacts. These artifact data will not be included in subsequent parameter calculations.

[0152] Furthermore, the phase-amplitude coupling unit is used to perform phase-amplitude coupling between the analytical phase of the gastric electroencephalogram (GEG) signal and the instantaneous amplitude of the EEG signal to obtain a phase-amplitude coupling value.

[0153] Phase-amplitude coupling (PAC) is the relationship between the phase of one frequency band and the power of another. PAC is an indicator that reflects how the amplitude of a high-frequency signal changes with the phase of a low-frequency signal. It can reveal the interaction between neural oscillations of different frequencies, potentially related to information processing and integration in the brain. Binning-variance analysis is a method for calculating phase-amplitude coupling. Its basic idea is to divide the phase signal into several equally wide intervals, each called a bin. Then, the average amplitude signal within each bin is calculated. Finally, using variance analysis, the ratio of the between-group mean squared error to the within-group mean squared error, i.e., the F-statistic, is used as an indicator of the strength of phase-amplitude coupling. The advantage of this method is its simplicity and ease of implementation, as it does not require the assumption that the relationship between phase and amplitude is linear or sinusoidal.

[0154] The following describes the specific implementation steps for phase-amplitude coupling calculation in binning-variance analysis:

[0155] First, the resolved phase obtained using the gastric electroencephalogram (GEG) signal analysis unit is divided into n equally wide intervals, each called a bin. The boundary value of each bin is calculated. Then, for each bin, the index of the phase belonging to that bin is found. Based on these indices, the corresponding amplitude is extracted from the EEG signal amplitude, and its average value is calculated to obtain the average amplitude of each bin. Next, the overall average value of the average amplitudes of all bins is calculated, called the global mean.

[0156] The phase of gastric electroanalysis is The instantaneous amplitude of the EEG is a t Soon Divided into n bin The system is divided into several equally spaced intervals (bins). To simplify calculations, n... bin The value is 18, and the boundary of each interval is... Where k = 1, 2, ..., n bin .

[0157] Then calculate the interval 'a' within the instantaneous amplitude of the EEG corresponding to each interval. t The average value, that is:

[0158]

[0159] Where, n k This represents the number of time points belonging to the k-th interval.

[0160] Then, using the principles of analysis of variance, the between-group sum of squares (the sum of squares of the differences between the average amplitude of each bin and the global mean) and the within-group sum of squares (the sum of squares of the differences between the amplitude of each bin and its average amplitude) are calculated. Next, the between-group degrees of freedom (the number of bins minus one) and the within-group degrees of freedom (the number of data points minus the number of bins) are calculated. Then, the between-group and within-group root mean squares (RMS) and the between-group sum of squares (RMS) and the within-group sum of squares (RMS) are calculated, respectively. Finally, the F-statistic (the between-group MMS divided by the within-group MMS) is calculated. This value reflects the degree of modulation of the amplitude signal by the phase signal, that is, the strength of the phase-amplitude coupling.

[0161] Furthermore, the transfer entropy acquisition unit is used to determine the transfer entropy based on the preprocessed gastric electroencephalogram (GEG) signal and the preprocessed electroencephalogram (EEG) signal.

[0162] The processing steps of this transfer entropy acquisition unit specifically include the following steps:

[0163] Step a: Determine the gastric electrical vector based on the preprocessed gastric electrical signal (i.e., the gastric electrical signal after artifact removal mentioned above).

[0164] To calculate the information content and causality between the gastrobrain time series, the data first needs to be transformed into points in a multidimensional space. This allows for better capture of the dynamic characteristics within the data. Specifically, starting from the first point in the preprocessed gastric electroencephalogram (GEG) signal, a value is taken every t points until M values ​​are collected, resulting in an M-dimensional vector. This process is repeated starting from the second point to obtain a second M-dimensional vector. This process continues until all points are collected, resulting in an M-dimensional GEG vector sequence. The length of this vector sequence is N-(M-1)*t, where N is the length of the signal x.

[0165] Step b: Determine the EEG vector based on the gastric electrical vector and the preprocessed EEG signal.

[0166] For preprocessing EEG signals, the dimension of the gastric electroencephalogram (GEG) vector can be determined starting from the (M-1)*t+1th point, taking a value at every other point until all points are taken, thus obtaining a one-dimensional EEG vector sequence. The length of this vector sequence is also N-(M-1)*t.

[0167] Step c: Determine the corresponding marginal probability distribution and joint probability distribution based on the gastric electrical vector and the electroencephalogram (EEG) vector. Construct a two-dimensional joint probability distribution based on the last column of the gastric electrical vector sequence and the EEG vector sequence.

[0168] Step d: Determine the conditional entropy and joint entropy based on the marginal probability distribution and joint probability distribution. The formulas for calculating the joint entropy H(x) and conditional entropy H(x|y) are as follows:

[0169]

[0170]

[0171] Where p(x) represents the marginal probability distribution of the last vector of the gastric electrical vector, p(y) represents the marginal probability distribution of the last vector of the electroencephalogram (EEG) vector, and p(x,y) represents the joint probability distribution of the last vector of the gastric electrical vector and the current value of the EEG vector.

[0172] Step e: Determine the transition entropy based on the conditional entropy and joint entropy.

[0173] Finally, a formula is needed to calculate the transfer entropy, which is expressed using the entropy of two random variables: the conditional entropy and the joint entropy.

[0174]

[0175] in, It is the joint entropy of the current value of the EEG vector and the last vector of the Gastrointestinal vector. It is the conditional entropy of the current value of the EEG vector under the condition of the last vector of the given Gastroelectroencephalogram, where M represents the length of the vector.

[0176] Transition entropy measures the influence of one random variable on the future state of another random variable, essentially indicating causality. Specifically, it uses the current state of one random variable and the past states of another to predict the future state of the third, and then examines how much uncertainty this prediction reduces. If the prediction significantly reduces uncertainty, it indicates that the first random variable has a large influence on the second, meaning it has high transition entropy; conversely, if the prediction does not significantly reduce uncertainty, it indicates that the first random variable has little influence on the second, meaning it has low transition entropy.

[0177] This embodiment calculates the transfer entropy between gastric electrical activity and brain electrical activity, and the difference between them, to determine the causal direction between different frequency bands of gastric and brain electrical rhythms—that is, which rhythm has a greater influence on the amplitude of the other. If the difference is greater than 0, it indicates that the gastric electrical rhythm has a greater influence on the amplitude of the brain electrical rhythm; conversely, it indicates that the brain electrical rhythm has a greater influence on the amplitude of the gastric electrical rhythm; if the difference is close to 0, it indicates a balance between the stomach and brain.

[0178] Furthermore, this embodiment also includes a judgment module, which is used to determine whether the vagus nerve stimulation intensity of the next time period is less than the preset maximum stimulation current intensity. If so, the vagus nerve stimulation intensity of the next time period is sent to the internal processor; if not, the vagus nerve stimulation intensity of the previous time period is sent to the internal processor.

[0179] The analysis module is specifically used for:

[0180] The gastric electrical frequency is compared with its corresponding normal range. When the gastric electrical frequency is within its normal range, d1 = 0. When the gastric electrical frequency is less than the minimum value of its normal range, d1 is equal to the difference between the minimum value of its normal range and the gastric electrical frequency. When the gastric electrical frequency is greater than the maximum value of its normal range, d1 is equal to the difference between the gastric electrical frequency and the maximum value of its normal range.

[0181] The gastric electrical amplitude is compared with its corresponding normal range. When the gastric electrical amplitude is within its normal range, d2 = 0; when the gastric electrical amplitude is less than the minimum value of its normal range, d2 is equal to the difference between the minimum value of its normal range and the gastric electrical amplitude; when the gastric electrical amplitude is greater than the maximum value of its normal range, d2 is equal to the difference between the gastric electrical amplitude and the maximum value of its normal range.

[0182] The normal proportion of slow gastric waves is compared with its corresponding normal range. When the normal proportion of slow gastric waves is within its normal range, d3 = 0; when the normal proportion of slow gastric waves is less than the minimum value of its normal range, d3 is equal to the difference between the minimum value of its normal range and the normal proportion of slow gastric waves; when the normal proportion of slow gastric waves is greater than the maximum value of its normal range, d3 is equal to the difference between the normal proportion of slow gastric waves and the maximum value of its normal range.

[0183] Calculate the current intensity of vagal nerve stimulation in the next time period:

[0184]

[0185] Where m = 1, 2, 3, IT k α represents the current intensity of the vagus nerve stimulation used in the previous time period. m For d m The weight, K P d1 represents the difference between the dominant frequency of gastric electrical activity and its normal range, d2 represents the difference between the amplitude of gastric electrical activity and its normal range, and d3 represents the difference between the proportion of normal slow gastric waves and its normal range.

[0186] The analysis module is also used to: send a command to the switch module when the gastric electrical frequency, the gastric electrical amplitude, the proportion of normal gastric slow waves, and the gastrobrain coupling parameters are all within their normal range, so that the switch module turns off the gastric function regulation device.

[0187] When all three gastric rhythm indicators are within the normal range, it indicates that the user's gastric function has returned to normal, the device automatically terminates, and saves the parameters of this usage process to the cloud. If any of the three gastric rhythm indicators is outside the normal range, the algorithm will adjust the settings in real time based on the d values ​​for that time period. m Based on the situation, calculate the vagus nerve stimulation intensity for the next time period. The calculation formula is:

[0188]

[0189] Where m = 1, 2, 3, IT k α represents the intensity of the vagus nerve stimulation current used in the previous time period T. m For d m The weights can be determined based on gastric rhythm parameters, K. P The gain coefficient is used to make d m With IT k Keep it within a certain range.

[0190] Furthermore, the analysis module is also used to: send a command to the switch module to turn off the gastric function regulation device when the gastric electrical frequency, gastric electrical amplitude, and the proportion of normal slow gastric waves are all within their normal ranges, and the gastrobrain coupling parameters are within the normal ranges; when the phase-amplitude coupling value is detected to be lower than the preset threshold or the transfer entropy is close to 1 or -1, send a command to the switch module to turn off the gastric function regulation device, or issue a prompt message.

[0191] Specifically, the above formula will take effect if any one of the following is outside the normal range: the dominant frequency of gastric electrical activity, the amplitude of gastric electrical activity, or the proportion of normal slow gastric waves. This formula is used to calculate the current intensity IT of vagal nerve stimulation in the next time period T. k+1 If IT k+1 Less than the maximum stimulation current intensity that the human body can withstand (IT) max Then IT will be used k+1 The current intensity begins the next time interval T for vagal nerve stimulation and synchronized EGG and ECG signal acquisition. If IT k+1 Greater Than IT max The system then uses the current intensity from the previous time period T to begin vagal nerve stimulation and simultaneous EGG and ECG signal acquisition for the next time period T. Simultaneously, the system's analysis module analyzes the gastrobrain coupling index PAC and transfer entropy in real time every T time period. When the detected PAC value is too low (below a preset threshold) or the transfer entropy is close to 1 or -1, it indicates an imbalance in the body's energy homeostasis or an imbalance between the stomach and brain. The system will automatically interrupt and prompt the user to rest for 30 minutes before resuming use.

[0192] The above steps are repeated until all gastric rhythm and gastrobrain coupling parameters reach the normal range, indicating that the user's gastric function has been regulated to normal. The device then automatically terminates, and the client displays the improved gastric rhythm parameters and gastrobrain coupling after the adjustment. Users can also end the gastric function regulation process at any time via the "End" button on the client app. The system has a maximum number of cycles and will automatically terminate if too many cycles are exceeded. In addition, the client app uploads the user parameters from this usage process to the cloud so that users can view historical usage reports for future use. The device uses the historically best vagus nerve stimulation intensity as the initial stimulation intensity to achieve a more precise and personalized gastric function regulation process.

[0193] This invention relates to a gastric function regulation system based on gastric rhythm and gastrobrain coupling. It combines vagus nerve stimulation technology with gastric rhythm parameters reflecting human gastric function and cardiobrain coupling parameters reflecting human energy homeostasis and gastrobrain balance. By monitoring changes in gastric rhythm during stimulation, the system provides feedback to guide the vagus nerve stimulation parameters and monitors the gastrobrain coupling parameters in real time. The system automatically terminates the regulation process when there is an imbalance in human energy homeostasis or between the gastrobrain and vagus nerve. This achieves more precise and individualized gastric function regulation while ensuring user safety, maximizing the regulation of the user's gastric function system and promoting appetite. This invention is a device combining vagus stimulation and physiological signal acquisition. The client-side interface controls the gastric function regulation device and visualizes gastric electrical signals, electroencephalogram (EEG) signals, gastric rhythm parameters, and stimulation parameters; the cloud-based interface stores user historical data and usage reports. The wearable gastric EEG acquisition band and EEG headband in the gastric EEG signal acquisition section enable a more portable and precise acquisition process.

[0194] In the several embodiments provided by this invention, it should be understood that the apparatus and methods disclosed in this invention can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0195] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0196] 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 gastric function regulation system based on gastric rhythm and gastrobrain coupling, characterized in that, This includes gastric function regulation devices, client applications, and the cloud platform, among which... The client is used to control the start and end of the gastric function regulation device, and is also used to acquire gastric electrical signals and electroencephalogram signals collected by the gastric function regulation device for processing and analysis to obtain gastric rhythm parameters and gastrobrain coupling parameters for the current time period. Then, the vagus nerve stimulation parameters for the next time period are obtained through the gastric rhythm parameters for the current time period, and the user's gastrobrain balance state is obtained through the gastrobrain coupling parameters for the current time period. The system usage process is terminated when the gastrobrain balance state is unbalanced. The gastric function regulation device is used to connect an external vagus nerve stimulation electrode to the ear, apply vagus nerve stimulation to the ear according to the vagus nerve stimulation parameters to regulate gastric function, and is also used to connect an external gastric electrical signal acquisition electrode to the abdomen to acquire gastric electrical signals, and an external electroencephalogram (EEG) signal acquisition electrode to the forehead to acquire EEG signals, and upload the gastric electrical signals and the EEG signals to the client. The cloud is used to store historical usage records, which include changes in vagus nerve stimulation parameters, gastric rhythm parameters, and gastrobrain coupling parameters during the usage process. The client includes a switch module, a communication module, a gastric electrical signal processing module, a gastrobrain coupling module, an analysis module, and a display module, wherein... The switch module is used to turn the gastric function regulating device on and off; The communication module is used to acquire gastric electrical signals and electroencephalogram (EEG) signals collected by the gastric function regulation device; The gastric electrical signal processing module is used to process the gastric electrical signal of the current time period to obtain the gastric rhythm parameters of the current time period. The gastric rhythm parameters include the gastric electrical dominant frequency, gastric electrical amplitude and the proportion of normal slow gastric waves. The gastrobrain coupling module is used to obtain gastrobrain coupling parameters based on the gastric electroencephalogram (GEG) and brain electroencephalogram (EEG) signals of the current time period. The gastrobrain coupling signals include phase-amplitude coupling values ​​and transfer entropy. The analysis module is used to analyze and compare the gastric electrical frequency, gastric electrical amplitude, and proportion of normal slow gastric waves in the current time period with their corresponding normal ranges to obtain the vagus nerve stimulation intensity in the next time period. The display module is used to display the gastric electrical signal waveform, the electroencephalogram waveform, the gastric rhythm parameters, and the vagus nerve stimulation parameters in real time. The communication module is also used to feed back the vagus nerve stimulation intensity for the next time period to the gastric function regulation device.

2. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 1, characterized in that, The gastric function regulation device includes an internal controller, a stimulation current signal generator, a physiological signal acquisition amplifier, and a power supply module. The internal controller is used to control the physiological signal acquisition amplifier to acquire gastric electroencephalogram (GEG) signals and electroencephalogram (EEG) signals for the current time period and transmit them to the client. It is also used to receive vagus nerve stimulation parameters for the next time period from the client and send them to the stimulation current signal generator. The stimulation current signal generator is externally connected to a vagus nerve stimulation electrode, and is used to generate a vagus nerve stimulation signal according to the vagus nerve stimulation parameters and transmit it to the vagus nerve stimulation electrode. The physiological signal acquisition amplifier is externally connected to gastric electroencephalogram (GEG) signal acquisition electrodes and electroencephalogram (EEG) signal acquisition electrodes, which are used to acquire gastric GEG and EEG signals during the regulation process and upload them to the client. The power module is used to provide isolated power to the internal controller, the stimulation current signal generator, and the physiological signal acquisition amplifier.

3. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 1, characterized in that, It also includes a wearable gastric electrical signal acquisition belt, which has a reference electrode, a ground electrode and multiple gastric electrical signal acquisition electrodes inside. The gastric electrical signal acquisition electrodes are used to acquire gastric electrical signals. The two ends of the wearable gastric electrical signal acquisition belt are provided with Velcro or buckles to fix the wearable gastric electrical signal acquisition belt to the abdomen.

4. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 3, characterized in that, It also includes a wearable soft headband, which has three electrodes embedded in the part facing the forehead, namely Fp1, Fp2 and Fpz in a 10-20 lead system, wherein Fp1 and Fp2 are EEG signal acquisition electrodes, and Fpz is a reference electrode for Fp1 and Fp2.

5. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 4, characterized in that, The gastric electrocardiogram (ECG) signal processing module includes a power spectrum analysis unit, a filtering unit, a gastric ECG signal parsing unit, an artifact detection unit, and a gastric rhythm feature extraction unit. The power spectrum analysis unit is used to process the gastric electrical signal of the current time period, obtain the power spectrum of the gastric electrical signal of the current time period, and select the gastric electrical signal with the largest power spectrum among the gastric electrical signals collected by multiple gastric electrical signal acquisition electrodes. The filtering unit is used to filter the gastric electrical signal with the largest power spectrum to obtain the filtered gastric electrical signal. The gastric electrical signal analysis unit is used to analyze the filtered gastric electrical signal to obtain the analyzed phase information and instantaneous amplitude; The artifact recognition unit is used to filter out large amplitude vibrations caused by body movement and non-monotonic phase changes introduced by linear interference, and obtain gastric electrical signals after artifact removal. The gastric rhythm feature extraction unit is used to obtain the gastric rhythm parameters for the current time period based on the gastric electrical signal after artifact removal.

6. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 4, characterized in that, The EEG signal processing module includes an EEG signal preprocessing unit, a phase-amplitude coupling unit, and a transfer entropy acquisition unit, wherein... The EEG signal preprocessing unit is used to filter, extract features and identify artifacts from the acquired EEG signals to obtain preprocessed EEG signals. The phase-amplitude coupling unit is used to couple the analytical phase of the gastric electroencephalogram (GEG) signal with the instantaneous amplitude of the electroencephalogram (EEG) signal to obtain a phase-amplitude coupling value. The transfer entropy acquisition unit is used to determine the transfer entropy based on the preprocessed gastric electroencephalogram (GEG) signal and the preprocessed electroencephalogram (EEG) signal.

7. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 6, characterized in that, The transfer entropy acquisition unit is specifically used for: Determine the gastric electrical vector based on the gastric electrical signal after artifact removal; The brainwave vector is determined based on the gastric electrical vector and the preprocessed brainwave signal; The corresponding marginal probability distribution and joint probability distribution are determined based on the gastric electrical vector and the brain electrical vector; The conditional entropy and joint entropy are determined based on the marginal probability distribution and the joint probability distribution. The joint entropy... and conditional entropy The calculation formula is: in, The marginal probability distribution of the last vector of the gastric electrical vector. The marginal probability distribution representing the last vector of the EEG vector. This represents the joint probability distribution of the last vector and the current value of the gastric electroencephalogram (GEG) vector and the brain electroencephalogram (EEG) vector. Determine the transition entropy based on the conditional entropy and the joint entropy: in, It is the joint entropy of the current value of the EEG vector and the last vector of the Gastrointestinal vector. It is the conditional entropy of the current value of the EEG vector under the condition of the last vector of the given Gastroelectroencephalogram, where M represents the length of the vector.

8. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 4, characterized in that, The analysis module is specifically used for: The gastric electrical frequency is compared with its corresponding normal range. When the gastric electrical frequency is within its normal range, the result is obtained. When the dominant frequency of gastric electrical activity is less than the minimum value of its normal range, It is equal to the difference between the minimum value of the normal range of gastric electrical frequency and the gastric electrical frequency; when the gastric electrical frequency is greater than the maximum value of its normal range, It is equal to the difference between the main frequency of gastric electrical activity and its maximum value within the normal range; The gastric electrical amplitude is compared with its corresponding normal range. When the gastric electrical amplitude is within its normal range, the result is obtained. ; When the gastric electrical amplitude is less than the minimum value of its normal range, It is equal to the difference between the minimum value of the normal range of gastric electrical amplitude and the gastric electrical amplitude; when the gastric electrical amplitude is greater than the maximum value of its normal range, It is equal to the difference between the gastric electrical amplitude and its maximum value within the normal range; The normal proportion of slow gastric waves is compared with its corresponding normal range. When the normal proportion of slow gastric waves is within its normal range, the result is obtained. When the proportion of normal slow waves in the stomach is less than the minimum value of its normal range, It is equal to the difference between the minimum value of the normal range of the proportion of slow gastric waves and the stated proportion of slow gastric waves. When the proportion of normal slow waves in the stomach is greater than its maximum value within the normal range, It is equal to the difference between the proportion of normal slow waves in the stomach and the maximum value of its normal range; Calculate the current intensity of vagal nerve stimulation in the next time period: Where m = 1, 2, 3, This represents the current intensity of the vagus nerve stimulation used in the previous time period. for The weight, This is the gain coefficient.

9. The gastric function regulation system based on gastric rhythm and gastrobrain coupling according to claim 5, characterized in that, The analysis module is also used for: When the gastric electrical frequency, the gastric electrical amplitude, and the proportion of normal slow gastric waves are all within their normal ranges, and the gastrobrain coupling parameters are within their normal ranges, a command is sent to the switching module to turn off the gastric function regulation device. When the phase-amplitude coupling value is detected to be lower than a preset threshold or the transfer entropy is close to 1 or -1, an instruction is sent to the switch module to make the switch module turn off the gastric function regulation device, or a prompt message is issued.

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