45 results about "Resting state eeg" patented technology

A monitoring device, method and system are disclosed herein. The monitoring device is capable of determine when a user's wrist is at rest using a motion sensor disposed within a wrist module that is attached to the user's wrist. When at rest, the monitoring device utilizes a vital sign monitor to determine a plurality of vital signs of the user. The vital sign monitor preferably comprises a light source and photodetector in communication with a pulse-oximetry circuit. The motion sensor is preferably an accelerometer.

A system for monitoring physiology, having: a RADAR transmitter and a RADAR receiver; a state estimation module configured to process a returned RADAR signal to detect a presence of motion and set a motion state upon said presence of motion, said state estimation module configured to detect a presence of one or more physiological parameters including heartbeat and respiration, and said state estimation module configured to assign a still state or a concern state based on said presence of physiological parameters; a rate estimation module configured to estimate one or more estimated physiological rates including an estimated respiration rate and an estimated heart rate; and an alerting module configured to provide an alert if an alert value exceeds an alert value threshold, wherein the alert value is derived from at least one of the motion state, concern state, still state and the estimated physiological rates.

A scene change detector algorithm in image sequence is disclosed, in which a two-stage detecting process is applied to perceive a scene change in a precise and safe way. The algorithm includes classifying images into two different states, a transition state and a stationary state, after determining whether there is any change in adjacent frames, and confirming the scene change by rechecking whether there is the scene change in the classified frames.

Functional MRI (fMRI) methods are presented for utilizing a magnetic resonance tomograph to map connectivity between brain areas in the resting state in real-time without the use of regression of confounding signal changes. They encompass: (a) iterative computation of the sliding window correlation between the signal time courses in a seed region and each voxel of an fMRI image series, (b) Fisher Z-transformation of each correlation map, (c) computation of a running mean and a running standard deviation of the Z-maps across a second sliding window to produce a series of meta mean maps and a series of meta standard deviation maps, and (d) thresholding of the meta maps. This methodology can be combined with regression of confounding signals within the sliding window. It is also applicable to task-based real-time fMRI, if the location of at least one task-activated voxel is known.

The invention belongs to the technical field of autism examination, and relates to a resting-state EEG analysis system oriented to autism diagnosis. The system comprises a data collecting module for collecting EEG signals of a testee during an eye-open rest, a data preprocessing module for denoising resting-state EEG data, and a feature extracting module for calculating the correlation coefficientbetween preprocessed resting-state EEG topographical maps, conducting k-means blind clustering with the correlation coefficient as the feature parameter to obtain three resting-state EEG micro-statetopographical maps of three resting-state EEG micro-states, extracting the average duration time or average appearing frequency in each second of each type of micro-state to obtain a training data sample set and a testing sample set, obtaining a mode classifier according to a standard forward neural network structural design technology, and finally obtaining the diagnosis result of the autism riskthrough the obtained mode classifier. The auxiliary diagnosis of autism can be objectively and rapidly made, and the system is suitable for autism diagnosis of children in early development.

The invention discloses a brain function connection analysis method oriented to resting-state EEG signals. The method includes the following steps of S1, obtaining resting-state EEG data; S2, preprocessing the resting-state EEG data; S3, conducting micro-state analysis on the preprocessed resting-state EEG data; S4, determining the type of micro-state of each time point; S5, calculating function connection within the duration time range of each type of micro-state. By means of the method, the limitation of the existing brain function connection analysis technology of resting-state EGG signalsis overcome.

A rate adaptive pacemaker comprises a means (2) for determining the demand of the patient's organism, a pacing rate controlling means (16) for controlling the pacing rate in response to the patient's demand, and a pacing rate limiting means (20) for preventing the pacing rate from becoming too low. The pacing rate limiting means is adapted to limit the pacing rate downwards such that a first predetermined relation is satisfied between actual cardiac output (CO) and cardiac output (CO_rest) for the patient in rest conditions and a second predetermined relation is satisfied between actual stroke volume (SV) and rest stroke volume (SV_rest).

The invention relates to a method and a device for judging the sleep state, the rest state and the motion state. The 3D acceleration sensor module and the capacitance sensor module on the intelligent wearable device monitor the sleep state of the human body to record the motion data of the human body and carry out data transmission through the connection of the Bluetooth and the APP of the mobile terminal, so that the human health motion tracker of the intelligent wear device can be realized to automatically and reliably record the motion data and the sleep state of the human body.

The invention relates to a sleep monitoring method based on internet of things, the method comprises the steps of detecting whether or not the custodian has entered the monitoring area; detecting whether or not the custodian is lying in bed, gathering information of gravity acceleration and parameters of physiological characteristics; sending the information of gravity acceleration and the parameters of physiological characteristics to an external intelligent terminal; judging whether or not the custodian is in a motionless state using the intelligent terminal, according to the received information of gravity acceleration; judging whether or not the custodian is in a sleeping state using the intelligent terminal, according to the parameters of physiological characteristics; gathering the nightmare sounds or the state information of somniloquy and information of sleeping environment parameters; attaching time information on the information of gravity acceleration, the parameters of physiological characteristics, the nightmare sounds or the state information of somniloquy and the information of sleeping environment parameters using the intelligent terminal, and sending the information to a background server. The method can capture the sleeping quality and the physiological characteristics of the custodian in time without violating the privacy space of the custodian.

The invention discloses an emotion regulation ability survey method based on frontal lobe EEG lateralization and ERP. Resting state EEG signals and task state EEG signals are acquired; filtering and down-sampling preprocessing is performed on the acquired resting state EEG signals and the task state EEG signals; power spectrum feature extraction is performed; event related potential extraction isperformed; the lateralization coefficient B and the P300 difference coefficient E are calculated; if B>0 and E>0, the emotion regulation ability is great; if B>0 or E>0, the emotion regulation abilityis common; and if B<0 and E<0, the emotion regulation ability is poor. The diagnostic error caused by subjectivity can be avoided, the objective and reliable result based on the electroencephalogramcan be provided, and a set of perfect neural survey method can be obtained through further research so that the emotion regulation ability survey accuracy can be enhanced and the considerable social benefit and economic benefit can be acquired.

The invention discloses a Wi-Fi (Wireless Fidelity) based on commercial use. According to the gait recognition method with the independent direction of the Wi-Fi, a subject repeatedly walks for a specified distance in a preset area and passes through commercial Wi-Fi; the Wi-Fi device collects a CSI signal of the subject; motion detection is carried out according to different states of CSI signalPSD distribution in walking and static states by using PCA dimension-reduced data, and the walking direction of the subject is derived; STFT is carried out on the first ten principal component data after dimension reduction, a time domain signal is converted to a time-frequency domain, 10 spectrograms with the horizontal axis as time and the longitudinal axis as frequency are obtained, and the spectrograms are denoised through a two-dimensional Gaussian low-pass filter; according to the derived direction, mapping the spectrogram to the direction reflecting the real movement of the human body,and carrying out error correction and fusion to obtain a spectrogram; extracting manual features from the spectrogram, and obtaining automatic features from a Gabor filter; and inputting the two features into a support vector machine with a radial basis function to realize gait recognition.

The invention relates to an entity classification method for a data space and belongs to the natural language processing field. Under an evolutionary environment, entities cannot be classified through supposing that the entities are in static states. The method includes the following steps that: an improved and evolutionary K-Means clustering framework is put forwards for evolutionary data space entities, namely, a contour value and KL-divergence-based objective cost function is put forward; a novel data space entity similarity measure method is put forward; an evolutionary K-Means clustering algorithm is put forward according to a heuristic rule; and the evolutionary clustering framework put forward in the invention is further expanded, so that a condition that the number of clusters changes with the time or snapshot entities are added or deleted with the time can be processed. With the method of the invention adopted, the clustering result of current entities can be captured in a high-quality manner, and historical clustering situations can be reflected robustly.

The invention discloses a gyro failure autonomous diagnostic method of an inspector in a static state, and the method is capable of utilizing the given stable to-be-detected angular speed of the inspector in the static state and does not need attitude information and carries out failure diagnosis on four gyros. The method comprises the following basic flow procedures of firstly establishing a redundancy relation for the outputs of the four gyros, judging the consistency of the redundancy relation, judging a failure source resulting in inconsistency, and positioning a failed gyro through a failure diagnosis policy.

The invention discloses a feedback background extraction-based city traffic scene foreground target detection method. The method comprises the steps of S1, firstly building a background model by adopting the position of each pixel in a group of samples, and initializing the model by utilizing an interval frame; S2, installing a few counters and performing adaptive updating to describe a current traffic state and pixel stability; and S3, detecting a significant foreground, and updating the model by using an adaptive feedback learning rate. According to the feedback background extraction-based city traffic scene foreground target detection method, an FViBe method is superior to other methods in effect of handling the problem that a target slowly moves or is temporarily in a stationary state;and the FViBe method is effective and can realize significant foreground detection in real time in a complex city traffic scene.

A physiological monitoring device, a physiological monitoring method and a non-transitory computer readable storage medium for implementing the physiological monitoring method are provided. The physiological monitoring device has a blood pressure sensing module, a motion sensing module and a processor. The motion sensing module senses a physical state of the physiological monitoring device to generate a physical state signal accordingly. The processor, which is coupled to the blood pressure sensing module and the motion sensor module, determines whether the physiological monitoring device is in a stationary state and a horizontal state according to the physical state signal so as to determine whether an ideal measurement condition is met. When the ideal measurement condition is met, the processor controls the blood pressure sensing module to measure a blood pressure of the user.

The invention discloses a behavior identification system based on a probabilistic graphical model and a behavior identification method based on the probabilistic graphical model, equipment and a storage medium. The method comprises the steps that one or multiple sensor signals are acquired through one or multiple sensor processors; the original sensor signals of the time domain are processed through one or multiple sensor processors so as to acquire the feature vectors of the system; one binary state is determined through one or multiple sensor processors, wherein the binary state informationcan include stationary and movement, and partial features can be the signal energy or variance; if the non-stationary state is determined, all aspects of human activities are determined through one activity recognizer and processed based on the feature vectors. Only the feature vectors of the non-stationary state are processed so that the effective processing rate of the feature vectors can be enhanced. Besides, the behavior feature vectors are analyzed by using the probabilistic graphical model so that the detection accuracy can be enhanced and the influence of the observed time domain segmentation change can be reduced.

The invention relates to a motion injury early warning method based on laser speckle, which comprises the following steps: an original speckle image is shot and the elimination of shake preprocessingis carried out; the speckle image in a static state is taken as a reference initial value, so that the change caused by other uncertain factors such as noise in the measurement process is reduced; thelining ratio of the speckle image is calculated and comparative analysis under a time sequence is carried out; the blood dynamic index of the measured tissue is calculated, and the excessive motion is early warned. Based on the rapid laser speckle image processing algorithm, the information obtained in a time sequence is strong in correlation, the blood power indexes of local body tissues can beeffectively monitored, the local muscle group state of an athlete can be objectively evaluated, and compared with the existing motion monitoring tool, more attention should be paid to blood vessel andmuscle status monitoring of local body parts. The method has the advantages of good real-time performance, good safety, accurate evaluation effect, simple operation mode and the like.

The invention discloses a gait detection method suitable for a pedestrian with a smart phone. The gait detection method comprises the steps of judging whether the intelligent mobile phone is in a moving state and detecting the gait. Whether the smartphone is in a moving state comprises the following steps: 1, continuously acquiring three-axis acceleration information of the smartphone, and sequentially calculating three-axis combined acceleration according to the acquired three-axis acceleration information; 2, judging whether that triaxial resultant acceleration satisfy the first condition according to the result of calculating the triaxial resultant acceleration, and judging whether the second condition is satisfied before the first condition is satisfied. The first condition is a movement phenomenon of the smartphone, the second condition is whether the smartphone is in a stationary state, if the first condition and the second condition are not satisfied, the first step is returned,and if the first condition and the second condition are not satisfied, the gait detection step is executed. The method of the invention also does not adopt a detection threshold mode to carry out gait detection, and effectively overcomes the adverse influence caused by the difference of the walking modes of different people.