Brain dementia prediction method and system based on multi-mode electromagnetic field biological detection

Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to a brain dementia prediction method and system based on multi-mode electromagnetic field biological detection, and the method comprises the steps: collecting a disturbance coefficient set of the brain of a to-be-detected user through a data collection device, and inputting the disturbance coefficient set into a pre-trained brain dementia prediction model, the method can be applied to screening of people in communities, nursing homes and the like on a large scale, so that the users possibly suffering from the brain dementia are screened out in time to be reminded of seeing a doctor for diagnosis as soon as possible.

Description

Technical Field

[0001] The invention belongs to the field of artificial intelligence technology, and specifically relates to a method and system for predicting brain dementia based on multi-mode electromagnetic field biodetection. Background Art

[0002] Dementia, also known as dementia, refers to a large class of brain diseases in medicine. It is a common disease in middle-aged and elderly people that is caused by brain damage or disease and is characterized by a progressive deterioration of cognitive function, and the extent of this deterioration is much greater than the progression of normal aging. Symptoms of dementia include a significant decline in memory, thinking ability, language ability, judgment, and the ability to carry out daily activities. The prevalence of dementia increases with age, and it is estimated that by 2050, the number of people suffering from dementia worldwide will rise to more than 100 million. The most common type of dementia is Alzheimer's disease, which is the most common type of dementia, accounting for approximately 60-80% of all cases. Other types include vascular dementia, Lewy body dementia, frontotemporal dementia, and Parkinson's disease dementia. The causes of dementia vary, and are usually related to lesions or damage to the brain.

[0003] Electromagnetic field biodetection technology is now being widely used in clinical applications such as monitoring and diagnosis of brain diseases. The technology has the advantages of being non-invasive, digital, fast, portable, and low-cost. The main physical parameters used are the phase, phase slope, amplitude, and amplitude slope of the electromagnetic field, and are implemented in a multi-frequency environment. By establishing healthy person detection standards and multi-disease detection models and algorithms, a large model for cranial brain lesion tissue detection is realized. The main function of electromagnetic field biodetection technology is to detect organic and structural changes in brain biological tissues. For example, the applicant has proposed a device for monitoring hydrocephalus and cerebral edema based on electromagnetic field biodetection technology (see CN102525458B). For another example, the applicant has also proposed a method and device for processing data on cranial brain abnormalities caused by cerebral infarction (see CN117688430A), which respectively sets electrodes at the forehead, occipital position, left front side of the cranium, left rear side of the cranium, right front side of the cranium and right rear side of the cranium, generates disturbance coefficients between the electrodes, and determines the abnormality of cerebral infarction of the subject according to the disturbance coefficients. Specifically, it performs machine learning by pre-collecting disturbance coefficient data sets of healthy subjects and abnormal subjects with abnormalities in corresponding areas due to early cerebral infarction under a preset detection mode combination, thereby obtaining a multi-modal abnormality prediction model, and then inputs the collected disturbance coefficient data set under the preset detection mode combination into the multi-modal abnormality prediction model to obtain the corresponding prediction results, thereby realizing early prediction of abnormalities caused by early cerebral infarction, etc.

[0004] Although the above schemes provide abnormal prediction schemes for some brain diseases, they are not suitable for abnormal prediction of brain dementia. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for predicting brain dementia based on multi-modal electromagnetic field biodetection, which can partially solve or alleviate the above-mentioned deficiencies in the prior art and be more suitable for large-scale brain dementia screening in community welfare institutions, elderly care service centers and other places.

[0006] In order to solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions: The first aspect of the present invention is to provide a method for predicting brain dementia based on multi-mode electromagnetic field biodetection, which comprises: using a data acquisition device to collect a set of disturbance coefficients under a combination of reference detection modes; the data acquisition device comprises a first electrode sheet and a second electrode sheet corresponding to the forehead position and the occipital position of the subject to be tested, and a third electrode sheet and a fourth electrode sheet corresponding to the left side of the brain and arranged in parallel, and a fifth electrode sheet and a sixth electrode sheet corresponding to the right side of the brain and arranged in parallel; Obtaining the disturbance coefficient set collected by the data collection device, and inputting it into a pre-trained brain dementia prediction model to predict the probability value of the user to be detected to have brain dementia; Among them, the disturbance coefficient set under the reference detection mode combination includes: the first to fourth disturbance coefficient sets under the preset four-electrode detection mode I to four-electrode detection mode IV, and the fifth to eighth disturbance coefficient sets under the three-electrode detection mode I to three-electrode detection mode IV; wherein, in the four-electrode detection mode I, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the third electrode sheet and the fifth electrode sheet located on the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the first disturbance coefficient set includes: the seventh disturbance coefficient R7 and the fifth disturbance coefficient R8 on the propagation path between the first electrode sheet and the second electrode sheet, the third electrode sheet and the fifth electrode sheet respectively. R5 and the sixth disturbance coefficient R6; in the four-electrode detection mode II, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the second disturbance coefficient set includes: the eighth disturbance coefficient R7', the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode sheet and the first electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; the four-electrode detection mode III, and the eleventh disturbance coefficient set obtained under the four-electrode detection mode III; in the four-electrode detection mode III, the first electrode sheet located at the forehead position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the second disturbance coefficient set includes: the eighth disturbance coefficient R7', the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode sheet and the first electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; the four-electrode detection mode III, and the eleventh disturbance coefficient set obtained under the four-electrode detection mode III; The excitation transmitting end is used as the excitation receiving end, the second electrode sheet located at the occipital position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively. Correspondingly, the eleventh disturbance coefficient set includes: the twentieth disturbance coefficient R5'', the twenty-first disturbance coefficient R6'' and the seventh disturbance coefficient R7 on the propagation path between the first electrode sheet and the second electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; the four-electrode detection mode IV, and the twelfth disturbance coefficient set obtained under the four-electrode detection mode IV; in the four-electrode detection mode IV, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively The third electrode sheet and the fifth electrode sheet on the side are used as the excitation receiving end, and accordingly, the twelfth disturbance coefficient set includes: the eighth disturbance coefficient R7', the twenty-second disturbance coefficient R3'' and the twenty-third disturbance coefficient R4'' on the propagation path between the second electrode sheet and the first electrode sheet, the third electrode sheet and the sixth electrode sheet respectively; in the three-electrode detection mode I, the third electrode sheet located on the left side of the skull is used as the excitation transmitting end, the first electrode located at the forehead position and the fifth electrode sheet located on the right side of the skull are used as the excitation receiving end, and accordingly, the three disturbance coefficient sets include: the twelfth disturbance coefficient R5' and the first disturbance coefficient R1 on the propagation path between the third electrode sheet and the first electrode sheet and the fifth electrode sheet respectively;In the three-electrode detection mode II, the fourth electrode sheet located on the left side of the brain is used as the excitation transmitting end, the second electrode located at the occipital position and the sixth electrode sheet located on the right side of the brain are used as the excitation receiving ends. Accordingly, the four disturbance coefficient sets include: the tenth disturbance coefficient R3' and the second disturbance coefficient R2 on the propagation path between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet respectively; in the three-electrode detection mode III, the fifth electrode sheet located on the right side of the brain is used as the excitation transmitting end, the first electrode located at the forehead position and the third electrode sheet located on the left side of the brain are used as the excitation receiving ends. Correspondingly, the five disturbance coefficient sets include: the thirteenth disturbance coefficient R6' and the eighth disturbance coefficient R1' on the propagation path between the fifth electrode sheet and the first electrode sheet and the third electrode sheet respectively; in the three-electrode detection mode IV, the sixth electrode sheet located on the right side of the skull is used as the excitation transmitting end, the second electrode located at the occipital position and the fourth electrode sheet located on the left side of the skull are used as the excitation receiving ends, and accordingly, the six disturbance coefficient sets include: the eleventh disturbance coefficient R4' and the ninth disturbance coefficient R2' on the propagation path between the sixth electrode sheet and the second electrode sheet and the fourth electrode sheet respectively; Among them, the steps of training to obtain the brain dementia prediction model specifically include: respectively obtaining the healthy disturbance coefficient sets of multiple healthy subjects under the benchmark detection mode combination; respectively obtaining the abnormal disturbance coefficient sets of multiple brain dementia subjects under the benchmark detection mode combination; obtaining the scale detection results obtained by testing the multiple brain dementia subjects with a specified scale method; inputting the healthy disturbance coefficient data set, the abnormal disturbance coefficient data set and the scale detection results into a pre-constructed neural network model for learning to obtain the brain dementia prediction model.

[0007] Preferably, the step of obtaining the abnormal disturbance coefficient set specifically includes: obtaining and identifying the associated past medical histories of the multiple brain dementia subjects; the associated past medical histories include any one or more of hypertension, stroke, cerebral infarction and genetic factors; if there are two or more past medical histories, obtaining the disturbance coefficient set under the baseline detection mode combination, and inputting it into the model with the healthy disturbance coefficient data set and the scale detection result for training to obtain a first prediction model; if it is a single past medical history, obtaining the disturbance coefficient sets under three-electrode detection mode V to three-electrode detection mode VIII respectively, and inputting them into the model with the disturbance coefficient obtained under the baseline detection mode combination, the healthy disturbance coefficient data set and the scale detection result for training to obtain a second prediction model. Preferably, in the three-electrode detection mode V, the third electrode sheet located on the left side of the skull serves as an excitation transmitting end, the first electrode located at the forehead and the sixth electrode sheet located on the right side of the skull serve as excitation receiving ends, and accordingly, the seventh disturbance coefficient set under the three-electrode detection mode V includes: the fifteenth disturbance coefficient R5' and the sixteenth disturbance coefficient R16 on the propagation path between the third electrode sheet and the first electrode sheet and the sixth electrode sheet, respectively. Preferably, in the three-electrode detection mode VI, the fourth electrode sheet located on the left side of the skull serves as an excitation transmitting end, the second electrode sheet located at the occipital position and the fifth electrode sheet located on the right side of the skull serve as excitation receiving ends, and accordingly, the eighth disturbance coefficient set under the three-electrode detection mode VI includes: the tenth disturbance coefficient R3' and the seventeenth disturbance coefficient R17 on the propagation path between the fourth electrode sheet and the second electrode sheet and the fifth electrode sheet, respectively. Preferably, in the three-electrode detection mode VII, the fifth electrode sheet located on the right side of the skull serves as an excitation transmitting end, the first electrode located at the forehead and the fourth electrode sheet located on the left side of the skull serve as excitation receiving ends, and accordingly, the ninth disturbance coefficient set in the three-electrode detection mode VII includes: the thirteenth disturbance coefficient R6' and the eighteenth disturbance coefficient R18 on the propagation path between the fifth electrode sheet and the first electrode sheet and the fourth electrode sheet, respectively. Preferably, in the three-electrode detection mode VIII, the sixth electrode sheet located on the right side of the skull serves as an excitation transmitting end, the second electrode located at the occipital position and the third electrode sheet located on the left side of the skull serve as excitation receiving ends, and accordingly, the tenth disturbance coefficient set in the three-electrode detection mode VIII includes: the eleventh disturbance coefficient R4' and the nineteenth disturbance coefficient R19 on the propagation path between the sixth electrode sheet and the second electrode sheet and the third electrode sheet, respectively.Preferably, if the past medical history of the subject to be tested includes stroke, in the corresponding disturbance coefficient set, the ratio of the sum of the third disturbance coefficient set and the four disturbance coefficient sets under three-electrode detection modes I and II is greater than the sum of the five disturbance coefficient sets and the six disturbance coefficient sets under three-electrode detection modes III and IV; or, if the past medical history of the subject to be tested includes cerebral infarction, in the corresponding disturbance coefficient set, the ratio of the sum of the three disturbance coefficient sets and the five disturbance coefficient sets under three-electrode detection modes I and III is greater than the sum of the four disturbance coefficient sets and the six disturbance coefficient sets under three-electrode detection modes II and IV.

[0008] The second aspect of the present invention is to provide a brain dementia prediction system based on multi-mode electromagnetic field biodetection, which includes: a data acquisition device, including a data communication module, and an electrode group for collecting data, the electrode group includes a first electrode sheet and a second electrode sheet corresponding to the forehead position and the occipital position of the skull, respectively, and a third electrode sheet and a fourth electrode sheet arranged in parallel corresponding to the left side of the skull, and a fifth electrode sheet and a sixth electrode sheet arranged in parallel corresponding to the right side of the skull; a brain dementia prediction terminal, which communicates data with the data acquisition device, is used to obtain a disturbance coefficient set of the data acquisition device under a preset reference detection mode combination, and inputs the disturbance coefficient set into a pre-trained brain dementia prediction model to predict the predicted probability value of the user to be detected to develop brain dementia; Specifically, the brain dementia prediction terminal includes: a first data acquisition module, used for performing data communication with the data acquisition device, so as to respectively obtain the health disturbance coefficient sets of multiple healthy subjects under the reference detection mode combination; and respectively obtain the abnormal disturbance coefficient sets of multiple brain dementia subjects under the reference detection mode combination; and obtain the disturbance coefficient set of the user to be detected under the reference detection mode combination; a second data acquisition module, used for performing data communication with the data acquisition device, so as to obtain the scale detection results obtained by the multiple brain dementia subjects through the specified scale method test; A brain dementia prediction model construction module is used to train a pre-constructed neural network model based on the healthy disturbance coefficient set, the abnormal disturbance coefficient set and the scale detection result to obtain the brain dementia prediction model; a brain dementia prediction module is used to predict the brain dementia prediction model constructed by the brain dementia prediction model construction module and the disturbance coefficient set of the user to be detected under the benchmark detection mode combination obtained by the first data acquisition module to obtain the predicted probability value of the brain dementia prediction model for the user to be detected; wherein, The reference detection mode combination includes: the first to fourth disturbance coefficient sets under the preset four-electrode detection mode I to the four-electrode detection mode IV, and the fifth to eighth disturbance coefficient sets under the three-electrode detection mode I to the three-electrode detection mode IV; wherein, in the four-electrode detection mode I, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the first disturbance coefficient set includes: the first electrode sheet is respectively connected with the second electrode sheet, the third electrode sheet and the fifth electrode sheet The seventh disturbance coefficient R7, the fifth disturbance coefficient R5 and the sixth disturbance coefficient R6 on the propagation path between the electrode pieces; in the four-electrode detection mode II, the second electrode piece located at the occipital position is used as the excitation transmitting end, the first electrode piece located at the forehead position and the fourth electrode piece and the sixth electrode piece located on the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the second disturbance coefficient set includes: the eighth disturbance coefficient R7', the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode piece and the first electrode piece, the fourth electrode piece and the sixth electrode piece respectively; the four-electrode detection mode Formula III, and the eleventh disturbance coefficient set obtained under the four-electrode detection mode III; in the four-electrode detection mode III, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the fourth electrode sheet and the sixth electrode sheet located on the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the eleventh disturbance coefficient set includes: the twentieth disturbance coefficient R5'', the twenty-first disturbance coefficient R6'' and the seventh disturbance coefficient R7 on the propagation path between the first electrode sheet and the second electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively;Four-electrode detection mode IV, and the twelfth disturbance coefficient set obtained under the four-electrode detection mode IV; in the four-electrode detection mode IV, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the twelfth disturbance coefficient set includes: the eighth disturbance coefficient R7', the twenty-second disturbance coefficient R3'' and the twenty-third disturbance coefficient R4'' on the propagation path between the second electrode sheet and the first electrode sheet, the third electrode sheet and the sixth electrode sheet respectively; in the three-electrode detection mode I, the third electrode sheet located at the left side of the skull is used as the excitation transmitting end, the first electrode located at the forehead position and the fifth electrode sheet located at the right side of the skull are used as the excitation receiving ends, and accordingly, the three disturbance coefficient sets include: the third electrode sheet The twelfth disturbance coefficient R5' and the first disturbance coefficient R1 on the propagation path between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet respectively; in the three-electrode detection mode II, the fourth electrode sheet located on the left side of the skull serves as the excitation transmitting end, the second electrode located at the occipital position and the sixth electrode sheet located on the right side of the skull serve as the excitation receiving ends, and accordingly, the four disturbance coefficient sets include: the tenth disturbance coefficient R3' and the second disturbance coefficient R2 on the propagation path between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet respectively; in the three-electrode detection mode III, the fifth electrode sheet located on the right side of the skull serves as the excitation transmitting end, the first electrode located at the forehead position and the third electrode sheet located on the left side of the skull serve as the excitation receiving ends, and accordingly, the five disturbance coefficient sets include: the thirteenth disturbance coefficient R6' on the propagation path between the fifth electrode sheet and the first electrode sheet and the third electrode sheet respectively and the eighth disturbance coefficient R1'; in the three-electrode detection mode IV, the sixth electrode sheet located on the right side of the skull is used as the excitation transmitting end, the second electrode located at the occipital position and the fourth electrode sheet located on the left side of the skull are used as the excitation receiving ends, and accordingly, the six disturbance coefficient sets include: the eleventh disturbance coefficient R4' and the ninth disturbance coefficient R2' on the propagation path between the sixth electrode sheet and the second electrode sheet and the fourth electrode sheet respectively. ;

[0009] Preferably, the brain dementia prediction terminal also includes: a third data acquisition module, used to communicate data with the data acquisition device to acquire and identify the associated past medical histories of the multiple brain dementia subjects; the associated past medical histories include at least one of hypertension, stroke, cerebral infarction and genetic factors; a fourth data acquisition module, used to communicate data with the data acquisition device to acquire the perturbation coefficient set of the brain dementia subject under the baseline detection mode combination when the brain dementia subject has two or more associated past medical histories, and to acquire the perturbation coefficient set under three-electrode detection mode VI to three-electrode detection mode VIII when the brain dementia subject has a single past medical history. Preferably, the screening model building module is also used for, when the subject has two or more related past medical histories, inputting the perturbation coefficient set of the subject under the baseline detection mode combination, the healthy perturbation coefficient data set and the scale detection result into the model for training to obtain a first prediction model; when the subject has a single past medical history, inputting the perturbation coefficient set of the subject under the fifth to eighth three-electrode detection modes, the perturbation coefficient set obtained under the baseline detection mode combination, the healthy perturbation coefficient data set and the scale detection result into the model for training to obtain a second prediction model.

[0010] Beneficial effect: In the prior art (see CN117688430A), electrodes are respectively set at the forehead, occipital position, left front side of the brain, left rear side of the brain, right front side of the brain and right rear side of the brain, disturbance coefficients are generated between the electrodes, and prediction is performed in combination with the default detection mode combination to obtain the probability of cerebral infarction lesions in the user to be detected; however, due to the complexity of human brain tissue and clear division of labor, there are differences in the lesion locations (i.e., detection locations) that induce different brain diseases, and the above device is not suitable for the prediction of brain dementia. Based on this, this application proposes a brain dementia prediction method and system with multi-mode electromagnetic field biodetection with parallel collection points for brain dementia, which has the characteristics of high accuracy and strong universality.

[0011] On the one hand, this scheme sets two sets of electrodes in parallel in the parts corresponding to the left and right brains, and sets multiple detection modes based on multiple electrodes (corresponding to electrodes in different positions and different electrode propagation paths), respectively detects the users to be detected to obtain multivariate perturbation data sets, thereby predicting the probability of the user developing brain dementia, with rich samples and high reliability of prediction results. On the other hand, the possible influencing factors of brain dementia (associated with past medical history; elderly people who usually have or have suffered from hypertension, stroke, genetic disease history (such as Alzheimer's disease), etc., have a higher risk of developing brain dementia) are introduced into the model training process, and the model is divided into a first model and a second model. For users with a higher risk of disease and a large number of past medical histories that have been screened, the first model with a smaller amount of data (measured by fewer detection modes) is used for prediction, which can effectively reduce the amount of calculation, and for users with a single medical history that is not easy to be screened, the second model with a larger amount of data (measured by more detection modes) is used for prediction, which improves the accuracy of the prediction. That is to say, this application effectively improves the efficiency of the prediction process by distinguishing user categories and quickly matching the corresponding prediction models. Compared with CT, ultrasound, and MRI equipment, it has almost no negative impact on the user's body and can be continuously monitored (for example, for several hours or even for several consecutive days). Therefore, it can be widely used in public medical systems such as communities. In addition, the above prediction method can be used not only to predict whether a user will develop brain dementia, but also to predict whether a user will develop early cognitive impairment, and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is 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 paying creative labor.

[0013] Figure 1 A flowchart of a method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to the present invention; Figure 2 This is a functional module diagram of a brain dementia prediction system based on multi-mode electromagnetic field biodetection of the present invention; Figure 3a Schematic diagram of disturbance coefficients on propagation paths between the first electrode sheet and the second electrode sheet, the third electrode sheet and the fifth electrode sheet in the four-electrode detection mode I of the present invention; Figure 3bIt is a schematic diagram of the disturbance coefficients on the propagation paths between the second electrode sheet and the first electrode sheet, the fourth electrode sheet and the sixth electrode sheet in the four-electrode detection mode II of the present invention; Figure 3c It is a schematic diagram of the disturbance coefficients on the propagation paths between the first electrode sheet and the second electrode sheet, the fourth electrode sheet and the sixth electrode sheet in the four-electrode detection mode III of the present invention; Figure 3d It is a schematic diagram of the disturbance coefficients on the propagation paths between the second electrode sheet and the first electrode sheet, the third electrode sheet and the sixth electrode sheet respectively in the four-electrode detection mode IV of the present invention; Figure 3e Schematic diagram of disturbance coefficients on the propagation paths between the first electrode sheet and the third electrode sheet and the fifth electrode sheet in the four-electrode grounding detection mode I of the present invention; Figure 3f It is a schematic diagram of the disturbance coefficients on the propagation paths between the second electrode sheet and the fourth electrode sheet and the sixth electrode sheet in the four-electrode grounding detection mode II of the present invention; Figure 3g It is a schematic diagram of the disturbance coefficients on the propagation paths between the first electrode sheet and the fourth electrode sheet and the sixth electrode sheet respectively in the four-electrode grounding detection mode III of the present invention; Figure 3h It is a schematic diagram of the disturbance coefficients on the propagation paths between the second electrode sheet and the third electrode sheet and the fifth electrode sheet respectively in the four-electrode grounding detection mode IV of the present invention; Figure 4a Schematic diagram of disturbance coefficients on the propagation paths between the third electrode sheet and the first electrode sheet and the fifth electrode sheet in the three-electrode detection mode I of the present invention; Figure 4b Schematic diagram of disturbance coefficients on the propagation paths between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet in the three-electrode detection mode II of the present invention; Figure 4c Schematic diagram of disturbance coefficients on the propagation paths between the fifth electrode sheet and the first electrode sheet and the third electrode sheet in the three-electrode detection mode III of the present invention; Figure 4d It is a schematic diagram of the disturbance coefficients on the propagation paths between the sixth electrode sheet and the second electrode sheet and the fourth electrode sheet in the three-electrode detection mode IV of the present invention; Figure 4e Schematic diagram of the disturbance coefficient on the propagation path between the third electrode sheet and the first electrode sheet in the three-electrode grounding detection mode I of the present invention; Figure 4f Schematic diagram of the disturbance coefficient on the propagation path between the fourth electrode sheet and the second electrode sheet in the three-electrode grounding detection mode II of the present invention; Figure 4g It is a schematic diagram of the disturbance coefficient on the propagation path between the fifth electrode sheet and the first electrode sheet in the three-electrode grounding detection mode III of the present invention; Figure 4h Schematic diagram of the disturbance coefficient on the propagation path between the sixth electrode sheet and the second electrode sheet in the three-electrode grounding detection mode IV of the present invention; Figure 4iSchematic diagram of the disturbance coefficient on the propagation path between the sixth electrode sheet and the third electrode sheet in the three-electrode grounding detection mode V of the present invention; Figure 4j Schematic diagram of the disturbance coefficient on the propagation path between the fourth electrode sheet and the fifth electrode sheet in the three-electrode grounding detection mode VI of the present invention; Figure 4k Schematic diagram of the disturbance coefficient on the propagation path between the third electrode sheet and the sixth electrode sheet in the three-electrode grounding detection mode VII of the present invention; Figure 4l Schematic diagram of the disturbance coefficient on the propagation path between the fifth electrode sheet and the fourth electrode sheet in the three-electrode grounding detection mode VIII of the present invention; Figure 5a Schematic diagram of disturbance coefficients on the propagation paths between the third electrode sheet and the first electrode sheet and the sixth electrode sheet in the three-electrode detection mode V of the present invention; Figure 5b Schematic diagram of disturbance coefficients on the propagation paths between the fourth electrode sheet and the second electrode sheet and the fifth electrode sheet in the three-electrode detection mode VI of the present invention; Figure 5c Schematic diagram of disturbance coefficients on the propagation paths between the fifth electrode sheet and the first electrode sheet and the fourth electrode sheet in the three-electrode detection mode VII of the present invention; Figure 5d It is a schematic diagram of the disturbance coefficients on the propagation paths between the sixth electrode sheet and the second electrode sheet and the third electrode sheet in the three-electrode detection mode VIII of the present invention; Figure 6-Figure 10 They are respectively the prediction results obtained by the prediction method in Example 1 of the present invention for the subject IV to be tested, and the corresponding verified clinical cases obtained by CT diagnosis / MRI diagnosis; Figures 11 to 14 They are respectively the prediction results obtained by using the prediction method in Example 2 of the present invention for the test subjects VI-IX, and the corresponding verified clinical cases obtained through CT diagnosis / MRI diagnosis.

[0014] Markings in the figure: 1, first electrode sheet; 2, second electrode sheet; 3, third electrode sheet; 4, fourth electrode sheet; 5, fifth electrode sheet; 6, sixth electrode sheet. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. Herein, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and they themselves have no specific meaning. Therefore, "module", "component" or "unit" can be used in a mixed manner. Herein, the orientation or position relationship indicated by the terms "upper", "lower", "inner", "outer", "front", "back", "one end", "other end" and the like is based on the orientation or position relationship shown in the drawings, which is 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, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance. In this document, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should 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, an indirect connection through an intermediate medium, or a connection between the 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 the specific circumstances. "And / or" herein includes any and all combinations of one or more listed related items. "Multiple" herein means two or more, that is, it includes two, three, four, five, etc. In this specification, certain embodiments may be disclosed in a format that is in a certain range. It should be understood that this description of "in a certain range" is only for convenience and brevity, and should not be interpreted as a rigid limitation on the disclosed range. Therefore, the description of the range should be considered to have specifically disclosed all possible sub-ranges and independent numerical values ​​within this range. For example, the description of the range 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, 2, 3, 4, 5, and 6. The above rules apply regardless of the breadth of the range.

[0016] In this article, "abnormality" means that when an excitation signal of a specific frequency is used to transmit an excitation signal from the transmitting end, and it reaches the receiving end through a specific transmission path (i.e., the propagation path between the transmitting end and the receiving end), due to the presence of diseased tissue corresponding to a certain disease in the transmission path, the detection signal received by the receiving end is different from the detection signal (i.e., electromagnetic field biological signal, including receiving impedance, transmission differential impedance and disturbance coefficient, etc.) received when there is no diseased tissue in the transmission path or when there is diseased tissue corresponding to other diseases. Therefore, it is called abnormality.

[0017] In this document, "excitation signal" includes a sinusoidal curve having a frequency selected from a specified frequency range; or, a pulse or pulse sequence having a pulse width and a pulse repetition frequency selected from a specified pulse width range and a specified pulse repetition frequency range; or a coded signal, such as a linear frequency modulation signal, which has a frequency that varies with time to obtain a tissue response within a specified frequency range and minimize interference, multipath effects, and radio frequency (RF) noise and other noise.

[0018] The "detection mode" in this article refers to the electrode patch at a specified position acting as an excitation transmitter to transmit an excitation signal of a specific frequency range to the subject's brain to obtain the disturbance coefficient of a specific area.

[0019] Example 1: See Figure 1 , is a flow chart of a method for predicting brain dementia based on multi-mode electromagnetic field biodetection of the present invention. Specifically, the method comprises the steps of: S10 is based on the pre-constructed training data set to train and obtain a brain dementia prediction model. In this embodiment, the step S10 specifically includes the steps: S101 respectively obtains the health disturbance coefficient sets of multiple healthy subjects under the combination of the benchmark detection mode. In this embodiment, a data acquisition device wearable on the user's brain is used to obtain the health disturbance coefficient set of the benchmark detection mode combination of healthy subjects. Among them, the data acquisition device is respectively provided with the first to sixth electrode sheets, specifically the first electrode sheet 1 and the second electrode sheet 2 corresponding to the forehead position and the occipital position on the skull of the user to be detected, and the third electrode sheet 3 and the fourth electrode sheet 4 arranged in parallel on the left side of the skull, and the fifth electrode sheet 5 and the sixth electrode sheet 6 arranged in parallel on the right side of the skull. S103 respectively obtains the abnormal disturbance coefficient sets of multiple brain dementia subjects under the combination of the benchmark detection mode. As above, in this embodiment, the above-mentioned data acquisition device is used to obtain the abnormal disturbance coefficient sets of each brain dementia subject under the combination of the benchmark detection mode. S105 obtains the scale test results obtained by the specified scale method test of the multiple brain dementia subjects. In this embodiment, each brain dementia subject also needs to be tested by a specified scale method. Preferably, the scale method includes: Community Dementia Scale (GSI-D), Cognitive Function Self-Assessment Scale (AD8), Daily Living Ability Assessment Scale (ADL), Mini-Cognitive Assessment (Mini-Cog) scale, etc., which are scales for screening cognitive function and emotional state of the elderly population. S107 inputs the health disturbance coefficient data set, the abnormal disturbance coefficient data set and the scale detection results into a pre-constructed neural network model for learning to obtain the brain dementia prediction model.

[0020] S11 uses a data acquisition device to obtain a disturbance coefficient set under a reference detection mode combination.

[0021] S12 inputs the disturbance coefficient set obtained in step S11 into a pre-trained brain dementia prediction model to predict the probability value of the user to be tested developing brain dementia.

[0022] Due to different brain diseases, the location of lesions or pathological tissues varies greatly, and in the process of electromagnetic field biological detection, different locations for transmission and reception will produce different detection data. Therefore, slight differences in detection positions may have a great impact on the detection results. In this embodiment, two electrode sheets are set at the forehead and occipital positions of the brain, and two electrode sheets are set in parallel on the left and right sides of the brain, respectively. A model is constructed in advance based on the interference coefficient of the brain dementia population, so that in subsequent detections, only the preset benchmark detection mode needs to be used to detect the user to be detected to obtain the corresponding interference coefficient set, that is, the probability of the user's brain dementia can be predicted, so that users who may have brain dementia can be screened out based on the predicted probability. If the predicted probability is greater than the preset threshold range, it will be marked and reminded to go to a medical institution for timely treatment for diagnosis.

[0023] Specifically, for example, if the predicted probability belongs to the first preset threshold range (for example, the predicted probability ≥ 90%), it means that there is a risk of developing brain dementia; if it is less than the minimum value of the first preset threshold range, it means that there is no risk. The above-mentioned first preset range threshold can be obtained by calculating the probability of elderly people with a medical history suffering from brain dementia based on a pre-statistical medical database. Different past medical histories correspond to different probability ranges. When there are multiple past medical histories, the corresponding probability range is obtained by combining the probabilities of multiple past medical histories, and different past medical history combinations correspond to different probability ranges. Generally speaking, the more types of past medical histories, the higher the probability of suffering from Alzheimer's disease or causing brain dementia.

[0024] In this embodiment, the perturbation coefficient set under the reference detection mode combination includes: the first to fourth perturbation coefficient sets under the preset four-electrode detection mode I to the four-electrode detection mode IV, and the fifth to eighth perturbation coefficient sets under the three-electrode detection mode I to the three-electrode detection mode IV; wherein, see Figure 3a In the four-electrode detection mode I, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the brain respectively are used as the excitation receiving ends, and accordingly, the first disturbance coefficient set includes: the seventh disturbance coefficient R7, the fifth disturbance coefficient R5 and the sixth disturbance coefficient R6 on the propagation path between the first electrode sheet and the second electrode sheet, the third electrode sheet and the fifth electrode sheet respectively; see Figure 3bIn the four-electrode detection mode II, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the second disturbance coefficient set includes: the eighth disturbance coefficient R7', the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode sheet and the first electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; see Figure 3c , four-electrode detection mode III, and the eleventh disturbance coefficient set obtained under the four-electrode detection mode III; in the four-electrode detection mode III, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the fourth electrode sheet and the sixth electrode sheet located on the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the eleventh disturbance coefficient set includes: the twentieth disturbance coefficient R5'', the twenty-first disturbance coefficient R6'' and the seventh disturbance coefficient R7 on the propagation path between the first electrode sheet and the second electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; see Figure 3d , four-electrode detection mode IV, and the twelfth disturbance coefficient set obtained under the four-electrode detection mode IV; in the four-electrode detection mode IV, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the third electrode sheet and the fifth electrode sheet located on the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the twelfth disturbance coefficient set includes: the eighth disturbance coefficient R7', the twenty-second disturbance coefficient R3'' and the twenty-third disturbance coefficient R4'' on the propagation path between the second electrode sheet and the first electrode sheet, the third electrode sheet and the sixth electrode sheet respectively; see Figure 4a In the three-electrode detection mode I, the third electrode sheet located on the left side of the skull is used as the excitation transmitting end, the first electrode located at the forehead and the fifth electrode sheet located on the right side of the skull are used as the excitation receiving ends, and accordingly, the three disturbance coefficient sets include: the twelfth disturbance coefficient R5' and the first disturbance coefficient R1 on the propagation path between the third electrode sheet and the first electrode sheet and the fifth electrode sheet respectively; see Figure 4b In the three-electrode detection mode II, the fourth electrode sheet located on the left side of the skull is used as the excitation transmitting end, the second electrode located at the occipital position and the sixth electrode sheet located on the right side of the skull are used as the excitation receiving ends, and accordingly, the four perturbation coefficient sets include: the tenth perturbation coefficient R3' and the second perturbation coefficient R2 on the propagation path between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet respectively; see Figure 4cIn the three-electrode detection mode III, the fifth electrode sheet located on the right side of the skull is used as the excitation transmitting end, the first electrode located at the forehead and the third electrode sheet located on the left side of the skull are used as the excitation receiving ends, and accordingly, the five disturbance coefficient sets include: the thirteenth disturbance coefficient R6' and the eighth disturbance coefficient R1' on the propagation path between the fifth electrode sheet and the first electrode sheet and the third electrode sheet respectively; see Figure 4d In the three-electrode detection mode IV, the sixth electrode located on the right side of the skull serves as the excitation transmitting end, the second electrode located at the occipital position and the fourth electrode located on the left side of the skull serve as the excitation receiving ends. Accordingly, the six disturbance coefficient sets include: the eleventh disturbance coefficient R4' and the ninth disturbance coefficient R2' on the propagation path between the sixth electrode and the second electrode and the fourth electrode respectively.

[0025] In some embodiments, if the past medical history of the subject to be tested includes stroke, in the corresponding disturbance coefficient set, the ratio of the sum of the third disturbance coefficient set and the four disturbance coefficient sets under three-electrode detection modes I and II is greater than the sum of the five disturbance coefficient sets and the six disturbance coefficient sets under three-electrode detection modes III and IV; if the past medical history of the subject to be tested includes cerebral infarction, in the corresponding disturbance coefficient set, the ratio of the sum of the three disturbance coefficient sets and the five disturbance coefficient sets under three-electrode detection modes I and III is greater than the sum of the four disturbance coefficient sets and the six disturbance coefficient sets under three-electrode detection modes II and IV. According to a large number of studies, in the past medical history, the risk of developing brain dementia caused by stroke is higher than others, especially cerebral infarction in stroke.

[0026] Adjusting the proportion of data sets in different modes according to high-risk lesions with different medical histories can effectively improve the detection rate. According to a large number of studies, stroke lesions on the left side of the brain have a high risk of cognitive impairment. Therefore, the detection proportion of the first and second three-electrode detection modes is greater than the detection proportion of the third and fourth electrode detection modes; and when cerebral infarction occurs in the left hemisphere and anterior hemisphere, the risk of cognitive impairment in the anterior side of the brain is high; therefore, when collecting data, increasing the detection proportion of the first and third three-electrode detection modes is greater than the detection proportion of the second and fourth three-electrode detection modes.

[0027] In this embodiment, the brain dementia prediction model trained in the above step S107 specifically includes the following steps: S1071 obtains and identifies the associated medical history of the multiple brain dementia subjects; the associated medical history includes any one or more of hypertension, stroke, cerebral infarction and genetic factors; if there are two or more medical histories, execute step S1073; if there is a single medical history, execute step S1075. S1073 inputs the health disturbance coefficient data set obtained in step S101, the abnormal disturbance coefficient set obtained in step S103 and the scale test result obtained in step S105 into the model for training to obtain a first prediction model.

[0028] S125 obtains the abnormal disturbance coefficient sets under three-electrode detection mode V to three-electrode detection mode VIII respectively, and inputs them into the model for training with the abnormal disturbance coefficient under the reference detection mode combination obtained in step S103, the healthy disturbance coefficient data set and the scale detection result to obtain a second prediction model. See also Figure 5a In the three-electrode detection mode V, the third electrode sheet located on the left side of the skull is used as the excitation transmitting end, the first electrode located at the forehead and the sixth electrode sheet located on the right side of the skull are used as the excitation receiving ends. Accordingly, the seventh disturbance coefficient set under the three-electrode detection mode V includes: the fifteenth disturbance coefficient R5' and the sixteenth disturbance coefficient R16 on the propagation path between the third electrode sheet and the first electrode sheet and the sixth electrode sheet respectively. Figure 5b In the three-electrode detection mode VI, the fourth electrode sheet located on the left side of the skull is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the fifth electrode sheet located on the right side of the skull are used as the excitation receiving ends. Accordingly, the eighth disturbance coefficient set under the three-electrode detection mode VI includes: the tenth disturbance coefficient R3' and the seventeenth disturbance coefficient R17 on the propagation path between the fourth electrode sheet and the second electrode sheet and the fifth electrode sheet respectively. See Figure 5c In the three-electrode detection mode VII, the fifth electrode sheet located on the right side of the skull is used as the excitation transmitting end, the first electrode located at the forehead and the fourth electrode sheet located on the left side of the skull are used as the excitation receiving ends. Accordingly, the ninth disturbance coefficient set in the three-electrode detection mode VII includes: the thirteenth disturbance coefficient R6' and the eighteenth disturbance coefficient R18 on the propagation path between the fifth electrode sheet and the first electrode sheet and the fourth electrode sheet respectively. Figure 5d In the three-electrode detection mode VIII, the sixth electrode located on the right side of the skull serves as an excitation transmitting end, the second electrode located at the occipital position and the third electrode located on the left side of the skull serve as excitation receiving ends. Accordingly, the tenth disturbance coefficient set under the three-electrode detection mode VIII includes: the eleventh disturbance coefficient R4' and the nineteenth disturbance coefficient R19 on the propagation path between the sixth electrode and the second electrode and the third electrode respectively.

[0029] Generally speaking, compared with subjects with a single medical history or no medical history, the more severe and complex the craniocerebral injury of subjects with multiple related medical histories is, the higher the risk of illness is, and naturally, the easier it is to be screened out under the benchmark detection mode combination, while subjects with a single medical history or no medical history are prone to missed detection. Therefore, in this embodiment, for subjects with a single medical history or no medical history, on the basis of the benchmark detection mode combination, four sets of perturbation coefficients under the detection modes are added as training samples to train the second prediction model. Accordingly, when testing, the related medical history of the subject to be tested must first be identified. If it is a single medical history or no medical history, in addition to obtaining the perturbation coefficient set under the benchmark detection mode combination, it is also necessary to obtain the perturbation coefficient set under the three-electrode detection mode V to the three-electrode detection mode VIII, and input it into the second prediction model for prediction; if it is multiple medical histories, the perturbation coefficient set under the benchmark detection mode combination is directly input into the first prediction model for prediction.

[0030] To verify the effectiveness of the brain dementia prediction method in this embodiment, the following clinical verification was conducted: Specifically, to verify the sensitivity of this device, the prediction method and system of this embodiment were used to verify 246 dementia patients in mental health centers, welfare homes, and elderly care service centers. The prediction method and system of this embodiment detected 209 people, accounting for 84.96%. To verify the specificity of the brain dementia prediction method in this embodiment, 468 healthy people were screened, and the screening results were evaluated as normal in 439 cases, accounting for 93.80%.

[0031] In summary, the prediction method and system for brain dementia in this embodiment is used to predict brain dementia, and the sensitivity of the device is 84.96% and the specificity is 93.80%. The prediction results of several subjects are described below: Example 1: See Figure 6 Patient I was diagnosed with mixed dementia by a health center in Chongqing. The disturbance coefficient predicted by the brain dementia prediction method in this embodiment is 174, which is not within the disturbance coefficient interval of 120-160 for healthy people, and the predicted probability of suffering from brain dementia is 92.23% (>90%), which is consistent with the actual diagnosis result. Example 2: See Figure 7 Patient II was diagnosed with vascular dementia and organic mental disorder by a health center in Chongqing. The disturbance coefficient predicted by the brain dementia prediction method in this embodiment is 114, which is not within the disturbance coefficient range of 120-160 for healthy people, and the probability of suffering from brain dementia is predicted to be 95.66% (>90%), which is consistent with the diagnosis result. Example 3: See Figure 8Patient III was diagnosed with Alzheimer's dementia (mixed type) by a health center in Chongqing. The disturbance coefficient predicted by the brain dementia prediction method in this embodiment is 171, which is not within the disturbance coefficient interval of 120-160 for healthy people, and the probability of suffering from brain dementia is predicted to be 94.37% (>90%), which is consistent with the diagnosis result. Example 4: See Fig. 9 Patient IV was diagnosed with Alzheimer's dementia by a health center in Chongqing. The disturbance coefficient detected by this device was 110, which was not within the disturbance coefficient range of 120-160 for healthy people. The predicted probability of suffering from brain dementia was 92.59% (>90%), which was consistent with the diagnosis result. Example 5: See Fig.10 Patient V was diagnosed with Alzheimer's dementia by a health center in Chongqing. The disturbance coefficient detected by this device was 184, which was not within the disturbance coefficient range of 120-160 for healthy people. The predicted probability of him suffering from brain dementia was 96.57% (>90%), which was consistent with the diagnosis result.

[0032] Embodiment 2: The present invention also provides a prediction method, which can predict whether a patient suffers from early cognitive impairment or brain dementia, that is, predict brain dementia and early cognitive impairment at the same time. Specifically, it includes the various steps in the above-mentioned embodiment 1, and its working principle is also the same, but the difference is: in the above-mentioned step S103, in addition to obtaining the abnormal disturbance coefficient set of multiple brain dementia subjects under the baseline detection mode combination, it is also necessary to obtain the abnormal disturbance coefficient set of multiple early cognitive impairment patients under the baseline detection mode combination; accordingly, in the above-mentioned step S105, in addition to obtaining the scale detection results obtained by testing multiple brain dementia subjects with a specified scale method; it is also necessary to obtain the scale detection results obtained by testing multiple early cognitive impairment patients with a corresponding scale method; accordingly, in the above-mentioned step S107, in addition to inputting the healthy disturbance coefficient data set, the abnormal disturbance coefficient data set of brain dementia subjects and their scale detection results into the pre-constructed neural network model for learning, it is also necessary to input the abnormal disturbance coefficient data set of early cognitive impairment patients and their scale detection results into the pre-constructed neural network model. The neural network model constructed first is learned to obtain a prediction model that can simultaneously predict brain dementia and early cognitive impairment, so that when the perturbation coefficient set of the subject to be tested under the baseline detection mode combination is input into the prediction model, the prediction model can respectively obtain the predicted probability value of the patient suffering from predicted brain dementia and the predicted probability value of suffering from early cognitive impairment. If the predicted probability value of the patient suffering from brain dementia reaches the first preset threshold range, it is determined that the patient has the risk of suffering from brain dementia, regardless of whether the predicted probability value of the patient suffering from early cognitive impairment reaches the corresponding third preset threshold range; if the predicted probability value of the patient suffering from brain dementia is less than the minimum value of the first preset threshold range, but the predicted probability value of the patient suffering from early cognitive impairment reaches the third preset threshold range, the patient is considered to have the risk of suffering from early cognitive impairment; of course, if the predicted probability value of the patient suffering from brain dementia does not reach the first preset threshold range, and the predicted probability value of the patient suffering from early cognitive impairment does not reach the third preset threshold range, the patient is judged to have no risk of suffering from brain dementia or early cognitive impairment, or the risk is low. For example, if the probability value of a subject to be tested is predicted to be 94% (the first preset threshold range is ≥90%), the subject to be tested is marked as a patient with brain dementia and is reminded to seek medical diagnosis, regardless of the predicted probability value of suffering from early cognitive impairment. For another example, if the probability value of a subject to be tested is predicted to be 93% (the third preset threshold range is ≥86%), and the probability value of suffering from brain dementia is 89% (the first preset threshold range is ≥90%), the subject to be tested is marked as a patient with brain dementia and is reminded to seek medical diagnosis.

[0033] In order to verify the effectiveness of the method and system for predicting brain dementia and early cognitive impairment in this embodiment, the following clinical verification was carried out: Specifically, in order to verify the sensitivity of this device, a general custom scale and the Community Dementia Inventory (GSI-D), a self-assessment scale of cognitive function (AD8), an assessment scale of daily living ability (ADL), a simple cognitive assessment (Mini-Cog) scale, etc. were used to screen the cognitive function and emotional state of the elderly group in mental health centers, welfare homes, elderly care service centers and communities. At the same time, the screening results obtained using the above scales were compared with the screening results obtained using this device.

[0034] The total number of elderly people screened in this activity was 1,502, of which 1,023 were diagnosed with early cognitive impairment according to the scale, and 746 were predicted to have early cognitive impairment using the prediction method of this embodiment, accounting for 72.92%. There were 124 people with brain dementia according to the scale, and 108 were predicted to have brain dementia using the prediction method of this embodiment, accounting for 87.10%. In order to verify the specificity of the prediction method in this embodiment, 180 young healthy people from related companies were screened using this device, and the screening results were evaluated as normal in 168 cases, accounting for 93.33%.

[0035] In summary, this method has high sensitivity and specificity. The following is an illustration of the prediction results of several subjects: Example 1: See Fig. 9 , subject IV was diagnosed with Alzheimer's dementia by a health center in Chongqing, and the disturbance coefficient measured by the prediction method of this embodiment was 110 (the same as the measurement result of the prediction method of the above embodiment), which is not within the disturbance coefficient interval of 120-160 for healthy people, and the probability of suffering from brain dementia was predicted to be 91.20% (>90%), and the probability of suffering from early cognitive impairment was predicted to be 85.37% (<86%), which is consistent with the diagnosis result. Example 2: See Fig.10 , subject V was diagnosed with Alzheimer's dementia by a health center in Chongqing, and the disturbance coefficient measured by the prediction method of this embodiment was 184 (the same as the measurement result of the prediction method of the above embodiment), which is not within the disturbance coefficient interval of 120-160 for healthy people, and the predicted probability of suffering from brain dementia is 96.21% (>90%), and the predicted probability of suffering from early cognitive impairment is 87.37% (>86%), and since 96.21%>87.37%, it meets the diagnosis result. Example 3: See Fig.11, subject VI was diagnosed with severe vascular dementia and organic mental disorder by a health center in Chongqing, and the disturbance coefficient measured by the prediction method of this embodiment was 99, which was not within the disturbance coefficient interval of 120-160 for healthy people, and the probability of suffering from brain dementia was predicted to be 96.89% (>90%), and the probability of suffering from early cognitive impairment was predicted to be 83.69% (<86%), which was consistent with the diagnosis result. Example 4: See Fig.12 , subject VII was diagnosed with mild cognitive impairment by a health center in Chongqing, and the detection disturbance coefficient measured by the prediction method of this embodiment was 169, which was not within the disturbance coefficient interval of 120-160 for healthy people, and the probability of suffering from brain dementia was predicted to be 76.53% (<90%), and the probability of suffering from early cognitive impairment was predicted to be 93.47% (>86%), which was consistent with the actual diagnosis result. Example 5: See Fig.13 , subject VIII was diagnosed with mild cognitive impairment by a health center in Chongqing, and the detection disturbance coefficient measured by the prediction method of this embodiment was 183, which was not within the disturbance coefficient interval of 120-160 for healthy people, and the probability of suffering from brain dementia was predicted to be 78.97% (<90%), and the probability of suffering from early cognitive impairment was predicted to be 94.89% (>86%), which was consistent with the actual diagnosis result. Example 6: See Fig.14 , subject IX was diagnosed with mild cognitive impairment by a health center in Chongqing, and the detection disturbance coefficient measured by the prediction method of this embodiment was 114, which was not within the disturbance coefficient range of 120-160 for healthy people, and the predicted probability of suffering from brain dementia was 66.96% (<90%), and the predicted probability of suffering from early cognitive impairment was 91.44% (>86%), which was consistent with the actual diagnosis result.

[0036] Embodiment 3: The present invention also provides another prediction method. Different from Embodiment 1 and Embodiment 2, this embodiment proposes a grounding reference detection mode combination for the elderly population with no previous medical history or with less previous medical history (for example, less than or equal to 2 types). Specifically, the grounding reference detection mode combination includes: Figure 3e , four-electrode grounding detection mode I, with the first electrode sheet as the excitation transmitting end, the second electrode sheet on the opposite side of the first electrode sheet 1 as the grounding end, the third electrode sheet and the fifth electrode sheet as the excitation receiving end, the eleventh disturbance coefficient set is obtained, the eleventh disturbance coefficient set includes: the fifth disturbance coefficient R5 and the sixth disturbance coefficient R6 on the propagation path between the first electrode sheet and the third electrode sheet and the fifth electrode sheet respectively; see Figure 3f, four-electrode grounding detection mode II, with the second electrode sheet as the excitation transmitting end, the first electrode sheet as the grounding end, the fourth electrode sheet and the sixth electrode sheet as the excitation receiving end, to obtain a twelfth disturbance coefficient set, the twelfth disturbance coefficient set includes: the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode sheet and the fourth electrode sheet and the sixth electrode sheet respectively; see Figure 3g , four-electrode grounding detection mode III, with the first electrode sheet as the excitation transmitting end, the second electrode sheet on the opposite side of the first electrode sheet 1 as the grounding end, the fourth electrode sheet and the sixth electrode sheet as the excitation receiving end, the twenty-first disturbance coefficient set is obtained, which includes: the twentieth disturbance coefficient R5'' and the twenty-first disturbance coefficient R6'' on the propagation path between the first electrode sheet and the fourth electrode sheet and the sixth electrode sheet respectively; see Figure 3h , four-electrode grounding detection mode IV, with the second electrode sheet as the excitation transmitting end, the first electrode sheet as the grounding end, the third electrode sheet and the fifth electrode sheet as the excitation receiving end, obtain the twenty-second disturbance coefficient set, which includes: the twenty-second disturbance coefficient R3'' and the twenty-third disturbance coefficient R4'' on the propagation path between the second electrode sheet and the third electrode sheet and the fifth electrode sheet respectively; see Figure 4e , three-electrode grounding detection mode I, with the third electrode sheet as the excitation transmitting end, the fifth electrode sheet on the opposite side of the third electrode sheet as the grounding end, and the first electrode sheet as the excitation receiving end, a thirteenth disturbance coefficient set is obtained, the thirteenth disturbance coefficient set includes; a twelfth disturbance coefficient R5' on the propagation path between the third electrode sheet and the first electrode sheet; see Figure 4f , three-electrode grounding detection mode II, with the fourth electrode sheet as the excitation transmitting end, the sixth electrode sheet as the grounding end, and the second electrode sheet as the excitation receiving end, a fourteenth disturbance coefficient set is obtained, the fourteenth disturbance coefficient set includes; the tenth disturbance coefficient R3' on the propagation path between the fourth electrode sheet and the second electrode sheet; see Figure 4g , three-electrode grounding detection mode III, with the fifth electrode sheet as the excitation transmitting end, the third electrode sheet as the grounding end, and the first electrode sheet as the excitation receiving end, a fifteenth disturbance coefficient set is obtained, the fifteenth disturbance coefficient set includes: a fifteenth disturbance coefficient R6' on the propagation path between the fifth electrode sheet and the first electrode sheet; see Figure 4h , three-electrode grounding detection mode IV, with the sixth electrode sheet as the excitation transmitting end, the fourth electrode sheet as the grounding end, and the second electrode sheet as the excitation receiving end, the sixteenth disturbance coefficient set is obtained, and the sixteenth disturbance coefficient set includes: the eleventh disturbance coefficient R4' on the propagation path between the sixth electrode sheet and the second electrode sheet.

[0037] Furthermore, compared with patients with a history of disease, patients without a history of disease are more difficult to be screened. Therefore, in order to avoid missed detection to a certain extent, when the predicted probability value predicted by the above screening method meets the second preset threshold range, a secondary screening is performed. Specifically, it includes the above-mentioned various detection modes, except that it also includes alternative modes: see Figure 4i , three-electrode grounding detection mode V, with the sixth electrode sheet as the excitation transmitting end, the second electrode sheet as the grounding end, and the third electrode sheet as the excitation receiving end, a seventeenth disturbance coefficient set is obtained, the seventeenth disturbance coefficient set includes: a nineteenth disturbance coefficient R19 on the propagation path between the sixth electrode sheet and the third electrode sheet; see Figure 4j , three-electrode grounding detection mode VI, with the fourth electrode sheet as the excitation transmitting end, the second electrode sheet as the grounding end, and the fifth electrode sheet as the excitation receiving end, an eighteenth disturbance coefficient set is obtained, and the eighteenth disturbance coefficient set includes: the seventeenth disturbance coefficient R17 on the propagation path between the fourth electrode sheet and the fifth electrode sheet; see Figure 4k , three-electrode grounding detection mode VII, with the third electrode sheet as the excitation transmitting end, the first electrode sheet as the grounding end, and the sixth electrode sheet as the excitation receiving end, a nineteenth disturbance coefficient set is obtained, the nineteenth disturbance coefficient set includes: a sixteenth disturbance coefficient R16 on the propagation path between the third electrode sheet and the sixth electrode sheet; see Figure 4l , three-electrode grounding detection mode VIII, with the fifth electrode sheet as the excitation transmitting end, the first electrode sheet as the grounding end, and the fourth electrode sheet as the excitation receiving end, the twentieth disturbance coefficient set is obtained, and the twentieth disturbance coefficient set includes: the eighteenth disturbance coefficient R18 on the propagation path between the fifth electrode sheet and the fourth electrode sheet.

[0038] Specifically, the second preset threshold range is smaller than the first preset threshold range. For example, the first preset threshold range is ≥90%; the second preset threshold range is: [85%, 90).

[0039] Correspondingly, training a brain dementia prediction model also includes: obtaining a disturbance coefficient set of multiple brain dementia subjects with no history of disease or brain dementia subjects with less previous medical history under a grounded benchmark detection mode combination, and inputting it together with the healthy disturbance coefficient data set and the scale detection results into a pre-constructed neural network model for training to obtain a third prediction model.

[0040] The grounded reference detection mode combination with the above-mentioned mode can satisfy the whole brain detection while having a small amount of data and can be used and promoted on a large scale. Of course, in other embodiments, the method of this embodiment can also be applied to subjects with more than two related past medical histories. Accordingly, when training the prediction model, the training data also includes a set of perturbation coefficients of brain dementia subjects with more than two related past medical histories under the corresponding detection mode.

[0041] Example 4: See Figure 2 , is a functional module diagram of a brain dementia prediction system based on multi-mode electromagnetic field biodetection of the present invention. Specifically, the system includes: a data acquisition device, including a data communication module, and an electrode group for user data acquisition, the electrode group includes a first electrode sheet and a second electrode sheet corresponding to the forehead position and the occipital position of the skull, respectively, and a third electrode sheet and a fourth electrode sheet arranged in parallel corresponding to the left side of the skull, and a fifth electrode sheet and a sixth electrode sheet arranged in parallel corresponding to the right side of the skull; a brain dementia prediction terminal, which communicates data with the data acquisition device, and is used to obtain a disturbance coefficient set of the data acquisition device under a preset reference detection mode combination, and inputs the disturbance coefficient set into a pre-trained brain dementia prediction model to predict the predicted probability value of the user to be detected to develop brain dementia.

[0042] In some embodiments, the brain dementia prediction terminal can be a mobile intelligent terminal or a host computer, which specifically includes: a first data acquisition module, used to communicate data with the data acquisition device to respectively obtain the health disturbance coefficient sets of multiple healthy subjects under the benchmark detection mode combination; and respectively obtain the abnormal disturbance coefficient sets of multiple brain dementia subjects under the benchmark detection mode combination; and obtain the disturbance coefficient set of the user to be detected under the benchmark detection mode combination; a second data acquisition module, used to obtain the scale test results obtained by the specified scale method test of the multiple brain dementia subjects; A brain dementia prediction model construction module is used to train a pre-constructed neural network model based on the healthy disturbance coefficient set, the abnormal disturbance coefficient set and the scale detection results to obtain the brain dementia prediction model; a brain dementia prediction module is used to predict based on the brain dementia prediction model constructed by the brain dementia prediction model construction module and the disturbance coefficient set of the user to be detected under the benchmark detection mode combination acquired by the first data acquisition module, to obtain the predicted probability value of the brain dementia prediction model for the user to be detected.

[0043] In some embodiments, the data acquisition device further includes an excitation generator, which is configured to generate an excitation signal under a corresponding detection mode according to a control signal sent by a host computer, and control the working state of the electrode group (including: as an excitation transmitting end, or as an excitation receiving end, or as a grounding end, or as a suspended end; wherein the suspended end means that the electrode sheet is neither grounded nor transmits an excitation signal, nor receives an excitation signal, but is suspended).

[0044] Of course, in other embodiments, the excitation generator may not be provided in the data acquisition device, that is, the data acquisition device is externally connected to an excitation generator, and the data acquisition device communicates data with a host computer (preferably, the above-mentioned screening device is integrated in the host computer) through a communication module, and the excitation generator is externally connected to the host computer, and the host computer controls the excitation generator to generate a corresponding excitation signal and then sends it to the corresponding electrode in the data acquisition device; of course, the host computer generates a corresponding control signal according to the corresponding detection mode and sends it to the data acquisition device to control the working state of each electrode sheet. In this embodiment, the above-mentioned reference detection mode combination is the same as the reference detection mode combination of the above-mentioned embodiment, and will not be repeated here.

[0045] In other embodiments, the brain dementia prediction terminal also includes: a third data acquisition module, used to acquire and identify the associated past medical histories of the multiple brain dementia subjects; the associated past medical histories include at least one of hypertension, stroke, cerebral infarction and genetic factors; a fourth data acquisition module, used to obtain the perturbation coefficient set of the brain dementia subject under the baseline detection mode combination when the brain dementia subject has two or more associated past medical histories, and to obtain the perturbation coefficient set under three-electrode detection mode VI to three-electrode detection mode VIII when the brain dementia subject has a single past medical history. Correspondingly, the above-mentioned screening model construction module is also used to, when the subject has two or more related past medical histories, input the perturbation coefficient set of the subject under the baseline detection mode combination, the healthy perturbation coefficient data set and the scale detection result into the model for training to obtain a first prediction model; when the subject has a single past medical history, input the perturbation coefficient set of the subject under the fifth to eighth three-electrode detection modes, the perturbation coefficient set obtained under the baseline detection mode combination, the healthy perturbation coefficient data set and the scale detection result into the model for training to obtain a second prediction model.

[0046] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0047] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course 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, 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), and includes a number of instructions for 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.

[0048] 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 enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for predicting brain dementia based on multi-mode electromagnetic field biodetection, characterized in that: include: A data acquisition device is used to collect a disturbance coefficient set under the reference detection mode combination; the data acquisition device includes a first electrode sheet and a second electrode sheet corresponding to the forehead position and the occipital position of the subject to be tested, and a third electrode sheet and a fourth electrode sheet corresponding to the left side of the brain and arranged in parallel, and a fifth electrode sheet and a sixth electrode sheet corresponding to the right side of the brain and arranged in parallel; Obtaining the disturbance coefficient set collected by the data collection device, and inputting it into a pre-trained brain dementia prediction model to predict the probability value of the user to be detected to have brain dementia; The disturbance coefficient set under the reference detection mode combination includes: the first to fourth disturbance coefficient sets under the preset four-electrode detection mode I to the four-electrode detection mode IV, and the fifth to eighth disturbance coefficient sets under the three-electrode detection mode I to the three-electrode detection mode IV; wherein, In the four-electrode detection mode I, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the brain respectively are used as the excitation receiving ends, and accordingly, the first disturbance coefficient set includes: the seventh disturbance coefficient R7, the fifth disturbance coefficient R5 and the sixth disturbance coefficient R6 on the propagation path between the first electrode sheet and the second electrode sheet, the third electrode sheet and the fifth electrode sheet respectively; In the four-electrode detection mode II, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the second disturbance coefficient set includes: the eighth disturbance coefficient R7', the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode sheet and the first electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; Four-electrode detection mode III, and the eleventh disturbance coefficient set obtained under the four-electrode detection mode III; in the four-electrode detection mode III, the first electrode located at the forehead is used as an excitation transmitting end, the second electrode sheet located at the occipital position and the fourth electrode sheet and the sixth electrode sheet located on the left side and the right side of the skull respectively are used as excitation receiving ends, and accordingly, the eleventh disturbance coefficient set includes: the twentieth disturbance coefficient R5'', the twenty-first disturbance coefficient R6'' and the seventh disturbance coefficient R7 on the propagation path between the first electrode sheet and the second electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; Four-electrode detection mode IV, and a twelfth disturbance coefficient set obtained under the four-electrode detection mode IV; in the four-electrode detection mode IV, the second electrode sheet located at the occipital position is used as an excitation transmitting end, the first electrode sheet located at the forehead position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the skull respectively are used as excitation receiving ends, and accordingly, the twelfth disturbance coefficient set includes: the eighth disturbance coefficient R7', the twenty-second disturbance coefficient R3'' and the twenty-third disturbance coefficient R4'' on the propagation path between the second electrode sheet and the first electrode sheet, the third electrode sheet and the sixth electrode sheet respectively; In the three-electrode detection mode I, the third electrode sheet located on the left side of the skull is used as an excitation transmitting end, the first electrode located at the forehead and the fifth electrode sheet located on the right side of the skull are used as excitation receiving ends, and accordingly, the three disturbance coefficient sets include: the twelfth disturbance coefficient R5' and the first disturbance coefficient R1 on the propagation path between the third electrode sheet and the first electrode sheet and the fifth electrode sheet respectively; In the three-electrode detection mode II, the fourth electrode sheet located on the left side of the skull is used as the excitation transmitting end, the second electrode located at the occipital position and the sixth electrode sheet located on the right side of the skull are used as the excitation receiving ends, and accordingly, the four disturbance coefficient sets include: the tenth disturbance coefficient R3' and the second disturbance coefficient R2 on the propagation path between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet respectively; In the three-electrode detection mode III, the fifth electrode sheet located on the right side of the brain is used as the excitation transmitting end, the first electrode located at the forehead and the third electrode sheet located on the left side of the brain are used as the excitation receiving ends, and accordingly, the five disturbance coefficient sets include: the thirteenth disturbance coefficient R6' and the eighth disturbance coefficient R1' on the propagation path between the fifth electrode sheet and the first electrode sheet and the third electrode sheet respectively; In the three-electrode detection mode IV, the sixth electrode sheet located on the right side of the skull is used as the excitation transmitting end, the second electrode located at the occipital position and the fourth electrode sheet located on the left side of the skull are used as the excitation receiving ends, and accordingly, the six disturbance coefficient sets include: the eleventh disturbance coefficient R4' and the ninth disturbance coefficient R2' on the propagation path between the sixth electrode sheet and the second electrode sheet and the fourth electrode sheet respectively; The step of training the brain dementia prediction model specifically includes: respectively obtaining health disturbance coefficient sets of a plurality of healthy subjects under the combination of the reference detection modes; respectively obtaining abnormal disturbance coefficient sets of a plurality of subjects with brain dementia under the combination of the benchmark detection modes; Obtaining scale test results obtained by testing the plurality of subjects with brain dementia using a designated scale method; The healthy disturbance coefficient data set, the abnormal disturbance coefficient data set and the scale detection result are input into a pre-built neural network model for learning to obtain the brain dementia prediction model.

2. The method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to claim 1, characterized in that: The step of obtaining the abnormal disturbance coefficient set specifically includes: Obtaining and identifying the related past medical histories of the multiple brain dementia subjects; the related past medical histories include any one or more of hypertension, stroke, cerebral infarction and genetic factors; If there are two or more past medical histories, obtain the disturbance coefficient set under the reference detection mode combination, and input it, the health disturbance coefficient data set and the scale detection result into the model for training to obtain a first prediction model; If it is a single past medical history, the disturbance coefficient sets under three-electrode detection mode V to three-electrode detection mode VIII are obtained respectively, and they are input into the model for training together with the disturbance coefficient obtained under the baseline detection mode, the health disturbance coefficient data set and the scale detection results to obtain a second prediction model.

3. The method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to claim 2, characterized in that: In the three-electrode detection mode V, the third electrode located on the left side of the skull serves as an excitation transmitting end, the first electrode located on the forehead and the sixth electrode located on the right side of the skull serve as excitation receiving ends. Accordingly, the seventh disturbance coefficient set under the three-electrode detection mode V includes: the fifteenth disturbance coefficient R5' and the sixteenth disturbance coefficient R16 on the propagation path between the third electrode and the first electrode and the sixth electrode respectively.

4. The method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to claim 2, characterized in that: In the three-electrode detection mode VI, the fourth electrode sheet located on the left side of the skull serves as the excitation transmitting end, the second electrode sheet located at the occipital position and the fifth electrode sheet located on the right side of the skull serve as the excitation receiving ends. Accordingly, the eighth disturbance coefficient set under the three-electrode detection mode VI includes: the tenth disturbance coefficient R3' and the seventeenth disturbance coefficient R17 on the propagation path between the fourth electrode sheet and the second electrode sheet and the fifth electrode sheet respectively.

5. The method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to claim 2, characterized in that: In the three-electrode detection mode VII, the fifth electrode located on the right side of the skull serves as the excitation transmitting end, the first electrode located on the forehead and the fourth electrode located on the left side of the skull serve as the excitation receiving ends. Accordingly, the ninth disturbance coefficient set in the three-electrode detection mode VII includes: the thirteenth disturbance coefficient R6' and the eighteenth disturbance coefficient R18 on the propagation path between the fifth electrode and the first electrode and the fourth electrode respectively.

6. The method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to claim 2, characterized in that: In the three-electrode detection mode VIII, the sixth electrode located on the right side of the skull serves as an excitation transmitting end, the second electrode located at the occipital position and the third electrode located on the left side of the skull serve as excitation receiving ends. Accordingly, the tenth disturbance coefficient set under the three-electrode detection mode VIII includes: the eleventh disturbance coefficient R4' and the nineteenth disturbance coefficient R19 on the propagation path between the sixth electrode and the second electrode and the third electrode respectively.

7. The method for predicting brain dementia based on multi-mode electromagnetic field biodetection according to claim 2, characterized in that: If the past medical history of the subject to be tested includes stroke, in the corresponding perturbation coefficient set, the ratio of the sum of the third perturbation coefficient set and the four perturbation coefficient sets under the three-electrode detection modes I and II is greater than the sum of the five perturbation coefficient sets and the six perturbation coefficient sets under the three-electrode detection modes III and IV; Alternatively, if the medical history of the subject to be tested includes cerebral infarction, in the corresponding disturbance coefficient set, the ratio of the sum of the three disturbance coefficient sets and the five disturbance coefficient sets under three-electrode detection modes I and III is greater than the sum of the four disturbance coefficient sets and the six disturbance coefficient sets under three-electrode detection modes II and IV.

8. A brain dementia prediction system based on multi-mode electromagnetic field biodetection, characterized in that: include: A data acquisition device, comprising a data communication module, and an electrode group for collecting data, wherein the electrode group comprises a first electrode sheet and a second electrode sheet corresponding to the forehead position and the occipital position of the brain, respectively, a third electrode sheet and a fourth electrode sheet arranged in parallel corresponding to the left side of the brain, and a fifth electrode sheet and a sixth electrode sheet arranged in parallel corresponding to the right side of the brain; A brain dementia prediction terminal, which communicates with the data acquisition device to obtain a disturbance coefficient set of the data acquisition device under a preset reference detection mode combination, and inputs the disturbance coefficient set into a pre-trained brain dementia prediction model to predict the probability value of the occurrence of brain dementia in the user to be detected; Specifically, the brain dementia prediction terminal includes: A first data acquisition module is used to communicate with the data acquisition device to respectively acquire a set of healthy disturbance coefficients of a plurality of healthy subjects under the combination of the reference detection modes; and respectively acquire a set of abnormal disturbance coefficients of a plurality of subjects with brain dementia under the combination of the reference detection modes; and acquire a set of disturbance coefficients of the user to be detected under the combination of the reference detection modes; A second data acquisition module is used to communicate with the data acquisition device to obtain the scale test results of the multiple brain dementia subjects obtained by the specified scale method test; A brain dementia prediction model construction module, used for training a pre-constructed neural network model based on the healthy disturbance coefficient set, the abnormal disturbance coefficient set and the scale detection result to obtain the brain dementia prediction model; A brain dementia prediction module, used to make predictions based on the brain dementia prediction model constructed by the brain dementia prediction model construction module and the disturbance coefficient set of the user to be detected under the reference detection mode combination acquired by the first data acquisition module, to obtain a predicted probability value of the brain dementia prediction model for the user to be detected; The reference detection mode combination includes: the first to fourth perturbation coefficient sets under the preset four-electrode detection mode I to the four-electrode detection mode IV, and the fifth to eighth perturbation coefficient sets under the three-electrode detection mode I to the three-electrode detection mode IV; In the four-electrode detection mode I, the first electrode located at the forehead is used as the excitation transmitting end, the second electrode sheet located at the occipital position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the brain respectively are used as the excitation receiving ends, and accordingly, the first disturbance coefficient set includes: the seventh disturbance coefficient R7, the fifth disturbance coefficient R5 and the sixth disturbance coefficient R6 on the propagation path between the first electrode sheet and the second electrode sheet, the third electrode sheet and the fifth electrode sheet respectively; In the four-electrode detection mode II, the second electrode sheet located at the occipital position is used as the excitation transmitting end, the first electrode sheet located at the forehead position and the fourth electrode sheet and the sixth electrode sheet located at the left side and the right side of the skull respectively are used as the excitation receiving ends, and accordingly, the second disturbance coefficient set includes: the eighth disturbance coefficient R7', the third disturbance coefficient R3 and the fourth disturbance coefficient R4 on the propagation path between the second electrode sheet and the first electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; Four-electrode detection mode III, and the eleventh disturbance coefficient set obtained under the four-electrode detection mode III; in the four-electrode detection mode III, the first electrode located at the forehead is used as an excitation transmitting end, the second electrode sheet located at the occipital position and the fourth electrode sheet and the sixth electrode sheet located on the left side and the right side of the skull respectively are used as excitation receiving ends, and accordingly, the eleventh disturbance coefficient set includes: the twentieth disturbance coefficient R5'', the twenty-first disturbance coefficient R6'' and the seventh disturbance coefficient R7 on the propagation path between the first electrode sheet and the second electrode sheet, the fourth electrode sheet and the sixth electrode sheet respectively; Four-electrode detection mode IV, and a twelfth disturbance coefficient set obtained under the four-electrode detection mode IV; in the four-electrode detection mode IV, the second electrode sheet located at the occipital position is used as an excitation transmitting end, the first electrode sheet located at the forehead position and the third electrode sheet and the fifth electrode sheet located at the left side and the right side of the skull respectively are used as excitation receiving ends, and accordingly, the twelfth disturbance coefficient set includes: the eighth disturbance coefficient R7', the twenty-second disturbance coefficient R3'' and the twenty-third disturbance coefficient R4'' on the propagation path between the second electrode sheet and the first electrode sheet, the third electrode sheet and the sixth electrode sheet respectively; In the three-electrode detection mode I, the third electrode sheet located on the left side of the skull is used as an excitation transmitting end, the first electrode located at the forehead and the fifth electrode sheet located on the right side of the skull are used as excitation receiving ends, and accordingly, the three disturbance coefficient sets include: the twelfth disturbance coefficient R5' and the first disturbance coefficient R1 on the propagation path between the third electrode sheet and the first electrode sheet and the fifth electrode sheet respectively; In the three-electrode detection mode II, the fourth electrode sheet located on the left side of the skull is used as the excitation transmitting end, the second electrode located at the occipital position and the sixth electrode sheet located on the right side of the skull are used as the excitation receiving ends, and accordingly, the four disturbance coefficient sets include: the tenth disturbance coefficient R3' and the second disturbance coefficient R2 on the propagation path between the fourth electrode sheet and the second electrode sheet and the sixth electrode sheet respectively; In the three-electrode detection mode III, the fifth electrode sheet located on the right side of the brain is used as the excitation transmitting end, the first electrode located at the forehead and the third electrode sheet located on the left side of the brain are used as the excitation receiving ends, and accordingly, the five disturbance coefficient sets include: the thirteenth disturbance coefficient R6' and the eighth disturbance coefficient R1' on the propagation path between the fifth electrode sheet and the first electrode sheet and the third electrode sheet respectively; In the three-electrode detection mode IV, the sixth electrode located on the right side of the skull serves as the excitation transmitting end, the second electrode located at the occipital position and the fourth electrode located on the left side of the skull serve as the excitation receiving ends. Accordingly, the six disturbance coefficient sets include: the eleventh disturbance coefficient R4' and the ninth disturbance coefficient R2' on the propagation path between the sixth electrode and the second electrode and the fourth electrode respectively.

9. The brain dementia prediction system based on multi-mode electromagnetic field biodetection according to claim 8, characterized in that: The brain dementia prediction terminal also includes: A third data acquisition module is used to communicate data with the data acquisition device to acquire and identify the associated past medical history of the multiple brain dementia subjects; the associated past medical history includes at least one of hypertension, stroke, cerebral infarction and genetic factors; The fourth data acquisition module is used to communicate data with the data acquisition device to obtain the perturbation coefficient set of the brain dementia subject under the baseline detection mode combination when the brain dementia subject has two or more related past medical histories, and to obtain the perturbation coefficient set under three-electrode detection mode VI to three-electrode detection mode VIII when the brain dementia subject has a single past medical history.

10. The brain dementia prediction system based on multi-mode electromagnetic field biodetection according to claim 9, characterized in that: The screening model building module is also used for, when the subject has two or more related past medical histories, inputting the perturbation coefficient set of the subject under the baseline detection mode combination, the healthy perturbation coefficient data set and the scale detection result into the model for training to obtain a first prediction model; when the subject has a single past medical history, inputting the perturbation coefficient set of the subject under the fifth to eighth three-electrode detection modes, the perturbation coefficient set obtained under the baseline detection mode combination, the healthy perturbation coefficient data set and the scale detection result into the model for training to obtain a second prediction model.

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