33 results about "Physiological significance" patented technology

An efficient method for the microfabrication of electronic devices which have been adapted for the analyses of biologically significant analyte species is described. The techniques of the present invention allow for close control over the dimensional features of the various components and layers established on a suitable substrate. Such control extends to those parts of the devices which incorporate the biological components which enable these devices to function as biological sensors. The materials and methods disclosed herein thus provide an effective means for the mass production of uniform wholly microfabricated biosensors. Various embodiments of the devices themselves are described herein which are especially suited for real time analyses of biological samples in a clinical setting. In particular, the present invention describes assays which can be performed using certain ligand/ligand receptor-based biosensor embodiments. The present invention also discloses a novel method for the electrochemical detection of particular analyte species of biological and physiological significance using an substrate/label signal generating pair which produces a change in the concentration of electroactive species selected from the group consisting of dioxygen and hydrogen peroxide.

The invention provides a method and a system for non-contact sleep monitoring. The method comprises the following steps: using an infrared monitoring camera to acquire video images of a head region and abdomen and thorax regions of a human body, and using a somatosensory device to acquire attitude information of the human body; acquiring sleep information of the human body; and comparing the sleep information of the human body with preset standard sleep information, and analyzing health degree of human body sleep quality. The method is based on an image processing technology, and acquires sleep signals of a human body by a non-contact method. The method maintains natural sleep state of a user to the greatest extent, and sleep state information with physiological significance is extracted through a video image processing technology. The system is low in device cost, and provides convenience for daily use of a family.

The invention discloses a method for automatically removing movement artifacts of near-infrared spectral signals. The method includes steps of firstly, computing movement standard deviation of the near-infrared spectral signals, automatically selecting threshold values and marking movement artifact regions of original signals; secondly, decomposing empirical modes of the near-infrared spectral signals to obtain intrinsic mode functions with clear physiological significances; thirdly, computing suspected movement artifact regions of each intrinsic mode function, combining the suspected movement artifact regions with the movement artifact regions, which are obtained in the first step, of the original signals, processing the different intrinsic mode functions and removing the movement artifacts; fourthly, reconstructing the signals. The method has the advantages that the threshold values are automatically selected according to probability distribution features of the near-infrared spectral signals, and intervals containing the movement artifacts are automatically detected according to the probability distribution features of the near-infrared spectral signals; the empirical modes of the near-infrared spectral signals are decomposed, accordingly, the intrinsic mode functions with the clear physiological significances can be obtained, obviously abnormal intrinsic mode functions can be processed, and the movement artifacts can be removed while physiological information in the NIRS (near-infrared spectral) signals can be effectively reserved.

The invention relates to a reduction responsiveness nanoscale graphene oxide coated medicine carrying mesoporous silicon dioxide nano particle and a preparation method thereof. The reduction responsiveness nanoscale graphene oxide coated medicine carrying mesoporous silicon dioxide nano particle consists of mesoporous silicon dioxide nano particles and nanoscale graphene oxide, wherein the mesoporous silicon dioxide nano particles are modified by a compound which simultaneously contains a disulfide bond, an amino group and carboxyl and can load medicines, and the nanoscale graphene oxide coats the mesoporous silicon dioxide nano particles. The preparation method comprises the following steps: adding the mesoporous silicon dioxide nano particles which are modified by the compound which simultaneously contains the disulfide bond, the amino group and the carboxyl and can load medicines into carboxyl activated nanoscale graphene oxide aqueous solution, stirring at room temperature to generate amidation reaction, carrying out centrifugal washing, and carrying out freeze drying to obtain a medicine controlled release material which is modified by the nanoscale graphene oxide and has reduction responsiveness. The carrier material of the invention has good biocompatibility and high medicine loading capacity, and the controlled release of the medicine can be realized by the reduction responsiveness of the disulfide bond, the medicine hardly releases around normal cells but quickly releases under the microenvironment of tumor cell reduction, and the nano particle has important physiological significance and research value.

The invention provides a physiological signal detection system based on multi-channel flexible integration. The physiological signal detection system is characterized by comprising n physiological signal detection channels, each physiological signal detection channel is provided with a single-channel feature extraction module, the output end of each physiological signal detection channel is connected with a soft-decision evidence generating module, the soft-decision evidence generating modules of the n channels are all connected in a multi-channel information integration module, and the output end of the multi-channel information integration module is connected with a diagnosis decision-making module. The physiological signal detection system has the advantages that the conflicting information integration problem existing during multi-channel binary detection is solved, robust detection of weak electrophysiological feature signals is achieved, the physiological significance of the channels are retained completely in the feature extraction process, the system conforms to the interpretation habit of a doctor, experience threshold values summarized for a long term can be clinically and normally used in the soft-decision process, and the system has very good universality for multi-channel detection of weak electrophysiological signals.

The invention provides a method and device for constructing a brain disease classification model, and an intelligent terminal. The brain disease classification model construction method comprises the steps of obtaining Rs-fMRI image data from multiple brain disease data sets; based on a plurality of preset brain templates, preprocessing the Rs-fMRI image data to obtain an average time sequence; constructing a functional connection network corresponding to each brain disease data set and the brain template according to the average time sequence, and extracting features from the functional connection network to obtain a feature matrix; constructing a plurality of initialized brain disease classification models corresponding to each brain disease data set according to a preset sparse learning model, a regularization item and a feature matrix; and performing integrated learning on the plurality of initialized brain disease classification models, and determining a target brain disease classification model corresponding to each brain disease data set. According to the method, the constructed brain function connection network has more physiological significance, the problem of high heterogeneity in data can be effectively solved, and the universality of a brain disease classification model is improved.

The invention provides a dynamic PET (Positron Emission Tomography) image reconstruction and tracing kinetic parameter estimation method based on image segmentation. A statistics reconstruction model is combined with a model with physiological significance can be combined to more accurately combine a reconstruction result. When reconstruction and segmentation are coupled to one a combined or concurrent solving frame, for a segmentation task, the information of a noise model for carrying out modeling on original projection data can be obtained, and a segmentation-based result can enhance homogeneity in each area so as to realize a reconstruction result which more conforms to a true situation. Compared with other algorithms which singly reconstruct PET images or estimation kinetic parameters, the method can obtain a good reconstruction result. Through the combination of the performance of the method in simulated data experiments and true data experiments, compared with other algorithms which singly reconstruct PET images, the estimation kinetic parameters and image segmentation, the method disclosed by the invention can obtain a good reconstruction result.

The invention discloses a baseline identification method for a fetal heart rate curve. A device for implementing the method comprises a signal input detecting module, a baseline identification extracting module, a baseline value parameter calculating module and a baseline value parameter output module. The method comprises the following steps: first, inputting fetal heart rate signals, performing pre-calculation, average optimization and interpolation smoothing in the baseline identification extracting module through pre-treatment and extraction of the effective signals, and calculating a baseline value; then, calculating the baseline proportion in a range and descriptive statistical parameters in a parameter calculating module; finally, outputting the calculating result in the baseline value parameter output module. The baseline value is calculated according to fetal heart rate data of long-term fetal heart rate monitoring and the definition of a fetal heart rate baseline, and has a relatively specific physiological significance; through combination of accelerating, decelerating and variance classifications, counts and duration proportions of the fetal heart rate, more comprehensive information can be obtained from the fetal heart rate signals; through application of the extracted baseline valve, the frequency domain of the fetal heart rate and related entropy information can also be obtained.

The invention relates to a sleeping state recognition classification method based on electrocardiogram data. A dichotomous multilayer neural network is used for performing recognition and classification on SAHS patients and healthy individuals; then, a deep neural network and binary tree decision fusion method is used for decomposing a multi-classification problem into a plurality of dichotomous problems; for each dichotomous model, different optimum feature groups are respectively obtained through feature screening, so that the sleeping states of two groups and the sleeping modes of the SAHSpatients can be better recognized. The time domain of the electrocardiogram waveform and the heart rate variability information are used for performing SAHS screening, breathing mode and sleeping state recognition; the electrocardiogram signal waveform is stable; the physiological significance is clear; meanwhile, the measurement is convenient; the physiological load of patients is greatly reduced; the portable sleeping monitoring can be favorably realized; a wide application range is realized.

The invention discloses a construction method of a zebra fish animal model for cardiovascular disease drug screening, and specifically relates to the field of animal model construction methods. The method comprises the following specific steps: selecting a gRNA target site of a zebra fish heg1 gene and synthesizing gRNA in vitro; in order to obtain the target site, firstly, downloading genome dataof the target gene heg1 from a zebra fish genome database; and selecting a 529bp long target sequence near a first exon of the heg1 gene and inputting the sequence into a target point website to obtain a target point primer. According to the invention, a cardiovascular malformation zebra fish mutant with genetic stability can be obtained; by using the zebra fish embryo transparency and the autonomous oxygen dissolving property, the mutant can be used for screening and verifying the influence of different cardiovascular drugs on cardiovascular development; and the zebra fish animal model construction method provides important physiological significance and specificity for screening of cardiovascular disease treatment drugs.

The invention generally relates to a device for assessing dynamic microvascular refill (DMR), a novel measure of microvascular function. Microvascular refill is determined under dynamic conditions by monitoring changes in fingernail reflectance spectra in response to small shear forces applied to the fingernail. A hemodynamic model is described to examine the physiological significance of observed signals. The invention will provide healthcare workers with a simple, user friendly, non-invasive method of rapidly assessing microvascular function that would greatly facilitates the early detection and monitoring of the onset and treatment of vascular diseases.

The invention relates to an analyzing method for heart rate variability based on a linear amplitude spectrum. The method comprises the following steps that a target heart rate variability signal is resampled, and a uniform time interval sequence is obtained; discrete time Fourier transform is conducted on the uniform time interval sequence; according to the discrete time Fourier transform, the linear amplitude spectrum is calculated and obtained; according to the linear amplitude spectrum, data points in an extreme low-frequency stage, a low-frequency stage, a high-frequency stage and a totalfrequency stage are summed, and five analyzed parameters of the heart rate variability frequency-domain, such as an extreme low-frequency amplitude parameter, a low-frequency amplitude parameter, a high-frequency amplitude parameter, a total amplitude parameter and a balance specific parameter are calculated. The analyzing method is reasonable in design, a time domain signal is transformed into the frequency domain, and through linear amplitude spectrum transform, the frequency domain amplitude of the signal reflects the real amplitude in the signal time domain; the frequency domain characteristic is used for analyzing of physiological signals of HRV and the like, and the significant physiological significance is achieved on analyzing frequency and intensity of regulating HRV by the sympathia-parasympathetic nerve through the calculated frequency domain parameters.

In diabetes management applications, recent glucose measurement values may be clinically useful for acute diagnosis and for the calculation of a corrective therapy. However, the physiological significance of these measurements can lessen with time, which can lead to their becoming less relevant for calculating an effective therapy. A technique is presented for emphasizing analyte values in a diabetes management application during a therapeutic window. In this way, a user is notified that the glucose measurement value is the most current measurement and remains usable for therapy applications,such as an insulin bolus calculation.