187 results about "Biomedical signal" patented technology

An apparatus for controlling emotion of a driver includes an emotion sensor unit configured to collect a biomedical signal from the driver, and generate biomedical information data based on the collected biomedical signal, a user memory unit configured to store driver information that includes biomedical signals for respective emotional states of the driver and a plurality of correspondence contents, and deliver the driver information and the correspondence content in response to a received request, and an emotion management unit configured to determine the emotional state of the driver from the driver information received from the user memory unit and the biomedical information data received from the emotion sensor unit, request a correspondence content corresponding to the determined emotional state of the driver from the user memory unit, and provide the driver with the content received from the user memory unit.

An active dry sensor module for measurement of bioelectricity is disclosed. The active dry sensor module of the present invention excludes the use of a conductive gel, thereby not supplying unpleasantness and discomfort to a reagent and preventing the interference of the signal due to a noise component. Further, the active dry sensor module of the present invention amplifies the biomedical signal to a desired level, thereby precisely and easily measuring the biomedical signal.

Methods and systems that (a) adaptively segment a raw data stream(s) of different type biomedical signals; (b) assign attribute values to each segment; (c) determine an attribute domain based on a point corresponding to said segment attribute value; (d) generate a cluster set(s) in said attribute domain that includes a combination of points; (e) obtain a probability of order of appearance of each cluster point, according to its property value; (f) use said probability to update each point's property value; (g) repeat (d) through (f) while varying combinations of points in clusters according to their most updated property values and points derived from additional raw data stream adaptive segmentations; (h) associate at least one updated cluster with a normal / abnormal physiological state based on reference clusters in said domain; and (i) obtain the change probability between normal / abnormal physiological states using said probability of the order of appearance.

A biomedical device for continuous or intermittent monitoring of vital signs, such a arterial blood pressure, pulse oxymetry, etc., comprises two components connected by a wireless link. The first component is an electronic bracelet attached to a patient, while the second one is a smartphone that controls the first component and receives from it biomedical signals. The bracelet carries various sensors and actuators to enable and acquiring medical signals. The smartphone has an app that commands the bracelet and then receives and processes data and takes further actions, like enabling an alarm, plotting data, calling an emergency service or doctor office.

A method of analysing biomedical signals, for example electrocardiograms, by using a Hidden Markov Model for subsections of the signal. In the case of an electrocardiogram two Hidden Markov Models are used to detect respectively the start and end of the QT interval. The relationship between the QT interval and heart rate can be computed and a contemporaneous value for the slope of this relationship can be obtained by calculating the QT / RR relationship for all of the beats in a sliding time window based on the current beat. Portions of electrocardiograms taken on different days can efficiently and accurately be compared by selecting time windows of the ECGs at the same time of day, and looking for similar beats in those time windows.

The invention provides a portable health-monitoring device with the function of motion compensation, comprising: a motion sensor (5) for detecting the motion signal of detected people; a biomedicine sensor (1) for detecting the biomedicine signal of detected people; and a motion compensation module (14) coupled to the motion sensor and the biomedicine sensor for compensating the biomedicine signal via the motion signal when the motion signal is less than a threshold value detected by the motion sensor, and the outputs the compensated signal as well as memories said output value of the portable health-monitoring device; in addition, when the motion signal is bigger than the threshold value detected by the motion sensor, the motion compensation module uses the memorized output value as the output of said portable health-monitoring device. The invention also provides a relative method for aforementioned device.

Provided is a film-type biomedical signal measuring apparatus configured in a such a way that a plurality of metallic thin film electrodes and a circuit unit are formed on a film-type piezoelectric element so as to easily attach the apparatus to the skin and an electrical signal as well as an electrical signal of a human body is simultaneously measured using the plurality of metallic thin film electrodes and the circuit unit. Accordingly, the film-type biomedical signal measuring apparatus simultaneously measures electrocardiogram (ECG) and ballistocardiogram (BCG) from the simultaneously measured electrical signal and vibration signal of the human body and extracts biomedical information of various types of health indexes such as a heart rate, a stress index, BCG, a blood pressure, an amount of physical activity, a respiration rate, and VO2max from the two different biomedical signals.

The invention relates to the field of treatment of biological and medical signals, in particular to a device for automatically identifying a fetal heart rate baseline and a method for realizing the device. The method provided by the invention comprises the steps of: collecting fetal heart rate data within a preset time length to obtain a fetal heart rate data series h'(n); preprocessing the collected fetal heart rate data series h'(n) to obtain respective fetal heart rate data series corresponding to the preprocessing process; selecting a main dominant peak value according to the frequency distribution of the respective fetal heart rate data series corresponding to the preprocessing process; and performing dynamic baseline identification according to the respective fetal heart rate data series corresponding to the preprocessing process and the main dominant peak value to obtain, display and print a dynamic baseline. According to the technical scheme of the device provided by the invention, the influence of regular changes of fetal heart rate on baseline solutions can be effectively avoided, and the baseline level and the changes thereof of a fetus under different conditions can beaccurately reflected.

An ultrasound diagnostic apparatus comprises an ultrasound probe having a plurality of piezoelectric transducers to transmit ultrasound waves to a subject and to receive echoes from the subject, a driving unit which generates a plurality of driving signals corresponding to the plurality of piezoelectric transducers to generate the ultrasound waves therefrom, a Doppler signal detecting unit which detects Doppler signals based on the echoes, a spectrum data generating unit which generates spectrum data based on the detected Doppler signals, and a display unit which displays the spectrum data. The ultrasound diagnostic apparatus further comprises a controller which controls the driving unit to switch, in synchronization with a biomedical signal of the subject, a high-power mode in which amplitude of the driving signal is relatively high and a low-power mode in which amplitude of the driving signal is relatively low.

The present invention relates to general diagnostic and real-time applications of discrete Hermite functions to digital data. More specifically, the invention relates to methods and systems for the application of dilated discrete digital Hermite functions (DDHF) to biomedical data, for example, to extract features from digital signals, including but not limited to, ECGs, EMGs, EOGs, EEGs, and others, by expanding the measured signals using a computationally efficient technique. These digital Hermite functions form the basis for the new discrete Hermite transform, generated on a beat by beat basis, which provides information about the shape of the signals, such as that in the BCG artifact in an EEG or in an ECG interval, or any noise in any other electrical signal. An automated system and method for real-time interpretation of any abnormalities present in a digital biomedical signal is provided.

The invention discloses an electroencephalographic-signal-based fatigue state recognizing method. The method comprises the steps that: an electroencephalographic (EEG) data acquisition instrument records electroencephalographic signals in different fatigue states from the surface of a human scalp; a signal acquisition analog circuit filters out interference factors from the signals, performs program-controlled amplification on weak electrical signals and removes level drifts to obtain analog electroencephalographic signals; a digital circuit performs analog / digital (AD) conversion on the electroencephalographic signals to obtain digital electroencephalographic signals, and the digital electroencephalographic signals are transmitted to a host and processed; and the host preprocesses the signals first, then extracts feature information from each channel of signals to establish a feature vector, and finally evaluates the fatigue degree according to the obtained electroencephalographic feature by an evaluation method based on fuzzy pattern recognition. By combining a biomedical signal processing technology and a fuzzy pattern recognizing technology, the invention provides an objectiveand feasible mental fatigue evaluating method, so the application of the electroencephalographic-signal-based detection and recognition to the field of mental fatigue evaluation is technically improved greatly.

In a body surface bio-potential sensor, and an apparatus for detecting biomedical signals having the same, the body surface bio-potential sensor includes a flexible membrane having a wire layer, a plurality of electrodes attached on a first surface of the membrane at predetermined intervals, each of the plurality of electrodes having a plurality of needles on a surface thereof, each of the plurality of needles having a predetermined height, and a cohesive layer covering the first surface of the membrane, the cohesive layer exposing regions of the flexible membrane corresponding to positions of the plurality of electrodes.

The invention discloses a dynamic electrocardiogram (ECG) T wave alternate quantitative analysis method based on models, which belongs to the technical field of biomedical signal processing. The dynamic electrocardiogram (ECG) T wave alternate quantitative analysis method includes steps of preprocessing 12 lead ambulatory electrocardiograms of a patient at first and removing random disturbance including baseline drift, power frequency disturbance, myoelectricity noise and the like; building various channel electrocardiosignal state space models and realizing robust estimation to electrocardiosignal waveforms by the aid of a dynamic multi-scale state estimation theory; applying a multi-sensor data fusion method to realize T wave fusion extraction and realizing T wave quantitative description; and finally realizing quantitative analysis for T wave alternate signals according to the analytic function of T waves. The dynamic electrocardiogram T wave alternate quantitative analysis method has the advantages that on the basis of the electrocardiosignal state spatial models, T wave quantitative analysis is realized at first, then dynamic electrocardiogram T wave alternate real-time detection and analysis are realized, and accordingly the dynamic electrocardiogram T wave alternate quantitative analysis method is convenient for catching T wave alternate electrocardio abnormal conditions suddenly caused in daily life and increases detecting level and diagnosis ability to patients in danger of sudden cardiac death.

A new apparatus of pure digital medical amplifier used for clinical and non-clinical biomedical signal acquisition purposes is disclosed, which include: multiple single-stage amplification buffers connected to biomedical signal inputs for receiving and buffering various biomedical and reference signals, one or more high resolution analog-to-digital converter whose analog inputs connected to the output of said buffers for digitizing biomedical signals, one or more digital signal controller whose inputs / outputs are connected to said buffers and analog-to-digital converters for receiving the digitized biomedical signal data and controlling the processing and output of said digitized biomedical signal data according to preset control program or user's commands. By real-time or non-real-time method said digital signal controller does the following signal processing on the digitized biomedical signal data: filtering, compressing, decompressing, encoding, decoding, transferring, storing and retrieving, analyzing, and displaying the required said digitized biomedical signal data. This invention of amplifier requires only a few electronic components, and yields extremely low internal noise, extremely low distortion of analog output, extremely high external noise rejection and immunity, extremely high flexibility in filtering frequency setting for various biomedical signals.

The invention discloses an algorithm for automatically recognizing a fetal heart rate baseline. The algorithm comprises the following steps: S1, collecting fetal heart rate signals; S2, performing curve optimization to the collected fetal heart rate signals; S3, performing feature extraction to the fetal heart rate signal curve after optimization, and clustering the extracted feature points into two feature parts through a clustering analysis algorithm; S4 distinguishing the two feature parts into a baseline part and a non-baseline part according to the distinguishing conditions; and S5, performing smooth filtering to the recognized baseline part to obtain the final estimated fetal heart rate baseline. The algorithm for automatically recognizing the fetal heart rate baseline can determine the fetal heart rate baseline more accurately by combining the fetal movement information particularly under the condition of close results of clustering, provide more intelligent and more reliable fetal heart monitoring results for clinic, and can be widely applied to the fields of biomedical signal processing, artificial intelligence and electronic fetal monitoring.

When an X-ray CT apparatus including a two-dimensional X-ray area detector performs a conventional (axial) scan or a cine scan, a plurality of segments of projection data items detected synchronously with an external signal or a biomedical signal is sampled from projection data items acquired during one scan or a plurality of scans. Projection data items corresponding to those detected during a half scan of rotating an X-ray tube by an angle of 180° plus the angle of a fan beam falling on the detector or during a 360° full scan are used to reconstruct a three-dimensional image. Thus, a three-dimensional tomographic image enjoying a high temporal resolution is displayed. Moreover, successive three-dimensional display images showing states of a subject synchronized with respective phases of the external signal or biomedical signal may be displayed as a four-dimensional image.

A wearing-type movement assistance device includes a frame to be worn on an upper limb of a wearer, a drive unit provided in each of joints of the frame, a biomedical signal detection unit detecting a biological potential signal of the wearer, a joint angle detection unit detecting an angle of each joint, and a control unit controlling the drive unit based on the biological potential signal and the angle of each joint. The frame includes a vertical frame, a shoulder frame that is laterally provided in a shoulder width direction from an upper end of the vertical frame, a first arm frame having one end coupled to the shoulder frame via a shoulder joint unit, and a second arm frame having one end coupled to the other end of the first arm frame via an intermediate joint unit and the other end connected to an elbow joint unit.

The invention relates to an intelligent biological signal anomaly detection method and a system capable of carrying out efficient, accurate and low-cost automatic analysis on biological signals. The system comprises a model training stage and a model application stage, wherein the model training stage comprises the following steps: acquiring a biological signal; preprocessing the acquired signal;sending the preprocessed data to a CNN-BilSTM-Attention combination model to extract features, wherein the signal firstly enters a CNN convolution neural network to extract the spatial features, thenenters a two-way long-short time memory network to extract time features, then passes through each network connection layer and finally passes through a SoftMax layer to calculate the final result, each link of the network is established, and the emphasis degree of different sequence points of signals are optimized by setting an Attention mechanism; calculating loss of the network module until themodule loss is less than the threshold, then the model training is completed, and otherwise Adam is used to optimize model parameters. The invention effectively improves the analysis effect of biomedical signals.

The invention discloses a human body fall detection method based on an acceleration sensor and relates to the technical field of biomedical signal processing. The human body fall detection method is based on a double check human body fall detection algorithm, wherein primary fall detection comprises the following steps: extracting a threshold value set according to pre-acquired activities of dailyliving (ADL) and fall acceleration sample data; then acquiring ADL acceleration data of a human body in real time, and extracting variance of the data as a dynamic threshold value for partially updating a pre fall behavior threshold value specific to a user; and setting the threshold value as a human body fall judgment standard, and performing primary detection on a human body fall behavior; in aprimary fall detection process, once a fact that a pre fall behavior happens is detected, human body behavior data t seconds before the pre fall behavior is sent onto a server near a data source, andsecondary fall detection judgment based on SVM is performed; and finally according to a dual fall judgment result, a human body fall event is determined, and the threshold value set of a fall detection algorithm is dynamically updated to be used for follow-up fall detection on the user.

The invention discloses a waveform generation method of biomedicine signals and a device. The device comprises a calculating module, a memory module, a display module and a waveform generation and control module; wherein the waveform generation and control module comprises a test signal generation module, a test plan generation module and a signal waveform output module; wherein the test signal generation module provides user selection basic signal types, adjusts parameters and descriptions and generates test signals; the test plan generation module provides user selection test signals, adjusts time sequences and descriptions of testing procedures and generates test plans; the signal waveform output module carries out digital operation on the test signals or the test plans to obtain waveform amplitude data and description information, carries out digital-to-analog conversion on the waveform amplitude data, outputs waveform diagrammatic presentation and simultaneously outputs waveform display or description display. The waveform generation device of the invention can output random biomedicine waveform signals according to the needs of the users so as to meet more extensive needs of the users.

The invention relates to the field of biomedical signal processing, in particular to a signal detection method and device based on multi-chip ultrasonic sensors. The method comprises the following steps: dividing the multi-chip ultrasonic sensors into M groups according to the preset standard; carrying out fetal heart signal acquisition in preset duration; obtaining the signal quality parameter of each chip group according to the acquired fetal heart signal; obtaining the chip group with the best signal quality through comparison; carrying out fetal heart signal acquisition and output on the chip group and stopping acquisition of other chip groups; detecting whether the signal quality parameter of the chip group is less than the preset threshold every preset duration, if so, repeating theabove steps, and if no, continuing fetal heart signal acquisition and output. The method and the device have the following beneficial effects: the burdens of the medical workers are lightened; more accurate intermediate processing data are obtained; meanwhile, the sound field combined by the chips is uniform; and only a group of chips work in a combined manner in the normal custody process, thus neither increasing power consumption nor increasing ultrasonic output quantity.

A dry electrode for biomedical signal measuring sensor includes a conductive sponge, a conductive fabric, and a thin metal film. The conductive fabric covers the whole conductive sponge, and the thin metal film is disposed on one face of the conductive fabric opposite to the conductive sponge. When using the dry electrode in measuring biomedical signal, it is not necessary to apply a conductive gel on a patient's skin, at where the biomedical signal is to be measured. Without the need of applying the conductive gel, the dry electrode is readily for measuring biomedical signal at any time and can be conveniently used in measuring signal over a long period of time without the problem of an attenuated signal due to gradually becoming dried conductive gel.

The invention provides a portable health-monitoring device with the function of motion compensation, comprising: a motion sensor (5) for detecting the motion signal of detected people; a biomedicine sensor (1) for detecting the biomedicine signal of detected people; and a motion compensation module (14) coupled to the motion sensor and the biomedicine sensor for compensating the biomedicine signal via the motion signal when the motion signal is less than a threshold value detected by the motion sensor, and the outputs the compensated signal as well as memories said output value of the portable health-monitoring device; in addition, when the motion signal is bigger than the threshold value detected by the motion sensor, the motion compensation module uses the memorized output value as the output of said portable health-monitoring device. The invention also provides a relative method for aforementioned device.

Disclosed herein is a visual stethoscope in which objective diagnosis is possible because a patient and his or her guardian can listen to thoracic and heart sounds also can see the waveform of the data at the time of diagnosis, and reliable diagnosis is possible because clinical data and a medical prescription based on a thoracic and heart sound database and diagnostic software are generated. The visual stethoscope includes a sound collector, a controller and an output unit. The sound collector collects biomedical signals generated from a human body and converts them into electric sound signals. The controller receives and amplifies thoracic and heart sounds output from the sound collector, performs filtering with respect to a predetermined frequency, or performs data conversion and processing, and outputs them. The output unit receives signals output from the controller, and outputs the signals as sound, or diagnoses, analyzes and displays the signals using a predetermined algorithm.

A method for automatically recognizing and eliminating the eye moving interference from electroencephalogram (EEG) includes such steps as extracting the components from EEG by ICA, dipole locating analyzing, creating a bipole position distribution model about eye moving interference, and ICA reverse transformation to automatically eliminate said interference.

The invention discloses a resting state complex fMRI data ICA-CNN classification framework of patients and healthy people, and belongs to the field of biomedical signal processing. According to the method, an interested functional network separated from resting state complex fMRI data by ICA is taken as a research object, and classification of patients and healthy people is realized by utilizing 2D CNN learning characteristics with few parameters; data augmentation is carried out by utilizing ICA results obtained under multiple groups of model orders, and the problem of fMRI data shortage is solved. Compared with an existing 3D CNN network, the training amount is reduced, and the accuracy is improved. For example, aiming at a plurality of fMRI data collected in an 82 tested resting state,by applying the DMN component extracted by ICA, the slice identification accuracy is higher than that of 3DCNN by (0.728 vs 0.701), and the tested identification accuracy obtained by a tested decisionis further improved by (0.914 vs 0.701).

The invention relates to a pulse wave motion artifact removal method for parameter adaptive optimization VMD, and belongs to the technical field of biomedical signal acquisition and processing. The method comprises the following steps of by optimizing an objective function of variational mode decomposition, evaluating the variational mode decomposition result by adopting a correlation coefficientwith the fusion of the intrinsic mode correlation degree and the central frequency dispersion degree; carrying out parameter adaptive optimization variational mode decomposition on a pulse wave signalto obtain an intrinsic mode [uk] of the pulse wave, and designing a denoising rule according to the relative energy of frequency bands corresponding to different components of the signal to remove the motion artifacts of the pulse wave. Compared with the prior art, the method has the advantages that the quality of pulse wave variational mode decomposition is improved, the effect of removing the motion artifacts is improved, and the data complexity in the process of removing the motion artifacts is simplified.