Epilepsy prediction system based on synchronously acquired electroencephalogram-electrogastrogram signals

Abstract

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

Description

[0001] This application is a divisional application based on the Chinese invention patent application with application number 202411311041.2, application date September 19, 2024, and invention name “An epilepsy prediction system based on synchronously acquired EEG-EGG signals”. Technical Field

[0002] The present invention relates to the field of disease prediction, and in particular to an epilepsy prediction system based on synchronously collected electroencephalogram (EEG) and electrogastric (EGG) signals. Background Art

[0003] Epilepsy is a chronic neurological disease that affects approximately 70 million people worldwide. It is listed by the World Health Organization as one of the five major neuropsychiatric disorders that require global prevention and treatment, and is also a key public relations area for the Global Brain Science Initiative. Epileptic seizures are caused by abnormal discharges from a group of brain cells, and different parts of the brain can become the site of abnormal discharges. They are characterized by recurrent attacks that begin and end suddenly, and EEG is difficult to capture during the attack. During an epileptic seizure, brief involuntary convulsions often occur in a part of the body or the entire body, sometimes accompanied by loss of consciousness and urinary and fecal incontinence. Seizures range from very brief loss of consciousness or muscle reflexes to severe and persistent convulsions. The frequency of seizures can also vary, ranging from less than once a year to several times a day.

[0004] Currently, the diagnosis of epilepsy is based on clinical seizures and epileptiform discharges on the electroencephalogram (EEG). On the one hand, the assessment of EEG abnormalities can be subjective and not very sensitive. For example, in routine EEG recordings, only 29% to 55% of patients exhibit epileptiform discharges (such as spikes, sharp waves, spike-slow complexes, and spike-slow complexes). On the other hand, a large proportion of epilepsy patients experience seizures of very short duration, making it difficult to capture abnormal discharges even with an EEG. Therefore, current EEG-based technologies can only detect epileptiform discharges in subjects, making them suitable for diagnosing epileptic seizures but difficult to use for epilepsy screening.

[0005] It's reported that up to 70% of epilepsy patients can achieve seizure-free status with appropriate use of antiepileptic drugs. This demonstrates the significant practical significance of "early detection and early treatment" for epilepsy. However, my country has at least 10 million epilepsy patients, yet only approximately one-third receive adequate treatment. The Global Burden of Disease Study estimates that the burden of epilepsy in my country is 1.6 million disability-adjusted life years (DALYs), accounting for 12% of the global burden and 95% of the burden in East Asia. This finding may be due to persistent misunderstandings, fears, and prejudices about epilepsy among patients, leading to a significant number of people in my country being reluctant to seek medical attention even after experiencing a seizure. Furthermore, current epilepsy diagnostics rely on the detection of epileptiform discharges, which can lead to patients not experiencing epileptiform discharges during EEG testing being missed by current technology. Meanwhile, some patients may suffer from non-epileptic seizure disorders (such as neurotic seizures and transient ischemic attacks) but often worry about epilepsy, which can be difficult to distinguish from epilepsy based solely on clinical manifestations. Correct diagnosis is the prerequisite for effective treatment, but existing technology makes it difficult to distinguish between non-epileptic and epileptic patients based solely on the patient's verbal description and electroencephalogram (EEG) without abnormal discharges, especially in larger populations such as communities and physical examination centers.

[0006] Chinese patent application CN117481666A discloses a method for coupling gastric and EEG signals at rest. This method reflects the degree of bidirectional influence between the original gastric and EEG signals by extracting phase-amplitude coupling, phase-phase coupling, and amplitude-amplitude coupling indicators in different frequency bands, as well as transfer entropy. However, its application is limited to regulating gastric function and is difficult to diagnose diseases. Summary of the Invention

[0007] The present invention provides an epilepsy prediction system based on synchronously collected EEG and gastrointestinal electrical signals, which is characterized by comprising: a data acquisition module, configured to acquire data, the data including EEG data and gastrointestinal electrical data, the EEG data being acquired from a first channel and the gastrointestinal electrical data being acquired from a second channel; the data including sample data from a sample population and subject data from a subject; A data preprocessing module, used for preprocessing the data to obtain corresponding time series data; A first analysis module is configured to obtain, based on the time series data and a first model, an average electrode inconsistency index of the first channel and the second channel; the first model includes: ; Where JSD represents the JS divergence scoring function, t represents time, represents the time series data of the corresponding channels of the first channel and the second channel recorded at the time point t seconds of the subject, m represents the total number of channels, n represents the total duration of the time series data of the corresponding channel; ICI represents the inconsistency index of the corresponding channel; a prediction module, configured to compare an average electrode inconsistency index of a subject with a preset evaluation threshold, and when the average electrode inconsistency index of the subject is not lower than the preset evaluation threshold, the subject is predicted to be an epileptic patient; Among them, the first channel includes F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz and POz; the second channel includes the gastric body, lesser curvature of the stomach, gastric antrum, ascending colon, transverse colon, descending colon and rectal passage.

[0008] In some embodiments, the system further includes a second analysis module for converting the time series data into probability density distribution data.

[0009] In some embodiments, the second analysis module is further configured to obtain an inconsistency index of a corresponding channel based on a second model according to the probability density distribution data, where the second model includes: ; Where ICI represents the inconsistency index function, represents the KL divergence function, t represents time, represents the time series data of the corresponding channel recorded at time point t seconds of the subject, represents the time series data of the corresponding channel recorded at the time point t seconds for healthy people, n is the total duration of the time series data of the corresponding channel, represents the sample distribution, represents the reference distribution.

[0010] In some embodiments, the system further includes a database for storing data obtained by the data acquisition module, the data preprocessing module, the first analysis module, the second analysis module and the prediction module, and for generating corresponding sample data sets.

[0011] In some embodiments, the system also includes an evaluation threshold setting module, which randomly obtains the average electrode inconsistency index of 50-70% of the epilepsy patient samples in the sample data set and calculates the critical value for the occurrence of epilepsy, and sets the average electrode inconsistency index corresponding to the minimum critical value as the evaluation threshold.

[0012] In some embodiments, the EEG data and the gastrointestinal electrical data are collected synchronously.

[0013] In some embodiments, the acquisition time of the EEG data and the gastrointestinal electrical data is at least 5 minutes.

[0014] In some embodiments, the preprocessing includes filtering the EEG data to filter out EEG signals below 1 Hz and above 30 Hz, and normalizing the gastrointestinal electrical data and the filtered EEG data to obtain the time series data.

[0015] In some embodiments, the data is collected before or after a meal.

[0016] In some embodiments, the data is collected from a subject in a normal state. As used herein, "normal state" refers to normal brain electrical activity in the subject, i.e., no detectable epileptiform discharges in the subject's EEG. In some embodiments, when the subject is a healthy person (i.e., a non-epileptic patient), the normal state is any state of the subject. In some embodiments, when the subject is an epileptic patient, the normal state is the subject's non-epileptic seizure period (or between epileptic seizures).

[0017] In some embodiments, the subject is an epileptic patient.

[0018] In some embodiments, the epilepsy patient has focal or generalized epilepsy.

[0019] Compared with the prior art, the beneficial effects of the present invention include at least the following aspects: The present invention provides an epilepsy prediction system based on synchronously collected EEG and gastrointestinal electrical signals. Existing methods for processing EEG and gastrointestinal electrical signals involve phase-amplitude coupling, phase-phase coupling, and amplitude-amplitude coupling of EEG and gastrointestinal electrical signals at different frequency bands. There are no reports of epilepsy prediction and diagnosis using EEG and gastrointestinal electrical signals.

[0020] Unlike the conventional approach of extracting frequency bands from EEG signals, the present invention achieves accurate prediction of epilepsy by collecting EEG data and gastrointestinal electrical data corresponding to streamlined EEG channels and gastrointestinal electrical channels (i.e., the first and second channels of the present invention) and combining them with processing methods such as the inconsistency index. It should be emphasized that, on the one hand, the present invention breaks through the general approach of simultaneously selecting channels with high correlation between EEG and gastrointestinal electrical channels. By streamlining the EEG channels with high correlation with gastrointestinal electrical channels and replacing them with specific gastrointestinal electrical channels, channel streamlining is achieved, reducing the actual operational difficulty while improving the accuracy and sensitivity of epilepsy diagnosis. On the other hand, the present invention does not detect epileptic seizures in epileptic patients by detecting whether abnormal discharges appear in the EEG, but rather diagnoses whether a subject has epilepsy based on the EEG (and gastric) of the subject in a normal state.

[0021] In large-scale screening, even experienced clinicians find it difficult to determine whether a subject is an epileptic patient based solely on the subject's verbal description of "epileptic" seizure symptoms, given the tight timeframe and lack of diagnostic evidence. The accuracy and sensitivity of the prediction model obtained based on the streamlined channels and specific algorithms of the present invention are both at a high level. It can capture and amplify the differences between epileptic patients and healthy people who have almost identical EEG activity under conventional methods (such as visual observation), and thus distinguish them. It is suitable for assisting in the diagnosis of subjects who may not have abnormal discharges in the EEG during large-scale screening (such as healthy people who have symptoms similar to epileptic seizures but are not epileptic patients, and epileptic patients whose epileptic seizures are infrequent).

[0022] Based on this, the system provided by the present invention not only requires a short time to collect data, but also can more accurately and efficiently distinguish between epilepsy patients and healthy people. It can be used as an auxiliary diagnostic tool to conduct initial screening of epilepsy patients in larger populations (such as communities and physical examination centers). BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the embodiments or the description of the prior art. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the various elements or parts are not necessarily drawn according to the actual scale. Obviously, the drawings described below are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without inventive work.

[0024] Figure 1 This is a simplified schematic diagram of the positions of the first channel and the second channel of the present invention; Figure 2Result graphs showing the performance of different prediction models obtained for the present invention; Figure 3 A flow chart of the method for analyzing synchronously collected EEG-Gastrointestinal electrical signals provided by the present invention; Figure 4 This is a schematic diagram of the epilepsy prediction system based on synchronously collected EEG-gastrointestinal electrical signals provided by the present invention.

[0025] 100 is a prediction system, 102 is a data acquisition module, 104 is a data preprocessing module, 106 is a first analysis module, 108 is a second analysis module, 110 is a prediction module, 112 is a database, and 114 is an evaluation threshold setting module. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] Herein, suffixes such as "module," "component," or "unit" used to represent elements are only used to facilitate description of the present invention and have no specific meaning. Therefore, "module," "component," or "unit" may be used interchangeably.

[0028] As used herein, terms such as "upper," "lower," "inner," "outer," "front," "back," "one end," and "the other end" indicate positions or locations based on those shown in the accompanying drawings. These terms are intended solely to facilitate and simplify the description of the present invention and are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0029] As used herein, unless otherwise expressly specified or limited, the terms "installed," "provided with," and "connected" should be understood broadly. For example, "connected" may refer to a fixed connection, a detachable connection, or an integral connection; it may refer to a mechanical connection, a direct connection, an indirect connection via an intermediate medium, or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention on a case-by-case basis.

[0030] As used herein, "and / or" includes any and all combinations of one or more of the associated listed items.

[0031] Herein, "plurality" means two or more than two, ie, it includes two, three, four, five, etc.

[0032] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0033] As used in this specification, the term "about" typically means + / - 5% of the stated value, more typically + / - 4% of the stated value, more typically + / - 3% of the stated value, more typically + / - 2% of the stated value, even more typically + / - 1% of the stated value, and even more typically + / - 0.5% of the stated value.

[0034] In this specification, certain embodiments may be disclosed in a format that is within a range. It should be understood that this description of "within a range" is merely for convenience and brevity and should not be interpreted as a rigid limitation on the disclosed range. Therefore, the description of a range should be considered to have specifically disclosed all possible subranges and individual numerical values within this range. For example, the description of a range of 1-6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as individual numbers within this range, such as 1, 2, 3, 4, 5, and 6. Regardless of the breadth of the range, the above rules apply.

[0035] Example 1 1.1 Dataset Subjects: This example included 102 patients with focal or generalized epilepsy (57 males, aged 33.82±12.65 years) and 46 healthy controls (26 males, aged 31.00±8.45 years).

[0036] 1.1.1 Electroencephalogram (EEG) recording Subjects were seated in a quiet room and prepared for testing. Five minutes of resting-state EEG data were obtained from all subjects using 64 Ag / AgCl electrodes (Brain Products, Munich, Germany) placed behind a 10 / 20 system with an impedance of <10 kΩ. EEG data were amplified and collected using an actiCAmp amplifier (BrainVision System Amplifier; Brain Products, Munich, Germany) at a sampling rate of 1000 Hz. Subjects were instructed to fixate their gaze at the center of the screen and to minimize body movement during EEG recording.

[0037] 1.1.2 Gastrointestinal electrical signal (EGG) recording Gastrointestinal electromyographic activity was measured using an 8-channel gastrointestinal electromyograph (XDJ-S8, Hefei Kaili Company, Hefei, China). Participants were instructed to avoid alcohol and spicy or irritating foods for at least three days and to fast for at least 6 hours before the examination. Measurements were performed in the supine position. Four gastric electrodes (reflecting the gastric body, antrum, lesser curvature, and greater curvature) and four intestinal electrodes (reflecting the ascending colon, transverse colon, descending colon, and rectum) were placed on the abdominal skin (Hanjie Co., Shanghai, China). The electrodes were placed at the following locations: gastric body: 3 to 5 cm lateral to the left and 1 cm superior to the midpoint of the line connecting the xiphoid process and the umbilicus; gastric antrum: 2 to 4 cm lateral to the right of the midpoint of the line connecting the xiphoid process and the umbilicus; lesser curvature: 1 / 2 of the way up from the midpoint of the line connecting the xiphoid process and the umbilicus; greater curvature: 1 / 2 of the way down from the midpoint of the line connecting the xiphoid process and the umbilicus. The ascending colon was located at the level of the umbilicus and two to four centimeters to the right; the transverse colon was located one centimeter below the umbilicus; the descending colon was located at the level of the umbilicus and two to four centimeters to the left; and the rectum was located on the back, just below the coccyx. A ground electrode was placed on the inner ankle of the right calf, and a reference electrode was placed on the inner wrist of the right hand. The subjects were instructed to remain silent and not move or speak during the test. Furthermore, after a six-minute pre-meal EGG recording, a functional stress test was performed with a meal of approximately 200 kcal.

[0038] 1.2 Prediction Algorithm Based on Jensen–Shannon Divergence (JSD) 1.2.1 Data Preprocessing The dataset was split into training and test sets in a ratio of 7:3. For independent training, only EEG or EGG signals were used. For EEG-EGG multimodal fusion training, both EEG and EGG signals were used.

[0039] First, the raw EEG signal was filtered using EEGLAB in MATLAB to maintain data integrity by removing signals below 1 Hz and above 30 Hz. Second, the filtered EEG data and the raw EGG signal were normalized. The resulting preprocessed EEG time series or feature matrix of the preprocessed EGG time series is shown below.

[0040] Where m and n represent the dimensions of the feature matrix of EEG signal or EGG signal respectively. m is the total number of electrodes, so the i-th row represents the number of electrodes. The records are recorded at t seconds per second. n is the total duration of the EEG time series signal or EGG time series signal, that is, the entire EEG data or EGG data contains n seconds, so the tth column represents the EEG data or EGG data measured at each electrode at the time point t seconds. Therefore, is the electrode recording at time t seconds EEG signal or EGG signal.

[0041] 1.2.2 Converting preprocessed EEG time series or preprocessed EGG time series into probability density distribution data Convert the data of each electrode into probability density distribution data. In the training set, use the time series data of each electrode To fit the sample distribution , where the mean and standard deviation As sample features. In addition, for the healthy control data in the training set, the time series data of each electrode Fitting to a reference distribution In which the mean and standard deviation As a reference feature for differentiation in, represents the sample distribution, represents the time series data of each electrode of the sample, represents the mean of the sample, represents the standard deviation of the sample.

[0042] in, represents the reference distribution, represents the time series data of each electrode of the reference, represents the mean of the reference, represents the standard deviation of the reference.

[0043] When the difference between the sample feature and the reference feature is small, it means that there is no substantial difference between the sample distribution and the reference distribution, indicating the absence of epilepsy. In contrast, when the difference is quite large, it means that there is a significant difference between the sample distribution and the reference distribution, indicating the presence of epilepsy.

[0044] 1.2.3 Constructing an Inconsistency Index Based on JSD The JS divergence was used to quantify the deviation of the sample distribution from the reference distribution to measure the difference between different states (i.e., epilepsy and healthy controls); the inconsistency index (ICI) was calculated for each electrode to represent the difference between time points.

[0045] in, represents the KL divergence function, represents the sample distribution, represents the reference distribution, represents the time series data of the corresponding channel of the sample recorded at the time point t seconds, Represents the time series data of the corresponding channel of the reference recorded at time point t seconds, t represents time, and T represents the total time ICI stands for inconsistency index function. represents the sample distribution, represents the reference distribution, represents the time series data of the corresponding channel of the sample recorded at the time point t seconds, represents the time series data of the corresponding channel of the reference recorded at time point t seconds, t represents time, and n is the total duration of the time series data of the corresponding channel.

[0046] By calculating the ICI, the fluctuations caused by the reference distribution across all time points are quantified. The average ICI score for each electrode can be used as a quantitative measure to assess the degree of distribution difference over time. When the average ICI score is small, it indicates that there is no substantial difference between the sample distribution of the electrode and the reference distribution of the electrode. Conversely, when the average ICI score is large, it indicates that there is a significant difference between the sample distribution of the electrode and the reference distribution of the electrode.

[0047] 1.2.4 Identification of DNB Electrode Based on the DNB (Dynamic Network Biomarker) theory, which assumes that a known set of molecules signals the pre-disease state of complex diseases before a significant transition, the average ICI score for each electrode is calculated. These scores are then ranked according to the relative importance of key moments. The ICI score for each electrode is then ranked based on the fluctuations caused by the reference distribution across all time points, as determined by the equation above.

[0048] In this embodiment, the selection of DNB electrodes is divided into two different strategies. For EEG training alone, the electrodes ranked in the top 50% according to the highest average score are classified as DNB electrodes.

[0049] For EEG-EGG multimodal fusion training, a total of 78 channels (62 EEG channels and 16 EGG channels) were included. First, the top 30 channels with the highest average scores for each individual were selected. Then, if a channel appeared in more than half of the individuals in the training set, it was used as a DNB electrode. Because different electrodes may be associated with different areas of the brain or stomach, a higher score for a specific electrode across the entire array indicates a significant relationship with epileptic activity.

[0050] 1.2.5 Calculating the JSD of a Global Time Series The average electrode inconsistency index of the global time series is defined as follows: Among them, JSD represents the JS divergence scoring function, t represents time, represents the time series data of the corresponding channel recorded at time point t seconds, m represents the total number of channels, n represents the total duration of the time series data of the corresponding channel; ICI represents the inconsistency index of the corresponding channel.

[0051] The JSD score is used to quantify the distribution differences between all electrodes, which helps to identify any significant deviations between the distributions. Higher scores indicate significant differences between the distributions, and when this difference is exceeded, it indicates that the differences have escalated to a critical level, thereby predicting epilepsy. In order to find the appropriate threshold level for each patient, the following training scenario can be used: 70% of the epilepsy data is used as training data to obtain the threshold. For each epilepsy case in the training dataset, based on the JSD score, the critical value in the DNB theory for each attack can be obtained, and the minimum of these critical values is used as the threshold. When the score at a certain time point exceeds the threshold, the subject is diagnosed with epilepsy.

[0052] 1.3 Correlation Analysis between EEG and EGG Channels The probability distribution of each channel was estimated by analyzing the 62-channel EEG data and 16-channel EGG data of each individual according to the following equation: The density function P( ) and P( ) were used to calculate the P value. Correlations between EEG and EGG channels were determined by comparing their P values. A P value less than 0.05 indicated no significant difference between the two, thus supporting the hypothesis of a link. To identify the EEG channels with the strongest correlations with EGG channels, the first three EEG channels in each sample with the least statistical deviation from the distribution of the 16 EGG channels were selected.

[0053] Example 2 It should be noted that, in the present invention, the selection of DNB electrodes is performed in two different ways. For individual EEG training, the electrodes with the highest average ICI scores in the top 50% are selected as DNB electrodes based on the total number of electrodes used. For EEG-EGG multimodal fusion training, the top 30 channels with the highest average ICI scores for each individual in the training set are selected. If a channel appears in more than half of the training sets, it is designated as a common DNB electrode for predicting epilepsy. The results are shown in Figure 2. Figure 1 As shown, the 30 channels obtained by the present invention include: 23 EEG channels (first channel): F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz and POz; 7 gastrointestinal channels (second channel): gastric body channel (i.e., electrode 1), lesser curvature of the stomach channel (i.e., electrode 2), gastric antrum (i.e., electrode 4), ascending colon channel (i.e., electrode 5), transverse colon channel (i.e., electrode 6), descending colon channel (i.e., electrode 7) and rectal channel (i.e., electrode 8).

[0054] Figure 2 In the example, EEG represents a prediction model obtained by training data collected from 62 EEG channels using the method of the present invention; * = indicates a prediction model trained using the method of the present invention and data collected from the top 50% of electrodes with the highest average ICI scores in 62 EEG channels; EEG+EGG indicates a prediction model trained using the method of the present invention and data collected from 78 channels (i.e., 62 EEG channels and 16 EGG channels); EEG+EGG * It represents a prediction model obtained by training with the data collected from the first channel and the second channel using the method of the present invention.

[0055] like Figure 2As shown, the prediction model generated by processing EEG or EEG+EGG data using the method of the present invention has shown good predictive ability. However, it requires collecting and calculating more data, and the accuracy is difficult to reach 95%. However, the prediction model constructed based on the simplified electrodes obtained by the present invention not only requires fewer electrodes to be placed, but also improves the performance of epilepsy prediction, with an accuracy of 98.92% and a sensitivity of 98.82%.

[0056] Correlation analysis between EEG channels and EGG channels.

[0057] This example further explored the correlation between different EEG channels and EGG channels in predicting epilepsy through probability distribution. Specifically, this example analyzed the probability distribution of 62 EEG channels and 16 EGG channels for each subject. In 148 samples, the frequency of occurrence of 62 EEG channels was statistically similar to that of 16 EGG channels. This example then extracted the top three EEG channels associated with each EGG channel in the subject, which were defined as key correlates of a specific EGG channel.

[0058] The distributions of all gastrointestinal electroencephalogram (GEG) and EEG channels were compared for similarity, and the number of occurrences of eight GEG leads that showed no significant difference in distribution from the EEG channels was calculated across the 148 subjects. A higher number of occurrences indicates a higher correlation between the EEG leads and GEG. This example determined that the top 20% of EEG channels showed no statistically significant distribution differences compared to the 16 EGG channels and designated them as highly correlated EEG channels, as shown in Table 1.

[0059] Table 1: Statistical analysis of the frequency of the top 20% EEG channels related to gastrointestinal electroencephalography in 148 sample results The results show that most of the EEG channels shown in Table 1 are not included in the first channel. Based on this, the present invention believes that the predictive effect of most of the EEG channels shown in Table 1 on epilepsy can be replaced by related gastrointestinal electrical channels (i.e., the above-mentioned second channel).

[0060] Example 3 See also Figure 3 The present invention provides a method for analyzing synchronously collected EEG-Gastrointestinal electrical signals, comprising the following steps: S101 inputs the subject's synchronously collected EEG data and gastrointestinal electrical data; In some embodiments, the EEG data is collected from a first channel, and the gastrointestinal electrical data is collected from a second channel.

[0061] In the present invention, "channel", "electrode" and "lead" have the same meaning unless otherwise specified.

[0062] In some embodiments, the first channel includes F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz and POz; the second channel includes the body, lesser curvature, antrum, ascending colon, transverse colon, descending colon and rectal passage.

[0063] It should be noted that the first channel and the second channel are respectively the simplified EEG channel and gastrointestinal EEG channel of the present invention, and the first channel and the second channel have low correlation in epilepsy.

[0064] Unlike existing techniques that couple gastrointestinal electrical signals with EEG signals, the present invention, after obtaining EEG channels with strong correlations with gastrointestinal electricity in epileptic patients, excludes these EEG channels from the actual EEG channels to be collected, thereby obtaining the aforementioned first channel. The present invention discovered that the predictive function of these EEG channels can be replaced by related gastrointestinal electrical channels (i.e., the aforementioned second channel). By using the aforementioned first and second channels, which have a lower correlation in predicting epilepsy, not only does the difficulty of the acquisition operation decrease, but it also improves the accuracy of predictions for epilepsy patients, making it more practical. Using a lower number of EEG channels (e.g., conventional 16-lead or 32-lead) is insufficient to cover the entire brain, making epilepsy prediction difficult.

[0065] In some embodiments, the acquisition time of the EEG data and the gastrointestinal electrical data is at least 5 minutes.

[0066] In some embodiments, the data is collected before or after a meal.

[0067] In some embodiments, the data is collected from subjects in a normal state.

[0068] In some embodiments, the subject is an epileptic patient.

[0069] In some embodiments, the epilepsy patient has focal or generalized epilepsy.

[0070] S102 preprocessing the synchronously collected EEG data and gastrointestinal electrical data to obtain corresponding time series data; In some embodiments, the preprocessing includes filtering the EEG data to filter out EEG signals below 1 Hz and above 30 Hz, and normalizing the gastrointestinal electrical data and the filtered EEG data to obtain the time series data.

[0071] S103, based on the first model, obtaining an average electrode inconsistency index of the first channel and the second channel; In some embodiments, the first model comprises: ; Where JSD represents the JS divergence scoring function, t represents time, represents the time series data of the corresponding channels of the first channel and the second channel recorded at the time point t seconds of the subject, m represents the total number of channels, n represents the total duration of the time series data of the corresponding channels; ICI represents the inconsistency index of the corresponding channel.

[0072] In some embodiments, the corresponding channels include F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz, POz, gastric body channel, gastric lesser curvature channel, gastric antrum channel, ascending colon channel, transverse colon channel, descending colon channel, and rectal channel. In some embodiments, the total duration refers to the total duration of the entire synchronously collected EEG data and gastrointestinal electrical data. For example, when the acquisition time is 5 minutes, the total duration is 5 minutes.

[0073] Based on the above-mentioned first channel and second channel, the present invention not only reduces the difficulty of the acquisition operation, but also reduces the amount of data that needs to be analyzed and calculated, improves the efficiency of the calculation, and maintains a high level of accuracy and sensitivity.

[0074] In some embodiments, the method further includes S201 converting the time series data into probability density distribution data.

[0075] In some embodiments, the method further includes S202 obtaining an inconsistency index of a corresponding channel based on a second model according to the probability density distribution data, where the second model includes: ; Where ICI represents the inconsistency index function, represents the KL divergence function, t represents time, represents the time series data of the corresponding channel recorded at time point t seconds of the subject, represents the time series data of the corresponding channel recorded at the time point t seconds for healthy people, n is the total duration of the time series data of the corresponding channel, represents the sample distribution, represents the reference distribution.

[0076] S104: Comparing the average electrode inconsistency index of the subject with a preset evaluation threshold; when the average electrode inconsistency index of the subject is not lower than the preset evaluation threshold, the subject is predicted to be an epileptic patient.

[0077] In some embodiments, the method for obtaining the preset evaluation threshold includes S203 randomly obtaining the average electrode inconsistency index of 50-70% of the cognitive impairment patient samples in the sample data set and calculating the critical value of their epilepsy occurrence (based on DNB theory), and setting the average electrode inconsistency index corresponding to the minimum critical value as the evaluation threshold.

[0078] Example 4 See also Figure 4 The present invention provides an epilepsy prediction system 100 based on synchronously collected EEG-gastrointestinal electrical signals, characterized by comprising: A data acquisition module 102 is used to acquire data, including EEG data and gastrointestinal electrical data; In some embodiments, the EEG data and the gastrointestinal electrical data are collected synchronously.

[0079] In some embodiments, the acquisition time of the EEG data and the gastrointestinal electrical data is at least 5 minutes.

[0080] In some embodiments, the data is collected before or after a meal.

[0081] In some embodiments, the EEG data is collected from a first channel, and the gastrointestinal electrical data is collected from a second channel.

[0082] In some embodiments, the data includes sample data from a sample population and subject data from a subject.

[0083] In some embodiments, the sample population includes healthy people and epilepsy patients.

[0084] In some embodiments, the data is collected from subjects in a normal state.

[0085] In some embodiments, the subject is an epileptic patient.

[0086] In some embodiments, the epilepsy patient has focal or generalized epilepsy.

[0087] In some embodiments, the first channel includes F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz and POz; the second channel includes the body, lesser curvature, antrum, ascending colon, transverse colon, descending colon and rectal passage.

[0088] A data preprocessing module 104 is used to preprocess the data to obtain corresponding time series data; In some embodiments, the preprocessing includes filtering the EEG data to filter out EEG signals below 1 Hz and above 30 Hz, and normalizing the gastrointestinal electrical data and the filtered EEG data to obtain the time series data.

[0089] A first analysis module 106 is configured to obtain, based on the time series data and a first model, an average electrode inconsistency index of the first channel and the second channel; In some embodiments, the first model comprises: ; Where JSD represents the JS divergence scoring function, t represents time, represents the time series data of the corresponding channels of the first channel and the second channel recorded at the time point t seconds of the subject, m represents the total number of channels, n represents the total duration of the time series data of the corresponding channels; ICI represents the inconsistency index of the corresponding channel.

[0090] The second analysis module 108 is configured to convert the time series data into probability density distribution data.

[0091] In some embodiments, the second analysis module 108 is further configured to obtain an inconsistency index of each sub-channel based on a second model according to the probability density distribution data.

[0092] In some embodiments, the second model comprises: ; Where ICI represents the inconsistency index function, represents the KL divergence function, t represents time, represents the time series data of the corresponding channel recorded at time point t seconds of the subject, represents the time series data of the corresponding channel recorded at the time point t seconds for healthy people, n is the total duration of the time series data of the corresponding channel, represents the sample distribution, represents the reference distribution.

[0093] In some embodiments, the first analysis module 106 and the second analysis module 108 can be combined into an analysis module.

[0094] A prediction module 110 is configured to compare the average electrode inconsistency index of the subject with a preset evaluation threshold, and when the average electrode inconsistency index of the subject is not lower than the preset evaluation threshold, the subject is predicted to be an epileptic patient; The database 112 is used to store the data obtained by the data acquisition module 102, the data preprocessing module 104, the first analysis module 106, the second analysis module 108 and the prediction module 110, and to generate a corresponding sample data set.

[0095] In some embodiments, the sample dataset includes a healthy population dataset and an epilepsy patient dataset.

[0096] The evaluation threshold setting module 114 randomly obtains the average electrode inconsistency index of 50-70% of the epilepsy patient samples in the epilepsy sample data set and calculates the critical value for the occurrence of epilepsy, and sets the average electrode inconsistency index corresponding to the minimum critical value as the evaluation threshold.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0098] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. An epilepsy prediction system based on synchronously collected EEG and gastrointestinal electrical signals, characterized in that: include: a data acquisition module, configured to acquire data, the data including EEG data and gastrointestinal electrical data, the EEG data being acquired from a first channel and the gastrointestinal electrical data being acquired from a second channel; the data including sample data from a sample population and subject data from a subject; A data preprocessing module, used for preprocessing the data to obtain corresponding time series data; a first analysis module, configured to obtain, based on the time series data and a first model, an average electrode inconsistency index of the first channel and the second channel; The first model includes: ; Where JSD represents the JS divergence scoring function, t represents time, represents the time series data of the corresponding channels of the first channel and the second channel recorded at the time point t seconds of the subject, m represents the total number of channels, n represents the total duration of the time series data of the corresponding channel; ICI represents the inconsistency index of the corresponding channel; a prediction module, configured to compare an average electrode inconsistency index of a subject with a preset evaluation threshold, and when the average electrode inconsistency index of the subject is not lower than the preset evaluation threshold, the subject is predicted to be an epileptic patient; Among them, the first channel includes F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz and POz; the second channel includes the gastric body, lesser curvature of the stomach, gastric antrum, ascending colon, transverse colon, descending colon and rectal passage.

2. The system according to claim 1, wherein The system further includes a second analysis module for converting the time series data into probability density distribution data.

3. The system according to claim 2, wherein: The second analysis module is further configured to obtain an inconsistency index of a corresponding channel based on a second model according to the probability density distribution data, wherein the second model includes: ; Where ICI represents the inconsistency index function, represents the KL divergence function, t represents time, represents the time series data of the corresponding channel recorded at time point t seconds of the subject, represents the time series data of the corresponding channel recorded at the time point t seconds for healthy people, n is the total duration of the time series data of the corresponding channel, represents the sample distribution, represents the reference distribution.

4. The system according to claim 1, wherein: The EEG data and the gastrointestinal electrical data are collected synchronously.

5. The system according to claim 1, wherein: The acquisition time of the EEG data and the gastrointestinal electrical data is at least 5 minutes.

6. A method for analyzing synchronously collected EEG-gastrointestinal electrical signals, characterized in that: The following steps are involved: S101 inputs synchronously collected EEG data and gastrointestinal electrical data of the subject; the EEG data is collected from the first channel, and the gastrointestinal electrical data is collected from the second channel; the first channel includes F3, C4, P4, O2, T7, FC1, CP1, CP6, TP9, TP10, F2, C1, C2, P2, FC3, FC4, CP3, CP4, PO4, P6, PO8, CPz, and POz; the second channel includes the gastric body, lesser curvature of the stomach, gastric antrum, ascending colon, transverse colon, descending colon, and rectal passage; S102 preprocessing the synchronously collected EEG data and gastrointestinal electrical data to obtain corresponding time series data; S103, based on the first model, obtaining an average electrode inconsistency index of the first channel and the second channel; The first model includes: ; Where JSD represents the JS divergence scoring function, t represents time, represents the time series data of the corresponding channels of the first channel and the second channel recorded at the time point t seconds of the subject, m represents the total number of channels, n represents the total duration of the time series data of the corresponding channel; ICI represents the inconsistency index of the corresponding channel; S104: Comparing the average electrode inconsistency index of the subject with a preset evaluation threshold; when the average electrode inconsistency index of the subject is not lower than the preset evaluation threshold, the subject is predicted to be an epileptic patient.

7. The method according to claim 6, wherein The method further includes S201 converting the time series data into probability density distribution data.

8. The method according to claim 7, wherein The method further includes S202 obtaining an inconsistency index of a corresponding channel based on a second model according to the probability density distribution data, where the second model includes: ; Where ICI represents the inconsistency index function, represents the KL divergence function, t represents time, represents the time series data of the corresponding channel recorded at time point t seconds of the subject, represents the time series data of the corresponding channel recorded at the time point t seconds for healthy people, n is the total duration of the time series data of the corresponding channel, represents the sample distribution, represents the reference distribution.

9. The method according to claim 6, wherein The data are collected before or after a meal.

10. The method according to claim 6, wherein The data were collected from subjects in normal condition.

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