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.

The present invention provides a health care support system that can give appropriate advice depending on a situation.At first step, whether a driver is seated is checked using a signal from a pressure sensor. At second step, whether the driver is in a resting state is checked using heart rate and the like. At third step, physical information is acquired. At forth step, a health status is judged based on the measured pieces of physical information and the like. At fifth step, activity status information is acquired. At sixth step, an activity status is judged based on the activity status information. At seventh step, an appropriate piece of advice for the driver is selected based on a judgment result of the health status and a judgment result of the activity status. At eighth step, the selected piece of advice is displayed on a displaying section and audibly outputted.

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.

The present invention provides apparatus and methods for improving satellite navigation by assessing the dynamic state of a platform for a satellite navigation receiver and using this data to improve navigation models and satellite tracking algorithms. The dynamic state of the receiver platform may be assessed using only accelerometer data, and does not require inertial navigation system integration. The accelerometers may not need to be very accurate and may not need to be aligned and / or accurately calibrated. A method of accelerometer assisted satellite navigation may comprise: detecting a stationary condition of an accelerometer set; gross-calibrating the accelerometer set when in the stationary condition, wherein gross-calibrating includes determining a combined-bias of the accelerometer set; defining at least two acceleration levels using the combined-bias; collecting accelerometer data; categorizing the accelerometer data into one of the acceleration levels; and utilizing the categorized accelerometer data to enhance satellite navigation.

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.

A non-contact method for detecting physiological signals and motion in real time comprises: sensing a portion of a human body to generate a series of image frames by an optical sensing element; determining a complexion region from each of the image frames; then calculating a central coordinate of the complexion region; determining a sample block by expanding a surrounding range from the central coordinate, for getting a complexion fluctuation waveform; comparing said central coordinate with a central coordinate of a later image frame, to calculate a displacement in a unit time for determining whether the portion of the human body is in a static state. In the static state, an operating unit performs noise filter and peak detecting for complexion fluctuation waveform, and calculates a physiological signal. Said method can reduce the amount of data required to be processed in physiological signal detection, and improve the accuracy of the detection.

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 (COrest) for the patient in rest conditions and a second predetermined relation is satisfied between actual stroke volume (SV) and rest stroke volume (SVrest).

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 discloses a mathematics supernormal teenager brain functional connection network analysis method based on resting state EEG signals. The method comprises: based on a resting state EEG signal, using a phase lag algorithm based on Wilcoxon signed-rank test (PLWT) to establish a connection network, so as to measure the connection network using graph theory indexes, and depicting connection network characteristics of mathematics supernormal teenagers, including active brain regions and graph theory indexes. Compared with the prior art, the method has the following advantages: comparing the connection network characteristics of mathematics supernormal teenagers, depicted based on the resting state EEG signals, with that based on task state EEG signal analysis, obtained characteristics are simpler and more universal, and the applied PLWT method performs phase synchronization determination of EEG signals by a Wilcoxon signed-rank test framework, to explore internal connection network, prevent subjective setting of a threshold value, and reduce undetermined factors in the connection network analysis.

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.

A system and method for measuring a functional lateralization of a subject brain is provided. The method includes providing a set of functional magnetic resonance imaging (fMRI) data acquired during a resting state of a subject, and selecting a plurality of seed voxels associated with locations in hemispheres of a brain of the subject. The method also includes determining a degree of within-hemisphere connectivity for each seed voxel using the fMRI data, determining a degree of cross-hemisphere connectivity for each seed voxel using the fMRI data, and computing an autonomy index for each seed voxel using the degree of within-hemisphere connectivity and the degree of cross-hemisphere connectivity, wherein the autonomy index is indicative of a connectivity asymmetry between the hemispheres. The method further includes generating a report indicative of a specialization profile determined for a region of interest in the brain of the subject.

The invention discloses a target motion and rest state judging method based on the passive multi-point positioning technology. The method can perform judgment through a signal measured value or multiple measured values. The judgment step with the signal measured value comprises computing the geometric accuracy factor matrix of the single measured value and utilizing the geometric accuracy factor matrix to normalize the measured value to obtain a judgment statistic. The judgment step with the multiple measured values comprises subtracting the N-1th measured value from the Nth measured value, composing a column vector according to x-axis data and y-axis data, structuring an associated matrix of the vector by combining with the geometric accuracy factor matrix computed from the measured values to normalize the column vector and further to structure a judgment statistic, and performing judgment according to a threshold value. The target motion and rest state judging method based on the passive multi-point positioning technology is based on statistical hypothesis testing and combined with an airport surface monitoring multi-point location system, thereby being high in system flexibility and robustness, small in calculating amount, good in detecting performance and capable of performing judgment flexibly through the single measured value and the multiple measured values.

The invention is suitable for the technical field of EEG signal processing, and provides a method and apparatus for extracting features from resting state EEG data. The method comprises the followingsteps: obtaining a EEG signal segment of a subject in the resting state, and calculating a global energy spectrum corresponding to the EEG signal segment; determine a micro-state corresponding to eachtime point in the EEG signal segment according to the global energy spectrum to obtain a micro-state sequence; and using a sliding time window algorithm to describe the micro-state sequence, and using micro-states whose duration are greater than a first time threshold in a first micro-state sequence sub-segment as main features of the first micro-state sequence sub-segment to obtain a characteristic sequence, wherein the first micro-state sequence sub-segment is a micro-state sequence sub-segment described by the current sliding time window. The technical solution provided by the invention uses the sliding time window algorithm to describe the micro-state sequence, can improve the effectiveness of the obtained feature sequence, reduces the amount of calculation, and reduces the influenceof noises on the data.

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.

Over 350 million people worldwide suffer from depression, a third of whom are medication resistant. Seizure therapy remains the most effective treatment in depression, even when many treatments fail. The utility of seizure therapy is limited due to its cognitive side effects and stigma. The biological targets of seizure therapy remain unknown, hindering design of new treatments with comparable efficacy. Seizures impact the brains temporal dynamicity observed through electroencephalography. This dynamicity reflects richness of information processing across distributed brain networks subserving affective and cognitive processes. We investigated the hypothesis that seizure therapy impacts mood (depressive symptoms) and cognition by modulating brain temporal dynamicity. We obtained resting-state EEG from thirty-four patients (age=46.0±14.0, 21 females) receiving two types of seizure treatments—electroconvulsive therapy or magnetic seizure therapy. We employed multi-scale entropy to quantify the complexity of brain's temporal dynamics before and after seizure therapy. We discovered that reduction of complexity in fine time scales underlined successful therapeutic response to both seizure treatments. Greater reduction in complexity of fine time scales in parieto-occipital and central brain regions was significantly linked with greater improvement in depressive symptoms. Greater increase in complexity of coarse time scales was associated with greater decline in cognition including the autobiographical memory. These findings were region- and time-scale specific. That is, change in complexity in occipital regions (e.g., O2 electrode or right occipital pole) at fine time-scales was only associated with change in depressive symptoms, and not change in cognition, and change in complexity in parieto-central regions (e.g., Pz electrode or intra and transparietal sulcus) at coarser time-scale was only associated with change in cognition, and not depressive symptoms. Finally, region and time-scale specific changes in complexity classified both antidepressant and cognitive response to seizure therapy with good (80%) and excellent (95%) accuracy, respectively. In this study, we discovered a novel biological target of seizure therapy; complexity of the brain resting-state dynamics. Region and time-scale dependent changes in complexity of the brain resting-state dynamics is a novel mechanistic marker of response to seizure therapy that explains both the antidepressant response and cognitive changes associated with this treatment. This marker has tremendous potential to guide design of the new generation of antidepressant treatments.

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.

A blood pressure measurement device measures a pulse period of a patient and includes a memory, a threshold calculation section, a rest / unrest condition determination section, and a display unit. The memory stores data of the measured pulse period and data of a pulse period of past measurement of the patient. The threshold calculation section calculates a threshold value range based on the data of the pulse period of the past measurement of the patient. The rest / unrest condition determination section determines whether the patient was under the rest condition or the unrest condition during blood pressure measurement by comparing the measured pulse period with the threshold value range. The display unit displays the blood pressure measured and whether the blood pressure was measured when the patient was in the rest condition or the unrest condition.

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.