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53results about "Medical informatics" patented technology

Multi-parameter mental stress assessment method based on analytical hierarchy method and device

The invention discloses a multi-parameter mental stress assessment method based on an analytical hierarchy method and a device. The method obtains a parameter set of mental stress influential factors through analysis of a frequency domain, a time domain and non-linearity of HRV signals of a testee, and obtains mental stress assessment results of the testee by the analytical hierarchy method and a mental stress assessment model. The device collects electrocardiograph signals of the testee, analyzes and assesses the electrocardiograph signals by the multi-parameter mental stress assessment method based on the analytical hierarchy method, outputs an assessment report and transmits the assessment report outwards. The multi-parameter mental stress assessment method based on the analytical hierarchy method and the invention have the beneficial effect that (1) a mental stress state of the testee is monitored according to changes of HRV physiological parameters of the testee, so influences brought by subjective factors and different cognitive levels of the testees to monitoring results are effectively avoided; and (2) the daily mental stress state of the testee is effectively recorded and analyzed, and key data is stored to remind the testee of self-adjustment and taken for standby application of medical staffs.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

Electrocardiograph monitoring system based on Internet of Things

The invention relates to an electrocardiograph monitoring system based on Internet of Things. The electrocardiograph monitoring system comprises an electrocardiograph collecting module, a main intelligent mobile terminal and an electrocardiograph monitoring module, wherein the electrocardiograph collecting module is connected with the main intelligent mobile terminal, the bidirectional communication is realized between the main intelligent mobile terminal and the electrocardiograph monitoring module, the electrocardiograph collecting module comprises a signal collecting end, a data processing module and a signal transmitting module which are sequentially connected, the data processing module is used for processing the collected electrocardiograph signal and sending to the main intelligent mobile terminal through the signal transmitting module, the main intelligent mobile terminal is used for sending the electrocardiograph signal to the electrocardiograph monitoring module, the electrocardiograph monitoring module comprises a remote communication module, a diagnosis and analysis module and an electrocardiograph signal database, the remote communication module is used for communicating with the main intelligent mobile terminal, and sending the received electrocardiograph signal to the electrocardiograph database to be stored, and the diagnosis and analysis module is used for extracting the received signal of the electrocardiograph database, diagnosing and analyzing the signal, and sending the results to the main intelligent mobile terminal through the remote communication module after diagnosis and analysis.
Owner:CHENGDU UNIV OF INFORMATION TECH

Major formula analysis and discovery method

The invention discloses a major formula analysis and discovery method, relates to the technical field of traditional Chinese medicine special departments and diseases, doctors and literature major formula big data correlation research, and solves the problems that a correlation rule-based analysis method is relatively fuzzy for definition of correlation among medicines and a clustering-based major formula correlation research method has relatively large difference in eigenvalue extraction and similarity measurement methods in the prior art. According to the major formula analysis and discovery method, each knowledge unit is endowed with coordinate mapping by utilizing a deep learning technology based on a knowledge graph; distance information is fully utilized to reflect correlation among major formulae; the advantages of correlation rules, sample clustering and complex network community discovery can be integrated; and the advantages, namely, multi-dimensional knowledge graph presentation and knowledge reasoning, inexistent in the conventional method are achieved. A user can freely set a community quantity; and the method has remarkable superiority in common medicine pair semantic retrieval, visual traditional Chinese medicine community discovery, single medicine and basic formula correlation research.
Owner:GUANGDONG HOSPITAL OF TRADITIONAL CHINESE MEDICINE

Risk assessment method and device based on literature data and server

The invention provides a risk assessment method and device based on literature data and a server. The method comprises: acquiring all article abstract data of a biomedical literature database; establishing an abstract database of articles related to adverse reactions of certain antihypertensive drugs through limiting a medical subject heading; establishing a data index for measuring article quality related to the adverse reactions of the certain antihypertensive drugs; extracting information of articles, authors, journals, publication types and adverse reactions of the certain antihypertensive drug in each article related to the certain antihypertensive drugs according to the abstract database; and constructing a weighting heterogeneous graphic according to the extracted information and data and sorting to obtain ranking information of the adverse reactions of the certain antihypertensive drugs. The method provided by the invention can recommend the ranking information of the adverse reactions of all types of the antihypertensive drugs, so that the use risk of the antihypertensive drugs is evaluated, the cognition on the risks of the antihypertensive drugs by low-experience and grass-roots doctors and patients is improved, adverse events are reduced and the antihypertensive curative effect is improved.
Owner:李雪 +6

Brain function connectivity detection system and method based on self-adaptive priori information guidance

InactiveCN104921727AOvercomes the disadvantage of having little or no prior information availableOvercome limitationsMedical informaticsDiagnostic recording/measuringPrior informationResonance
The invention relates to a brain function connectivity detection system based on self-adaptive priori information guidance and a brain function connectivity detection method utilizing the system. The method comprises the steps that S1, blind source signal separation is performed separately on functional magnetic resonance data of all single-subjects in group-subjects collected by the same mask through an independent component analysis method, so independent functional components corresponding to all the single-subjects are obtained; S2, adaptive prior information used for guiding functional magnetic resonance data analysis on the group-subject and single-subject levels is extracted from the functional components corresponding to all the single-subjects; S3, by utilizing the adaptive prior information, based on a multi-objective optimization framework, in combination with a weight summing algorithm and a fast fixed-point algorithm, blind source signal separation is performed on the functional magnetic resonance data on the group-subject level, group functional components reflecting all subject commonalities in the group are obtained, so that brain function connectivity detection is completed. The brain function connectivity detection system can position a brain function connectivity area more accurately.
Owner:SHANGHAI MARITIME UNIVERSITY

Adaptive fixed-point IVA algorithm applicable to analysis on multi-subject complex fMRI data

The invention discloses an adaptive fixed-point IVA algorithm applicable to analysis on multi-subject complex fMRI data, and belongs to the field of biomedical signal processing. The algorithm comprises the following steps: estimating an SCV distribution of complex fMRI data by adopting an MGGD-based nonlinear function; adaptively estimating a shape parameter of an MGGD by adopting a maximum likelihood estimation method, and automatically matching the shape parameter and the variable SCV distribution; updating the MGGD-based nonlinear function in an SCV-dominated subspace to implement noise elimination of the complex fMRI data; adding a pseudo-covariance matrix of input data in an algorithm updating process, and further improving the pertinence of IVA on the complex fMRI data by directly utilizing a non-circular characteristic of the complex fMRI data. According to the algorithm, multi-subject complex fMRI data of which the noise level is high but the brain function information is most comprehensive can be effectively analyzed, and under the unfavorable conditions of great differences among subjects and low signal to noise ratio, better bases can be provided for brain function researches and brain disease diagnosis.
Owner:DALIAN UNIV OF TECH
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