A cognitive impairment prediction system based on synchronous acquisition of electroencephalogram - electrogastrogram signals

Abstract

The present invention relates to the field of disease prediction, and particularly to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-electrogastrography signals, comprising: a data acquisition module for acquiring data, where the data includes electroencephalogram data and electrogastrogram data; a first analysis module for obtaining the average electrode inconsistency index of a first channel and a second channel based on a first model according to time series data; and a prediction module for comparing the average electrode inconsistency index of a subject with a preset evaluation threshold so as to predict patients with cognitive impairment. The system provided by the present invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnostic tool for primary screening of mild cognitive impairment in a large population (such as communities, physical examination centers).

Description

Technical Field

[0001] The present invention relates to the field of disease prediction, and particularly to a cognitive impairment prediction system based on synchronously collected electroencephalogram-electrogastrography signals. Background Art

[0002] Cognitive impairment (CI) refers to a class of diseases or symptoms in which the higher functions related to the cerebral cortex, including attention, memory, learning ability, language ability, etc., are impaired. Its pathogenic factors are relatively complex, including neurodegenerative diseases, cerebrovascular diseases, and mental diseases, as well as drugs that affect cognition (such as opioid drugs). Cognitive impairment may have a serious impact on the daily life and working ability of patients, thus significantly reducing the quality of life of patients. With the growth of the population and the intensification of social aging, the proportion of the high-incidence age group of nervous system diseases in the total population is increasing day by day. A variety of comorbidities including cognitive impairment in nervous system diseases have thus become an increasingly serious global health challenge. In the past 30 years, the number of patients with cognitive impairment has increased by 117%, and the comprehensive prediction model shows that the number of patients with cognitive impairment in 2050 will be three times that of the present.

[0003] In the prior art, the diagnostic methods for cognitive impairment diseases include basic clinical symptom assessment, neuropsychological state assessment, and imaging examinations such as computed tomography (CT), magnetic resonance imaging (MRI), and single photon emission computed tomography (SPECT). The examinations based on scale measurement are relatively subjective and require doctors to have highly professional knowledge. The examinations based on neuroimaging, such as high-precision magnetic resonance imaging and positron emission tomography, require expensive equipment. Due to the high examination costs and the lack of medical resources, many patients with mild cognitive impairment cannot be diagnosed and effectively intervened in a timely manner, which makes the mild symptoms more likely to deteriorate.

[0004] Chinese Patent Application CN117481666A discloses a method for coupling electrogastroencephalogram signals in a resting state, which reflects the degree of bidirectional influence between the electrogastrogram raw signal and the electroencephalogram raw signal by extracting the phase-amplitude coupling, phase-phase coupling, and amplitude-amplitude coupling indexes, as well as transfer entropy between the electrogastrogram raw signal and the electroencephalogram raw signal in different frequency bands. However, its application scenario is limited to regulating gastric function and it is difficult to achieve the diagnosis of diseases. Summary of the Invention

[0005] The present invention provides a cognitive impairment prediction system based on synchronously collected electroencephalogram-electrogastrointestinal signals, which is characterized by including:

[0006] A data acquisition module for acquiring data, where the data includes electroencephalogram data and electrogastrogram data. The electroencephalogram data is collected from a first channel, and the electrogastrogram data is collected from a second channel; the data includes sample data from a sample population and subject data from a subject.

[0007] A data preprocessing module for preprocessing the data to obtain corresponding time series data.

[0008] A first analysis module for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time series data; the first model includes: where JSD represents the JS divergence scoring function, t represents time, and a it represents the time series data of the corresponding channels of the first channel and the second channel recorded by the subject at the time point t seconds, m represents the total number of channels, and n represents the total duration of the time series data of the corresponding channels.

[0009] ICI represents the inconsistency index of the corresponding channel.

[0010] A prediction module for comparing the average electrode inconsistency index of a 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 a patient with cognitive impairment.

[0011] where the first channel includes F7, F9, FC5, C3, CP5, CP1, Oz, P10, P4, CP2, C4, FC2, FC6, F10, F1, CP3, PO7, PO4, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, F2, AF4, and AF8; the second channel includes the gastric body channel, the lesser curvature of the stomach channel, the ascending colon channel, the descending colon channel, and the rectal channel.

[0012] In some embodiments, the system further includes a second analysis module for converting the time series data into probability density distribution data.

[0013] In some embodiments, the second analysis module is further configured to obtain an inconsistency index of the corresponding channel based on a second model according to the probability density distribution data. The second model includes:

[0014] where ICI represents the inconsistency index function, and D KL represents the KL divergence function, t represents time, and a it represents the time series data of the corresponding channel recorded by the subject at the time point t seconds, and bit represents the time - series data of the corresponding channel recorded at time point t seconds for healthy individuals, n is the total duration of the time - series data of the corresponding channel, P(a it ) represents the sample distribution, Q(b it ) represents the reference distribution.

[0015] In some embodiments, the system further includes a database for storing the data obtained by the data acquisition module, the data pre - processing module, the first analysis module, and the second analysis module, and for generating corresponding sample data sets.

[0016] In some embodiments, the system further includes an evaluation threshold setting module. The evaluation threshold formulation module randomly selects 50 - 70% of the sample of patients with cognitive impairment in the sample data set, calculates the critical value of the onset of cognitive impairment based on the average electrode inconsistency index, and sets the average electrode inconsistency index corresponding to the smallest critical value as the evaluation threshold.

[0017] In some embodiments, the electroencephalogram data and the electrogastrogram data are synchronously collected.

[0018] In some embodiments, the collection time of the electroencephalogram data and the electrogastrogram data is at least 5 minutes.

[0019] In some embodiments, the cognitive impairment is mild cognitive impairment.

[0020] In some embodiments, the pre - processing includes filtering the electroencephalogram data to filter out electroencephalogram signals below 1 Hz and above 30 Hz, and normalizing the electrogastrogram data and the filtered electroencephalogram data to obtain the time - series data.

[0021] In some embodiments, the data is collected before or after a meal.

[0022] Compared with the prior art, the beneficial effects of the present invention at least include the following aspects:

[0023] The present invention provides a cognitive impairment prediction system based on synchronously collected electroencephalogram - electrogastrogram signals. The prior art methods for processing electroencephalogram signals and electrogastrogram signals are to perform phase - amplitude coupling, phase - phase coupling, and amplitude - amplitude coupling on electroencephalogram signals and electrogastrogram signals in different frequency bands. There has been no report in the prior art on using electroencephalogram signals and electrogastrogram signals for the prediction and diagnosis of cognitive impairment (or cognitive dysfunction).

[0024] Different from the conventional idea of extracting frequency bands from electroencephalogram signals in the prior art,

[0025] The present invention realizes the accurate prediction of mild cognitive impairment by collecting electroencephalogram data and electrogastrogram data corresponding to the streamlined electroencephalogram channels and electrogastrogram channels (i.e., the first channel and the second channel of the present invention) and combining processing means such as the inconsistency index. It should be emphasized that this application breaks through the general idea of simultaneously selecting channels with relatively high correlation in electroencephalogram channels and electrogastrogram channels. By streamlining the channels with relatively high correlation with electrogastrogram channels in electroencephalogram channels and replacing them with specific electrogastrogram channels, the streamlining of channels is realized, the actual operation difficulty is reduced, and the accuracy and sensitivity of diagnosing mild cognitive impairment are improved.

[0026] Based on this, the system provided by the present invention not only requires a short data acquisition time,

[0027] but also can more accurately and efficiently distinguish mild cognitive impairment patients from healthy people, and can be used as an auxiliary diagnostic tool for primary screening of mild cognitive impairment patients in a large population (such as communities, physical examination centers). BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art,

[0029] the following will briefly introduce the drawings required for the description of the embodiments or the prior art. In all the drawings, similar elements or parts are generally denoted by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is a schematic diagram of the positions of the streamlined first channel and second channel of the present invention;

[0031] Figure 2 It is a result diagram of the performance of different prediction models obtained by the present invention;

[0032] Figure 3 It is a flowchart of the analysis method for synchronously collected electroencephalogram-electrogastrogram signals provided by the present invention;

[0033] Figure 4 It is a schematic diagram of the cognitive impairment prediction system based on synchronously collected electroencephalogram-electrogastrogram signals provided by the present invention.

[0034] 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 OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] In this document, suffixes such as "module", "component", or

[0037] "unit" used to represent elements are only for the convenience of describing the present invention, and they have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0038] In this document, the terms "upper", "lower", "inner", "outer", "front",

[0039] "rear", "one end", "the other end", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0040] In this document, unless otherwise clearly defined and limited, the terms "installed", "provided with", "connected", etc. shall be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0042] In this document, "a plurality of" means two or more, that is, it includes two, three, four, five, etc.

[0043] It should be noted that in this document, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article, or device. Without further limitations, by the statement

[0044] “Comprising a…” defined element does not exclude the existence of other identical elements in the process, method, article or device comprising the element.

[0045] 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.

[0046] In this specification, some embodiments may be disclosed in a format of being in a certain range. It should be understood that such descriptions of "being in a certain range" are 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 sub-ranges and independent numerical values ​​within this range. For example, the description of a range of 1-6 should be considered to have specifically disclosed sub-ranges 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,

[0047] 2, 3, 4, 5 and 6. The above rules apply regardless of the breadth of the range.

[0048] Embodiment 1

[0049] 1.1 Dataset

[0050] Subjects: This example included 108 healthy controls (66 males,

[0051] Age 66.77±6.39 years) and 82 patients with mild cognitive impairment (including 45 males, age 65.27±6.98 years).

[0052] 1.1.1 EEG recording

[0053] Subjects were seated in a quiet room and prepared for the test. Five-minute 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 <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 fix their gaze at the center of the screen and to avoid body movements as much as possible during the EEG recording.

[0054] 1.1.2 Gastrointestinal Electrical Signal (EGG) Recording

[0055] The gastrointestinal myoelectric activity was measured using an 8-channel gastrointestinal electromyograph (XDJ-S8, Hefei Kaili Co., Ltd., Hefei, China). The subjects were informed to avoid alcohol and spicy or stimulating foods for at least three days and fast for more than 6 hours before the examination. The measurement was performed in the supine position. Four gastric electrodes (reflecting the gastric body, gastric antrum, lesser curvature of the stomach, and greater curvature of the stomach) and four intestinal electrodes (reflecting the ascending colon, transverse colon, descending colon, and rectum) were placed on the abdominal skin

[0056] (Hanjie Co., Shanghai, China). The specific placement sites of the above electrodes are as follows: Gastric body: 3 to 5 cm to the left and 1 cm upward from the midpoint of the line connecting the xiphoid process and the umbilicus; Gastric antrum:

[0057] 2 to 4 cm to the right of the midpoint of the line connecting the xiphoid process and the umbilicus; Lesser curvature of the stomach: at the upper 1 / 2 of the midpoint of the line connecting the xiphoid process and the umbilicus; Greater curvature of the stomach: at the lower 1 / 2 of the midpoint of the line connecting the xiphoid process and the umbilicus. Ascending colon: 2 to 4 cm to the right at the level of the umbilicus; Transverse colon: 1 cm below the umbilicus; Descending colon: 2 to 4 cm to the left at the level of the umbilicus; Rectum: below the coccyx of the back. The ground electrode was placed on the medial ankle of the right calf, and the reference electrode was placed on the medial wrist of the right hand. The subjects were informed to keep quiet during the test and not to move or speak. In addition, after the EGG recording 6 minutes before the meal, a meal-time functional load test was performed with about 200 kcal of food

[0058] 1.2 Prediction Algorithm Based on Jensen–Shannon Divergence (JSD)

[0059] 1.2.1 Data Preprocessing

[0060] The dataset was divided into a training set and a test set at a ratio of 7:3. For independent training, only EEG or EGG signals were used. For EEG-EGG multimodal fusion training, EEG and EGG signals were used

[0061] First, the original EEG signals were filtered using EEGLAB in MATLAB

[0062] to maintain data integrity by eliminating signals below 1 Hz and above 30 Hz. Second, the filtered EEG data and the original EGG signals were normalized. The feature matrices of the preprocessed EEG time series or the preprocessed EGG time series are as follows

[0063]

[0064] where m and n represent the dimensions of the feature matrices of the EEG signals or EGG signals, respectively. m is the total number of electrodes, so the i-th row represents electrode ei Recordings per second. n is the total duration of the EEG time series signal or the EGG time series signal, that is, the entire EEG data or EGG data contains n seconds. Therefore, the t-th column represents the EEG data or EGG data measured at each electrode at the time point of t seconds. Thus, a it is the EEG signal or EGG signal of electrode e i recorded at the time point of t seconds.

[0065] 1.2.2 Convert the preprocessed EEG time series or the preprocessed EGG time series into probability density distribution data

[0066] Convert the data of each electrode into probability density distribution data. In the training set,

[0067] Use the time series data a it of each electrode to fit the sample distribution P(a it ), where the mean u1 i and the standard deviation σ1 i are used as sample features. In addition, for the healthy control data in the training set, the time series data b it of each electrode is fitted into the reference distribution Q(b it ), where the mean u2 i and the standard deviation σ2 i are used as the reference features for discrimination.

[0068]

[0069] Among them, P(a it ) represents the sample distribution, a it represents the time series data of each electrode of the sample, u1 i represents the mean of the sample, and σ1 i represents the standard deviation of the sample.

[0070]

[0071] Among them, Q(b it ) represents the reference distribution, b it represents the time series data of each electrode of the reference, u2 i represents the mean of the reference, and σ2 i represents the standard deviation of the reference.

[0072] When the difference between the sample features and the reference features is very small, it means that there is no substantial difference between the sample distribution and the reference distribution, thus indicating no cognitive dysfunction. On the contrary, 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 cognitive dysfunction.

[0073] 1.2.3 Construction of Inconsistency Index Based on JSD

[0074] JS divergence is used to quantify the deviation of the sample distribution from the reference distribution to measure the differences between different states (i.e., cognitive impairment and healthy control); the Inconsistency Index (ICI) of each electrode is calculated to represent the differences between time points.

[0075]

[0076] Among them, D KL represents the KL divergence function, P(a it ) represents the sample distribution, Q(b it ) represents the reference distribution, a it represents the time series data of the corresponding channel of the sample recorded at time point t seconds, b it 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.

[0077]

[0078] ICI represents the inconsistency index function, P(a it ) represents the sample distribution, Q(b it ) represents the reference distribution, a it represents the time series data of the corresponding channel of the sample recorded at time point t seconds, b it 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.

[0079] By calculating the ICI, the fluctuations at all time points caused by the reference distribution are quantified.

[0080] The average ICI score of each electrode can be used as a quantitative measurement to evaluate 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. On the contrary, 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.

[0081] 1.2.4 Identification of DNB Electrodes

[0082] Based on the DNB (Dynamic Network Biomarker) theory, it is assumed that a set of known molecules signals the pre-disease state of complex diseases before a significant transition, and the average ICI score of each electrode is calculated. Subsequently, the above scores are ranked according to the relative importance at the critical moment. The fluctuations caused by the reference distribution between each electrode obtained according to the above equation at all time points are then used to rank the ICI scores of each electrode.

[0083] In this embodiment, the selection of DNB electrodes is divided into two different strategies. For the individual training of EEG, the electrodes ranked in the top 50% according to the highest average score are classified as DNB electrodes.

[0084] For the EEG-EGG multimodal fusion training, a total of 78 channels are included (62 EEG channels and 16 EGG channels). First, the top 35 channels with the highest average score for each individual are selected. Then, if a certain channel appears in more than half of the individuals in the training set, it is used as a DNB electrode. Since different electrodes may be associated with different regions of the brain or stomach, the higher the score of a specific electrode in the entire array, the more important its relationship with cognitive impairment activities.

[0085] 1.2.5 Calculate the JSD of the global time series

[0086] The average electrode inconsistency index of the global time series is defined as follows:

[0087]

[0088] where JSD represents the JS divergence scoring function, t represents time, a it 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.

[0089] The JSD score is used to quantify the distribution differences between all electrodes, thus helping to identify any significant deviations between the distributions. The higher the score, the more obvious the differences between the distributions. When the difference exceeds this value, it indicates that the difference has escalated to a critical level, thus predicting cognitive impairment. To find an appropriate threshold level for each patient, the following training scenario can be adopted: 70% of the mild cognitive impairment (MCI) data is used as training data to obtain the threshold. For each MCI case in the training data set, according to the JSD score, the critical value in the DNB theory for each episode can be obtained, and the minimum value of these critical values is used as the threshold. When the score at a certain time point exceeds this threshold, the subject is diagnosed with cognitive impairment.

[0090] 1.3 Correlation analysis of EEG channels and EGG channels

[0091] According to the following equation, the probability distribution of each channel is evaluated by analyzing the EEG data of 62 channels and the EGG data of 16 channels for each individual:

[0092]

[0093] The mean and standard deviation of the density functions P(a it ) and P(a it ) obtained from the EEG and EGG channels are used to calculate the P-value. The correlation between the EEG and EGG channels is determined by comparing the magnitudes of their P-values. A P-value less than 0.05 indicates no significant difference between the two, thus supporting the hypothesis of a connection between them. To identify the EEG channels most strongly correlated with the EGG channels, the top three EEG channels with the smallest statistical deviation from the distribution of the 16 EGG channels in each individual were selected.

[0094] Example 2

[0095] It should be noted that in the present invention, the DNB electrodes are selected in two different ways. For individual EEG training, the top 50% of the electrodes with the highest average ICI score are selected as DNB electrodes according to the total number of electrodes used. For EEG-EGG multi-modal fusion training, the top 35 channels with the highest average ICI score for each individual in the training set are selected. If a channel appears in more than half of the training set, it is designated as a common DNB electrode for predicting mild cognitive impairment. The results are as Figure 1 shown. The 35 channels obtained by the present invention include: 30 EEG channels (the first channel):

[0096] F7, F9, FC5, C3, CP5, CP1, Oz, P10, P4, CP2, C4, FC2, FC6, F10, F1, CP3, PO7, PO4, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, F2, AF4, and AF8; and 5 EGG channels (the second channel):

[0097] The corpus gastricum channel (i.e., electrode 1), the lesser curvature of the stomach channel (i.e., electrode 2), the ascending colon channel (i.e., electrode 5), the descending colon channel (i.e., electrode 7), and the rectal channel (i.e., electrode 8).

[0098] Figure 2 Among them, EEG represents a prediction model trained using the method of the present invention with data collected from 62 EEG channels; EEG *denotes a prediction model trained using the method of the present invention with data collected from the top 50% of electrodes with the highest average ICI scores among 62 EEG channels; EEG+EGG denotes a prediction model trained using the method of the present invention with data collected from 78 channels (i.e., 62 EEG channels and 16 EGG channels); EEG+EGG * denotes a prediction model trained using the method of the present invention with data collected from the first channel and the second channel.

[0099] As Figure 2 shown, the prediction models obtained by processing EEG or EEG+EGG data using the method of the present invention have already shown good prediction ability. However, the present invention finds that the accuracy and sensitivity of the prediction model based on the EEG electrodes obtained based on the DNB theory (i.e., the top 50% of electrodes with the highest average ICI scores among 62 EEG channels) instead decrease. The prediction model constructed based on the electrodes streamlined according to the present invention not only requires a reduced number of electrodes to be placed, but also improves the performance of predicting mild cognitive impairment, with a sensitivity of 96.77% observed.

[0100] In addition, the accuracy of the five-fold cross-validation of the prediction model constructed based on the electrodes streamlined according to the present invention is 76.84%, and the sensitivity is 86.86%, thus strongly affirming the robustness and generalization ability of the prediction model of the present invention.

[0101] Correlation analysis between EEG channels and EGG channels.

[0102] In this embodiment, the probability distribution is further used to explore whether there is a certain correlation between different EEG channels and EGG channels in predicting mild cognitive impairment. Specifically, the probability distributions of 62 EEG channels and 16 EGG channels of each subject were analyzed in this embodiment. Among 190 samples, the occurrence frequencies of 62 EEG channels were statistically similar to those of 16 EGG channels. Furthermore, the top three EEG channels related to each EGG channel in the subjects were extracted in this embodiment, and these channels were defined as the key relevant factors of specific EGG

[0103] channels.

[0104] The distributions of all gastrointestinal electrical and electroencephalogram channels were compared for similarity, and the number of occurrences of eight gastrointestinal electrical leads that did not have a significant difference from the electroencephalogram channel distribution among 190 subjects was calculated. The more times, the higher the correlation between the electroencephalogram lead and the gastrointestinal electricity.

[0105] In this embodiment, it is determined that compared with 16 EGG channels, the top 20% of EEG channels did not show a statistically significant distribution difference, and they were designated as highly relevant EEG channels, as shown in Table 1.

[0106] Table 1: Statistical analysis of the occurrence frequencies of the top 20% of EEG channels related to electrogastrogram in 190 sample results

[0107]

[0108] 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 mild cognitive impairment can be replaced by the relevant electrogastrogram channels (i.e., the above-mentioned second channel).

[0109] Example 3

[0110] See Figure 3 , the present invention provides an analysis method for synchronously collected EEG-electrogastrogram signals, including the following steps:

[0111] S101 Input the synchronously collected EEG data and electrogastrogram data of the subject;

[0112] In some embodiments, the EEG data is collected from the first channel, and the electrogastrogram data is collected from the second channel.

[0113] In the present invention, "channel", "electrode" and "lead" have the same meaning unless otherwise specified.

[0114] In some embodiments, the first channel includes F7, F9, FC5, C3, CP5, CP1, Oz, P10, P4, CP2, C4, FC2, FC6, F10, F1, CP3, PO7, PO4, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, F2, AF4 and AF8; the second channel includes the gastric body channel, the lesser curvature of the stomach channel, the ascending colon channel, the descending colon channel and the rectal channel.

[0115] It should be noted that the first channel and the second channel are respectively the refined EEG channel and electrogastrogram channel of the present invention, and the correlation between the first channel and the second channel in predicting mild cognitive impairment is relatively low.

[0116] Different from the prior art that couples electrogastrogram signals and EEG signals,

[0117] After obtaining the EEG channels with a strong correlation with the gastrointestinal electrical channels in patients with cognitive impairment, the present invention instead excludes these EEG channels from the actually collected EEG channels, thereby obtaining the above-mentioned first channels. The present invention finds that the predictive effect of these EEG channels can be replaced by the relevant gastrointestinal electrical channels (i.e., the above-mentioned second channels). After adopting the above-mentioned first channels and second channels with a lower correlation in predicting cognitive impairment, not only the difficulty of the acquisition operation is reduced, but also the sensitivity of predicting patients with cognitive impairment is improved, which is more convenient for practical application. Directly using the inherently lower number of EEG channels (such as conventional 16 leads, 32 leads, etc.) is not sufficient to cover the entire brain, and it is difficult to achieve accurate prediction of cognitive impairment.

[0118] In some embodiments, the acquisition time of the EEG data and the gastrointestinal electrical data is at least 5 minutes.

[0119] In some embodiments, the data is collected before or after a meal.

[0120] S102 Preprocess the synchronously collected EEG data and gastrointestinal electrical data to obtain corresponding time series data;

[0121] 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.

[0122] S103 Based on the first model, obtain the average electrode inconsistency index of the first channel and the second channel;

[0123] In some embodiments, the first model includes:

[0124] where JSD represents the JS divergence scoring function, t represents time, a it represents the time series data of the corresponding channels of the first channel and the second channel recorded by the subject 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 channels; ICI represents the inconsistency index of the corresponding channel.

[0125] In some embodiments, the corresponding channels refer to F7, F9, FC5, C3, CP5, CP1, Oz, P10, P4, CP2, C4, FC2, FC6, F10, F1, CP3, PO7, PO4, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, F2, AF4, AF8, the gastric body channel, the lesser curvature of the stomach channel, the ascending colon channel, the descending colon channel, and the rectal channel. In some embodiments, the total duration refers to the total duration of the synchronously collected electroencephalogram data and electrogastrogram data. For example, when the acquisition time is 5 minutes, the total duration is 5 minutes.

[0126] Based on the above method and the obtained 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.

[0127] While improving the calculation efficiency, both the accuracy and sensitivity are maintained at a relatively high level.

[0128] In some embodiments, the method further includes S201 of converting the time series data into probability density distribution data.

[0129] In some embodiments, the method further includes S202 of obtaining the inconsistency index of the corresponding channel based on the second model according to the probability density distribution data. The second model includes:

[0130] where ICI represents the inconsistency index function, D KL represents the KL divergence function, t represents time, a it represents the time series data of the corresponding channel recorded by the subject at the time point t seconds, b it represents the time series data of the corresponding channel recorded by the healthy population at the time point t seconds, n is the total duration of the time series data of the corresponding channel, P(a it ) represents the sample distribution, and Q(b it ) represents the reference distribution.

[0131] S104 compares 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 a patient with cognitive impairment.

[0132] In some embodiments, the cognitive impairment is mild cognitive impairment.

[0133] In some embodiments, the method for obtaining the preset evaluation threshold includes randomly obtaining the average electrode inconsistency index of 50-70% of the cognitive impairment patient samples in the sample dataset and calculating the critical value of the onset of cognitive impairment (based on the DNB theory), and setting the average electrode inconsistency index corresponding to the smallest critical value as the evaluation threshold.

[0134] Embodiment 4

[0135] See Figure 4 , the present invention provides a cognitive impairment prediction system 100 based on synchronously collected electroencephalogram-electrogastrogram signals, which is characterized by including:

[0136] A data acquisition module 102, configured to acquire data, where the data includes electroencephalogram data and electrogastrogram data;

[0137] In some embodiments, the electroencephalogram data and the electrogastrogram data are synchronously collected.

[0138] In some embodiments, the acquisition time of the electroencephalogram data and the electrogastrogram data is at least 5 minutes.

[0139] In some embodiments, the cognitive impairment is mild cognitive impairment.

[0140] In some embodiments, the data is collected before or after a meal.

[0141] In some embodiments, the electroencephalogram data is collected from a first channel, and the electrogastrogram data is collected from a second channel.

[0142] In some embodiments, the data includes sample data from a sample population and subject data from a subject.

[0143] In some embodiments, the sample population includes healthy people and cognitive impairment patients.

[0144] In some embodiments, the first channel includes F7, F9, FC5, C3, CP5, CP1, Oz, P10, P4, CP2, C4, FC2, FC6, F10, F1, CP3, PO7, PO4, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, F2, AF4, and AF8; the second channel includes a gastric body channel, a lesser curvature of the stomach channel, a ascending colon channel, a descending colon channel, and a rectal channel.

[0145] A data preprocessing module 104, configured to preprocess the data to obtain corresponding time series data;

[0146] In some embodiments, the preprocessing includes filtering the electroencephalogram data to filter out electroencephalogram signals below 1 Hz and above 30 Hz, and normalizing the electrogastrogram data and the filtered electroencephalogram data to obtain the time series data.

[0147] The first analysis module 106 is configured to obtain the average electrode inconsistency index of the first channel and the second channel based on the first model according to the time series data;

[0148] In some embodiments, the first model includes:

[0149] where JSD represents the JS divergence scoring function, t represents time, a it represents the time series data of the corresponding channels of the first channel and the second channel recorded by the subject 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 channels; ICI represents the inconsistency index of the corresponding channels.

[0150] The second analysis module 108 is configured to convert the time series data into probability density distribution data.

[0151] In some embodiments, the second analysis module 108 is further configured to obtain the inconsistency index of the corresponding channel based on the second model according to the probability density distribution data.

[0152] In some embodiments, the second model includes:

[0153] where

[0154] ICI represents the inconsistency index function, D KL represents the KL divergence function, t represents time,

[0155] a it represents the time series data of the corresponding channel recorded by the subject at time point t seconds,

[0156] b it represents the time series data of the corresponding channel recorded by the healthy population at time point t seconds, n is the total duration of the time series data of the corresponding channel, P(a it ) represents the sample distribution,

[0157] Q(b it ) represents the reference distribution.

[0158] In some embodiments, the first analysis module 106 and the second analysis module 108 can be combined into an analysis module.

[0159] A prediction module 110 is configured to compare the average electrode inconsistency index of a 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 a patient with cognitive impairment.

[0160] A database 112 is configured 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.

[0161] In some embodiments, the sample data set includes a healthy population data set and a cognitive impairment patient data set.

[0162] An evaluation threshold setting module 114 sets the average electrode inconsistency index corresponding to the smallest critical value as the evaluation threshold by randomly obtaining the average electrode inconsistency indexes of 50%-70% of the cognitive impairment patient samples in the sample data set and calculating the critical values of the onset of cognitive impairment.

[0163] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a computer terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0164] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose and scope protected by the claims of the present invention. These all fall within the protection scope of the present invention.

Claims

1. A cognitive impairment prediction system based on synchronously collected EEG-Gastrointestinal electrical signals, characterized in that: include: A data acquisition module, used to acquire data, the data including EEG data and gastrointestinal electrical data, the EEG data being acquired from the first channel, and the gastrointestinal electrical data being acquired from the second channel; the data including sample data from a sample population and subject data from a subject; the EEG data and the gastrointestinal electrical data being acquired synchronously, and the acquisition time of the EEG data and the gastrointestinal electrical data being at least 5 minutes; The data preprocessing module is used to preprocess the data to obtain corresponding time series data; the characteristic matrix of the time series data is as follows: Wherein, m and n represent the dimensions of the feature matrix of the EEG signal or EGG signal, respectively, m is the total number of electrodes, and n is the total duration of the EEG time series signal or EGG time series signal; A first analysis module is used to obtain an average electrode inconsistency index of the first channel and the second channel based on the time series data and a first model; 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 channels; ICI represents the inconsistency index of the corresponding channel; A prediction module, used for comparing 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 a patient with cognitive impairment; Among them, the first channels are F7, F9, FC5, C3, CP5, CP1, Oz, P10, P4, CP2, C4, FC2, FC6, F10, F1, CP3, PO7, PO4, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, F2, AF4 and AF8; the second channels are the gastric body channel, the lesser curvature of the stomach channel, the ascending colon channel, the descending colon channel and the rectal channel.

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

3. The system according to claim 2, characterized in that The second analysis module is further used to obtain an inconsistency index of a corresponding channel based on a second model according to the probability density distribution data, and 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 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 2, characterized in that The system also includes a database for storing the 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 a corresponding sample data set.

5. The system according to claim 4, characterized in that The system also includes an evaluation threshold setting module, which randomly obtains the average electrode inconsistency index of 50-70% of the cognitive impairment patient samples in the sample data set and calculates the critical value of the onset of cognitive impairment, and sets the average electrode inconsistency index corresponding to the minimum critical value as the evaluation threshold.

6. The system according to claim 1, wherein: The cognitive impairment is mild cognitive impairment.

7. The system according to claim 1, wherein: 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.

8. The system of claim 1, wherein: The data are collected before or after a meal.

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