1167 results about "Cognitive impairment" patented technology

The invention discloses a multi-modal cognitive impairment evaluation system based on a multi-dimensional cognitive function hypergraph. The multi-modal cognitive impairment evaluation system comprises a multi-modal signal acquisition system, a cognitive function evaluation normal form design, a universal feature extraction framework of each modal and online hypergraph learning based on a multi-dimensional cognitive function. The system constructs a dimensionality evaluation normal form containing the cognitive function based on multi-modal physiological and behavior signals, so that comprehensive evaluation of the cognitive function of the elderly is realized. Firstly, physiological and behavior responses of a user under different tasks and situations are recorded and analyzed in real time by fusing multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions. And then, mining fusion representation among the multi-modal signals, generating unified feature representation, introducing an online hypergraph of clinical knowledge, capturing a complex multi-modal data relationship, and providing an analysis result containing cognitive function evaluation. Finally, in combination with the technical means, the system can realize early detection of cognitive impairment.

The invention discloses a screening and evaluating system and method for cognitive impairment of old people, and belongs to the technical field of intelligent medical treatment. According to the system, electroencephalogram, VR / AR interaction, physiology and gait data are collected through the multi-modal acquisition module, the data processing module carries out data processing, dynamically selects a scale and carries out fusion analysis, the man-machine interaction module realizes natural interaction, and the output module generates a visual report and carries out early warning. The core algorithm comprises dynamic scale selection, multi-modal fusion evaluation and the like. The evaluation method comprises the steps of data acquisition, scale selection, data fusion, confidence verification, output and the like. Compared with a traditional method, the method overcomes the defects that subjectivity is high, and static evaluation, data splitting and base layer application are limited. The cognitive impairment assessment method can accurately and dynamically assess cognitive impairment, improves assessment accuracy, realizes full utilization of data, is simple and convenient to operate, is suitable for various scenes, provides efficient and reliable support for diagnosis, monitoring and intervention of cognitive impairment, and promotes development of intelligent medical treatment.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

The invention discloses a nerve regulation system for cognitive impairment, and the system comprises a cognitive behavior evaluation module which is used for recognizing a cognitive defect field which needs to be improved by a target object; the image data acquisition module is used for acquiring and preprocessing task state nuclear magnetic data and T1 weighted structure images of a target object; the brain region activation analysis module is used for determining a neural regulation potential target spot according to the preprocessed task state nuclear magnetic data; the brain network analysis module is used for identifying key nodes related to cognitive defects; the stimulation target screening module is used for determining an individualized stimulation target of the target object according to the nerve regulation potential target and the key node of the target object; the time domain interference electrical stimulation simulation and implementation module is used for performing electrical stimulation on the individualized stimulation target spot; and the dynamic feedback adjustment module is used for dynamically adjusting the stimulation parameters and enhancing the personalized regulation and control effect. By adopting the method and the device, the specific brain region directly related to the cognitive impairment can be determined, so that the targeting and the accuracy of treatment are enhanced.

Aspects of the disclosure relate to methods of reducing fatigue in a subject having myotonic dystrophy type 1 (DM1). Aspects of the disclosure relate to methods of treating one or more symptoms assessable by the MDHI (e.g., a GI symptom, myotonia, upper extremity function impairment, fatigue, mobility impairment, impairment in the ability to perform activities, pain, vision impairment, communication impairment, sleep impairment, emotional issues, cognitive impairment, social satisfaction impairment, social performance impairment, breathing impairment, swallowing impairment, and / or hearing impairment) in a subject having myotonic dystrophy type 1 (DM1). In some embodiments, the methods comprise administering to the subject a composition comprising complexes (e.g., muscle targeting complexes) comprising an oligonucleotide (e.g., a DMPK- targeting oligonucleotide) covalently linked to an antibody (e.g., anti-TfRl antibody).

The invention discloses a cognitive impairment man-machine interaction method and system based on emotion analysis, and relates to the field of cognitive impairment man-machine interaction, and the method comprises the steps: collecting the physiological information, language information and expression information of a user in real time, carrying out the feature fusion, forming a time series data set, and carrying out the emotion recognition and classification; based on a user emotion recognition classification result, establishing a generative adversarial network to generate man-machine interaction dialogue content matched with the current context and emotion in real time; when it is detected that the emotion recognition result of the user is negative emotion, a cognitive disorder emotion pacifying strategy is retrieved based on big data, emotion pacifying is conducted on the user, and related data are recorded; by analyzing historical related data of negative emotions of a user, an emotion prediction model is constructed, possible emotion changes are recognized in advance, and a man-machine interaction pacifying strategy is adjusted.

The invention discloses a post-stroke cognitive impairment prediction method based on multi-modal feature fusion. The method comprises the steps that firstly, multi-modal information of a stroke patient is collected, wherein the multi-modal information comprises a three-dimensional brain MRI image, an EEG electroencephalogram signal and clinical medical record information; secondly, converting MRI into a tensor, and inputting the tensor into a multi-scale spatial-temporal feature extraction backbone network to obtain MRI modal features; the EEG electroencephalogram signals are subjected to electroneurographic signals and are combined with Transform, and EEG modal features are obtained; the clinical medical record information of the stroke patient is converted into semantic sentences, the semantic sentences are input into a two-channel semantic encoder for encoding extraction, and clinical medical record information features are obtained. And finally, inputting the MRI modal features, the EEG modal features and the clinical medical record information features into a three-modal fusion device to obtain fusion features, and outputting probability prediction through a classifier. The post-stroke cognitive impairment prediction method achieves accurate prediction of post-stroke cognitive impairment, and significantly improves robustness and medical interpretation.

The invention provides an application of a fermentation lactobacillus mucus strain A21196 in preparation of a product for resisting anxiety and improving memory and concentration, and belongs to the technical field of biological medicines. Animal experiments show that the fermented lactobacillus mucus strain A21196 has the effects of resisting anxiety, resisting depression, improving cognitive impairment function and improving memory concentration, and provides a new means for preventing and / or treating nerve diseases related to anxiety disorder, depression and cognitive impairment.

The invention relates to the technical field of cognitive impairment detection, and discloses a cognitive impairment early warning method and device based on an electroencephalogram micro state and an eye movement trajectory, and the method comprises the following steps: S1, data acquisition, S2, electroencephalogram preprocessing, S3, electroencephalogram feature extraction, S4, eye movement feature extraction, S5, feature fusion, and S6, early warning judgment. According to the method, through independent convolution branch of electroencephalogram and eye movement features, a receptive field is expanded by utilizing cavity convolution to capture multi-scale features, and long-distance dependence is modeled by a self-attention layer; after tensor splicing, time sequence information is dynamically fused through a gating cycle unit (GRU), and cross-modal time correlation is captured. The method has the advantages that the multi-modal feature hierarchical extraction and self-adaptive modeling capability is enhanced, the complementarity fusion efficiency is optimized, meanwhile, by means of cavity convolution sparse connection, self-attention parameter sharing and GRU lightweight design, the model complexity and the calculation efficiency are balanced, and efficient feature representation is provided for cognitive impairment early warning.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention discloses a ginseng fermentation product rich in ginsenosides Rg3, Rk1 and Rg5 as well as a preparation method and application of the ginseng fermentation product. Aiming at the problem that the existing ginseng fermentation product has certain effects in the aspects of whitening, resisting anemia, resisting inflammation, delaying cognitive impairment, enhancing the immunity of the organism and the like, but no effect is particularly clear and prominent, the ginseng fermentation product is prepared by fermenting a ginseng fermentation substrate by using a specific probiotic composition; the composition is rich in rare ginsenosides Rg3, Rk1 and Rg5, and the relative content of the ginsenosides Rg3 is high. Experimental research shows that compared with the existing ginseng fermentation product, the ginseng fermentation product disclosed by the invention has a more definite and prominent effect of relieving cognitive impairment. The ginseng fermentation product is simple in preparation method and easy to industrialize, can be widely applied to functional food and other large health fields, and has a wide application prospect.

The invention discloses an early judgment system for cognitive impairment of Parkinson's disease based on transcranial magnetic-electroencephalogram multi-dimensional features. The early judgment system comprises a transcranial magnetic-electroencephalogram combined acquisition module, a synchronous signal preprocessing module, a multi-dimensional feature extraction module, a feature intelligent fusion module and a storage and display module, the transcranial magnetic-electroencephalogram combined acquisition module is used for disturbing the neural activity of a patient through monopulse transcranial magnetic stimulation coding and acquiring an evoked state electroencephalogram signal; the synchronizing signal preprocessing module is used for denoising and filtering the evoked state electroencephalogram signals; a multi-dimensional feature extraction module performs feature extraction on the processed evoked state electroencephalogram signals by utilizing multi-dimensional electroencephalogram feature analysis to obtain multi-parameter electroencephalogram neural activity features; the feature intelligent fusion module is used for carrying out weight distribution and feature fusion on the multi-parameter electroencephalogram neural activity features to obtain cognitive disorder multi-modal neural indexes; and the storage and display module is used for storing the cognitive disorder multi-modal nerve indexes and visualizing the cognitive disorder multi-modal nerve indexes.

The invention provides a cognitive impairment assessment system and method based on emotion interaction multi-mode physiological signals. The cognitive impairment assessment system comprises a multi-mode physiological signal acquisition module, an emotion interaction module based on emotion recognition and a cognitive impairment assessment module. The multi-modal physiological signal acquisition module acquires multi-dimensional physiological signals such as electroencephalogram, electro-oculogram and electrocardio through a flexible sensor, and a physiological signal coupling network is constructed; the emotion interaction module based on emotion recognition adopts a heterogeneous graph neural network to analyze emotion response under visual stimulation, recognizes deviation between emotion and a preset stimulation type through a comparison model, dynamically optimizes visual stimulation parameters, and forms closed-loop emotion interaction; and the cognitive impairment evaluation module combines an SE-CNN deep learning model and fuses multi-modal physiological data and emotion interaction characteristics to realize accurate classification of cognitive impairment. According to the method, the accuracy and individualized efficiency of emotion recognition can be effectively improved, and a new normal form and a new method are provided for early cognitive impairment assessment.

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention discloses a composition with the effect of relieving cognitive impairment and a preparation method and application thereof. The composition for relieving cognitive impairment comprises a ginseng fermentation product, wherein the ginseng fermentation product is obtained by fermenting a ginseng fermentation substrate using a microbial strain, and contains three ginsenosides Rg3, Rk1 and Rg5, and the total content of the ginsenosides Rg3, Rk1 and Rg5 is greater than or equal to 35% by weight, and the total weight of the ginsenosides in the ginseng fermentation product is 100% by weight; in the ginsenoside Rg3, the ginsenoside Rk1 and the ginsenoside Rg5, the ginsenoside Rg3 is larger than or equal to 40% by weight, and the total weight of the ginsenoside Rg3, the ginsenoside Rk1 and the ginsenoside Rg5 is 100% by weight. Experimental research proves that the composition for relieving the cognitive impairment has a clear and prominent cognitive impairment relieving effect.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention provides a cognitive disorder recognition method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring multi-modal data of a target; performing feature extraction on the multi-modal data to obtain a demographic feature and a plurality of acoustic features of the target; each acoustic feature corresponds to the category of the voice data; respectively calculating a category score corresponding to each category based on each acoustic feature; the category score is used for indicating the confidence value of the voice data belonging to the corresponding category; and performing cognitive impairment prediction on the target by using a cognitive prediction model according to the demographic characteristics of the target and the category scores to obtain a prediction result. According to the method and the device, the problem of low accuracy of cognitive impairment recognition in related technologies is solved.

The invention provides an eye movement normal form and index evaluation method and system for cognitive disorder detection, and relates to the technical field of eye movement data analys.The eye movement normal form and index evaluation method comprises the steps that eye movement parameters of a subject under eye movement normal forms graded at different difficulties are obtained, and the eye movement normal forms are evaluated on the basis of the eye movement parameters under the eye movement normal forms graded at different difficulties; extracting eye movement features under different difficulty grading eye movement paradigms; the cognitive competence of the participant is evaluated, and an MoCA score is obtained; a statistical analysis method is adopted to evaluate the normality of eye movement parameters, correlation analysis is carried out on eye movement characteristics under MoCA scores and different difficulty grading eye movement normal forms, a Spearman correlation analysis method is utilized to calculate the correlation coefficient and significance between the MoCA scores and the eye movement characteristics, rank correlation graphs are drawn respectively, and the eye movement parameters are obtained. And obtaining eye movement indexes with significant differences among the participants with different degrees of cognitive impairment under each normal form and the feasibility of the eye movement indexes in cognitive competence evaluation.

The invention relates to the field of preparation and application of biological peptides, in particular to a swelling hippocampal peptide and application of the swelling hippocampal peptide in preparation of a product for improving memory ability and cognitive impairment. According to the invention, a green controllable enzymolysis technology is utilized to obtain protein polypeptide which has the main molecular weight of less than 1000 Da and has nutritional, health-care and medicinal values, and four active peptide sequences with high biological activity are obtained by further research on the basis of the protein polypeptide.

The invention belongs to the field of medical artificial intelligence, and particularly relates to a multi-modal data acquisition and fusion method for Alzheimer's disease. The method comprises the following steps: firstly, synchronously acquiring eye movement, expression, voice, gait and grip strength data of a subject through a virtual reality multi-task normal form, and combining with an MoCA scale to score a result; then preprocessing and feature extraction are carried out on each modal data, and unified feature representation is constructed; on the basis, a cross-modal attention mechanism is adopted to realize interaction and weighted fusion of multi-modal features, and a unified fusion feature vector table is generated; and finally, outputting structured data organized according to task fragments for auxiliary evaluation and modeling of cognitive impairment. The method can effectively solve the problems that in the prior art, single-mode information is insufficient, and multi-mode data are difficult to align and fuse, has the advantages of being low in cost, easy to popularize and high in detection accuracy, and can be widely applied to early recognition and auxiliary diagnosis of the Alzheimer's disease.

The invention discloses a cognitive impairment intervention system and method based on general artificial intelligence, and the method comprises the steps: S1, collecting the physiological, behavior and cognitive state data of a patient, and carrying out the data preprocessing; s2, inputting a dual convolutional neural network to extract spatial-temporal features, and generating comprehensive cognitive state features; s3, time sequence clustering analysis is carried out, the key fluctuation period of the cognitive state is recognized, the decline trend is predicted, and the risk level is adjusted; s4, analyzing the emotional state of the patient in combination with a dynamic emotion recognition algorithm of micro-expression time sequence mapping, and optimizing an intervention scheme based on a user-defined emotion tag; s5, adjusting the intervention scheme by using an incremental learning method according to real-time feedback of the patient; and S6, optimizing the dual convolutional neural network by long-term training data, improving feature extraction and intervention precision, and realizing personalized long-term intelligent intervention. According to the method, multi-modal data and adaptive optimization are fused, accurate detection, personalized intervention and long-term optimization are realized, the rehabilitation efficiency is improved, and the life quality is improved.

The invention discloses an intelligent cognitive assessment method and system based on a virtual reality environment. According to the method, through accurate eye movement calibration and electroencephalogram calibration, resting-state electroencephalogram information, digital cognitive evaluation task parameters and eyeball movement track parameters of a subject in a virtual reality environment are collected, and through professional preprocessing and feature extraction, deep analysis and fusion are conducted on data through a common-attention multi-mode feature noise reduction fusion model. Through training and prediction of the neural network model, the cognitive function state of the subject can be effectively evaluated, the accuracy and efficiency of cognitive evaluation are improved, and meanwhile, the user experience is optimized. By utilizing the method, early recognition of cognitive impairment is facilitated, a scientific basis is provided for clinical diagnosis and intervention, and the method is of great significance in preventing and delaying dementia of old people.

The invention is applied to the technical field of cognitive disorder treatment aromatherapy, and particularly discloses cognitive disorder treatment equipment with a smell stimulation function, the cognitive disorder treatment equipment comprises a base, a control Bluetooth board is fixedly mounted in the base, an air outlet is formed in the outer surface of a top cover, and an aromatherapy containing pipe is mounted in the top cover in a threaded manner; a rotating mechanism is arranged between the base and the aromatherapy containing pipe, the position of the aromatherapy containing pipe can be changed in the top cover through the rotating mechanism, and aromatherapy smell can be changed and is not limited to one kind of smell. According to the cognitive disorder treatment equipment with the smell stimulation function, when the cognitive disorder treatment equipment is used, a Bluetooth board is controlled to control a rotating motor to rotate, the rotating motor drives a rotating disc to rotate, and an aromatherapy containing pipe fixed to the outer surface of the rotating disc in a twisting mode can rotate and be switched; the aromatherapy in the aromatherapy containing pipe can be volatilized and discharged from the air outlet, and different aromatherapy smells can be replaced during use.

The invention, which relates to the field of the intelligent accompanying system, discloses an interactive digital person accompanying system for the old with cognitive impairment, comprising a data acquisition module, an emotion recognition module, a first matching module, a second matching module, a personalized interaction generation module and a reinforcement learning adaptive module. The data acquisition module acquires multi-modal sensing data of a target old person; the emotion recognition module outputs an emotion label and an intensity value through feature extraction and Transform fusion; a first matching module screens corpus fragments according to emotion intensity triggering conditions; the second matching module performs secondary screening based on the retrieval weight; the personalized interaction generation module generates digital twin interaction data through probability distribution sampling; the reinforcement learning adaptive module dynamically adjusts the corpus weight according to the interaction reward value; according to the method, the dual guarantee of emotion guarding and physiological safety is realized, the anxiety emotion of the old with cognitive impairment is remarkably relieved, and the novelty of interaction and high emotion relieving efficiency can be kept.

The invention belongs to the technical field of transcranial ultrasonic stimulation. A transcranial focused ultrasound stimulation system and method for cognitive impairment patients is provided. The system comprises a main control module, a communication module, a display module, a signal source module, a driving module, an impedance matching module, an ultrasonic transducer module and an ultrasonic coupling module. According to the method, the personalized target spot can be determined according to the actual illness state of the target patient, and the appropriate treatment intensity can be determined, so that the optimal treatment effect can be obtained.

The invention relates to the field of traditional Chinese medicine, and discloses a traditional Chinese medicine composition for treating cognitive impairment of Parkinson's disease and application thereof.The traditional Chinese medicine composition is prepared from, by weight, 180-220 parts of vinegar tortoise plastron, 90-110 parts of morinda officinalis, 90-110 parts of angelica sinensis, 90-110 parts of radix curcumae, 55-65 parts of rhizoma acori graminei, 55-65 parts of polygala tenuifolia, 180-220 parts of calcined dragon bone, 180-220 parts of caulis spatholobi and 1-3 parts of borneol. When all the medicines of the traditional Chinese medicine composition are combined, the compatibility of monarch, minister, assistant and guide is performed, so that the traditional Chinese medicine composition has the effects of tonifying kidney, promoting blood circulation, dispersing blood stasis and inducing resuscitation, can relieve cognitive impairments such as space navigation, space exploration, working memory and the like of a Parkinson's disease cognitive impairment model mouse caused by chronic hypoperfusion, and realizes the treatment effect on the Parkinson's disease cognitive impairment; good application prospects are realized.

The invention discloses application of burdock root polysaccharide in preparation of a medicine for treating cognitive impairment, and belongs to the technical field of traditional Chinese medicines. Experiments prove that the burdock root polysaccharide can improve cognitive impairment and autonomous exploration ability of D-galactose induced cognitive impairment model mice, increase the activity of superoxide dismutase in serum of the mice, reduce the content of malondialdehyde, reduce the content of hippocampus interleukin-1beta and tumor necrosis factor-alpha in brains of the mice, and improve the cognitive impairment activity of the mice. According to the present invention, with the composition, the loss of the neuron in the hippocampal region of the cognitive impairment model mouse can be reduced so as to achieve the neuron protection effect, such that the good treatment effect on the cognitive impairment can be provided, and the new approach is provided for the development of the cognitive impairment-related drug.

The invention discloses an active peptide capable of improving cognitive impairment as well as a preparation method and application of the active peptide. The amino acid sequence of the active peptide is shown as SEQ ID NO: 1, 2, 3, 4, 5 or 6. The invention aims to provide a novel active peptide as well as a preparation method and application thereof aiming at the problems of limited means and unstable effect of improving cognitive impairment in the prior art, the active peptide prepared by the invention can improve the problems of learning and memory impairment and cognitive function decline to a certain extent, enriches technical approaches for improving cognitive impairment, and has a wide application prospect. And a new application choice is provided for related fields.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-mode early cognitive impairment screening method and system based on medical history information.The method comprises the steps that a first electrode plate and a second electrode plate are arranged at the forehead position and the occipital position of the brain of a user to be detected in advance; the third electrode plate and the fourth electrode plate are arranged on the left side of the brain side by side, and the fifth electrode plate and the sixth electrode plate are arranged on the right side of the brain side by side. And obtaining a disturbance coefficient set of the to-be-detected user under the reference detection mode combination, inputting the disturbance coefficient set into a pre-trained early cognitive impairment screening model, and predicting to obtain a prediction probability value of the early cognitive impairment of the to-be-detected user, and by adopting the scheme to screen the early cognitive impairment, the accuracy is high, the universality is strong, and the accuracy is high. The method can be applied to screening of people in communities, nursing homes and the like on a large scale.