106 results about "Biological signal processing" patented technology

The invention provides an electroencephalogram signal artifact removing method and device, equipment and a medium, and relates to the technical field of biological signal processing, collected electroencephalogram signal data is processed to obtain an optimal modal number and an optimal bandwidth parameter, and the optimal modal number and the optimal bandwidth parameter are used for conducting self-adaptive variational mode decomposition on the electroencephalogram signal data to obtain a plurality of modal components; then carrying out multi-dimensional feature analysis to obtain a plurality of feature indexes for judging an artifact suspicion mode and an effective mode component; performing short-time Fourier transform on the artifact suspicion mode, and constructing a time-frequency confidence map to guide weighted time-frequency independent component separation on the artifact suspicion mode to obtain an artifact component; suppressing the artifact component to obtain a suppressed independent component, and performing weighted reconstruction and multi-component fusion on the suppressed independent component and the effective modal component based on the time-frequency confidence map to obtain an artifact-removed electroencephalogram signal; and the fidelity, the robustness and the real-time performance of the electroencephalogram signal are improved.

The invention relates to the field of biological signal processing, and discloses an EEG signal fatigue detection method based on a time sequence enhanced hybrid architecture, which designs a time sequence perception extrusion-excitation module, adaptively performs attention weighting on a time dimension, effectively extracts key time sequence features, and improves the accuracy and reliability of fatigue detection. A fixed position code is replaced by an LSTM time embedded code, so that the attention of the model on important information in the time dimension is further enhanced; a mixed framework combining a space selection module and a Transform multi-head attention module is designed, and the long context sequence sensing ability and generalization ability are enhanced; a learnable activation function is introduced to replace a fixed linear weight, and the classification capability of the model is enhanced through nonlinear representation such as a spline function. According to the method, fatigue signs of EEG signals can be effectively captured, and the method has remarkable advantages in detection of different subjects and has important practical application value in reduction of traffic accidents caused by driver fatigue.

The invention relates to the field of biological signal processing, and particularly discloses a classification method for intrusive electroencephalogram signal motor imagery tasks, which comprises the following steps of: acquiring original electroencephalogram signals according to task requirements through an intrusive brain-computer interface system; preprocessing the electroencephalogram signal data, and removing noise in combination with a signal-to-noise ratio; performing independent component analysis to extract an electroencephalogram signal source; signal features are extracted manually, and principal component analysis dimension reduction is carried out; and putting the data into the model for training, and classifying the signals. According to the method, noise and redundant information of the electroencephalogram signals are effectively removed through combination of ICA and PCA, and the influence of artifacts on a classification result is reduced, so that the representative characteristics of the signals are improved, the classification accuracy is improved, and a good classification effect can still be ensured especially in a high-noise environment.

The present invention provides a technique capable of accurately and automatically setting a region of interest for acquiring biological information based on a biological information signal obtained from a subject placed in an examination space in a non-contact manner. A signal analyzing unit of a medical imaging apparatus uses the biological information signal for each of a plurality of regions included in a predetermined range among signals measured by a biological information measuring apparatus to select the region of interest in which movement of the subject is to be acquired among the plurality of regions. The movement of the subject is calculated using the biological information signal measured by the biological information measuring apparatus from the selected region of interest.

The invention discloses an intelligent drug delivery pain management bracelet, and relates to the fields of wearable equipment, transdermal drug delivery and biological signal processing. The bracelet is integrated with a multi-mode biosensor which comprises a temperature sensor, a pressure sensor, a heart rate sensor, a blood oxygen sensor and a skin electric response sensor, and physiological data related to pain can be collected in real time. Through an embedded microcontroller and a machine learning algorithm, a pain intensity evaluation model is constructed, and real-time evaluation of the pain state of a patient is realized. Based on the pain intensity and the physiological state of a patient, a drug release strategy is dynamically adjusted through an intelligent algorithm, and drugs are released painlessly and accurately through a micro-needle transdermal drug delivery technology, so that the patient obtains the optimal treatment effect. And the functions of data storage, historical analysis and remote doctor monitoring can be expanded. The hand ring is convenient to operate, comfortable to use and suitable for various pain management scenes such as chronic pain and postoperative pain, and the personalized level of pain treatment and patient compliance are remarkably improved.

The invention is suitable for the technical field of intelligent wearable devices and biological signal processing, and provides a heart rate monitoring method and system of an intelligent wearable device, and the method comprises the following steps: detecting the wearing state and the connection state of the device; waking up the sensor module to collect multi-modal data according to the wearing state and the connection state of the equipment; the multi-modal data comprises an ECG signal, a PPG signal and motion data; the multi-modal data is preprocessed, then the ECG signals and the PPG signals are corrected according to the motion data, motion interference is eliminated, and the corrected ECG signals and PPG signals are obtained; selecting a heart rate calculation mode according to the motion data and the corrected ECG signal and PPG signal, and performing real-time calculation to obtain heart rate data; abnormal recognition is carried out on the real-time heart rate data, and multi-level early warning response is carried out. According to the invention, the power consumption defect in the prior art can be solved, the interference of a motion scene is reduced, the health monitoring accuracy and reliability are improved, and the user experience and health management efficiency can be optimized.

The invention belongs to the technical field of biological signal processing and monitoring, and particularly relates to a multi-mode-based non-contact heart rate intelligent monitoring method which comprises the steps that a ballistocardiogram signal of a user is collected through a piezoelectric film sensor, meanwhile, a photoelectric volume pulse wave signal of the user is collected through a photoelectric sensor, and a multi-mode physiological signal is formed; performing space-time alignment processing on the multi-modal physiological signal, eliminating low-frequency breathing interference and motion noise by adopting an adaptive filtering algorithm, and outputting a pre-processed signal; inputting the preprocessed signal into a space-time coupling feature analysis engine, extracting time sequence features through a bidirectional long-short-term memory network, and extracting spatial features through a full convolutional network; adopting a multi-head self-attention mechanism to carry out dynamic weight fusion on the time sequence features and the spatial features to obtain fusion features; and calculating a real-time heart rate value based on the fused features, and judging a heart rate abnormal state. The problem that the heart rate monitoring difficulty is large is well solved.

The application belongs to the technical field of smart home, wearable device, biological signal processing and artificial intelligence control, and specifically discloses a self-adaptive audio adjustment sleep-aiding method based on sleep state recognition, which comprises the following steps: a non-invasive touch interaction mode is introduced to simplify the starting process of the sleep-aiding process; key physiological indexes such as the heart rate and body movement of a user are continuously monitored, and subtle changes in the data are analyzed to determine the physiological state of the user and the reaction of the user to environmental changes, so that the change in the acoustic environment is ensured to be always within the comfortable range that can be accepted by the user; the amplitude of the volume adjustment is controlled to be below the perception threshold of the user, and a special disturbance observation window is set to evaluate the physiological influence of each fine adjustment operation, so that inappropriate adjustment can be found and cancelled in time before substantial interference is caused. The application significantly improves the reliability of the user experience and sleep-aiding effect.

A storage case with a biomedical electrode pad for storing a biological signal processing circuit unit for processing an biological electrical signal which has been detected by the biomedical electrode pad, wherein a case body thereof has a storage area for the biological signal processing circuit unit, the storage area being provided in an openable and closable manner, and the biological signal processing circuit unit is detachable with respect to the storage area.

The invention discloses a sign language recognition method based on double-arm electromyographic signals, and belongs to the technical field of human-computer interaction and biological signal processing. The method comprises the steps that multi-channel electromyographic signals generated when sign language gestures are executed are synchronously collected through electromyographic arm rings worn on the left forearm and the right forearm of a user; performing preprocessing and feature extraction on the signal to obtain a time sequence feature sequence of left and right arms; the time sequence feature sequence is input into a pre-trained two-arm collaborative recognition model, and the model outputs a sign language gesture recognition result by fusing the spatial-temporal features of the left arm and the right arm; and finally, the recognized text information is converted into voice to be output. According to the method, the cooperation and time sequence relation of the double-arm electromyographic signals is creatively utilized, the problems that a traditional visual recognition method is greatly interfered by the environment, privacy is invaded, and double-hand linkage complex gestures cannot be effectively analyzed through single-arm electromyographic recognition are solved, and natural, accurate and real-time recognition and translation of the double-hand sign language gestures are achieved.

This invention belongs to the field of biological signal processing and discloses a system and method for recovering and extracting neuronal signals from four-dimensional spatiotemporal data. The system includes a biological data acquisition module, a signal processing module, and a signal extraction module. The biological data acquisition module is connected to the signal processing module, and the signal processing module is connected to the signal extraction module. The biological data acquisition module is used to acquire image sequences of biological samples; the signal processing module is used to process the acquired biological image sequences; and the signal extraction module extracts the neuronal signal intensity from the data. Users can use this system to quickly and efficiently acquire, identify, extract, and analyze biological four-dimensional spatiotemporal data, significantly shortening the biological signal processing cycle and improving its efficiency. This invention flexibly utilizes various machine learning algorithms and designs a complete and reliable deployment scheme, obtaining highly accurate sample data while reducing the cost of manual intervention.

The application provides a contralateral movement direction prediction method for heterolateral lower limb myoelectric signals and a related device, and belongs to the technical field of human biological signal processing and pattern recognition. The application uses the combined features of the surface myoelectric signals of the left and right lower limbs, the left foot plantar pressure and the right foot three-dimensional acceleration to construct a training set and a test set; a three-class gait prediction model is constructed, the three-class gait prediction model is trained based on the constructed training set, the three-class gait prediction model training result is tested based on the constructed test set, and a trained three-class gait prediction model is obtained; the combined features of the surface myoelectric signals of the left and right lower limbs, the left foot plantar pressure and the right foot three-dimensional acceleration are input into the trained three-class gait prediction model for right foot movement direction prediction, and a right foot movement direction prediction result is obtained. The application solves the problem of low accuracy of contralateral movement direction prediction for heterolateral lower limb myoelectric signals.

The invention belongs to the technical field of smart home, wearable equipment, biological signal processing and artificial intelligence control, and particularly discloses a sleep state recognition-based adaptive audio adjustment sleep-aiding method, which comprises the following steps of: simplifying the starting process of a sleep-aiding process by introducing a non-intrusive touch interaction mode; by continuously monitoring key physiological indexes such as heart rate and body movement of the user and analyzing subtle changes of the data, the physiological state of the user and the response to environmental changes are judged, and it is ensured that the changes of the acoustic environment are always in a comfortable area which can be accepted by the physiology of the user; the volume adjustment amplitude is controlled below the perception threshold value of the user, and the special disturbance observation window is set up to evaluate the physiological influence of each fine adjustment operation, so that improper adjustment can be found and revoked in time before substantive interference is caused. The user experience and the reliability of the sleep aiding effect are remarkably improved.

A method and device for extracting features of a biological signal and a device for detecting biological information are disclosed. A device configured to extract features from a biological signal is disclosed. The device includes: a biological signal acquirer configured to acquire a biological signal; a processor configured to analyze the concavity and convexity of a waveform of the biological signal in an interval of the biological signal, and extract features from the biological signal based on the analyzed concavity and the analyzed convexity.

This invention discloses a method for accurate heart rate calculation based on multi-source multimodal fusion, relating to the fields of biosignal processing and health monitoring technology. The method includes: simultaneously acquiring multi-source array data such as biosignals, environmental information, and motion status, and constructing a time-aligned dataset; preprocessing and extracting features from the data to obtain multi-dimensional features such as pulse peak interval, R-wave interval, environmental interference level, and exercise intensity level; identifying the current dynamic scene based on features and adaptively assigning heart rate calculation weights to photoelectric and electrocardiogram signals; calculating two preliminary heart rate values ​​in parallel and performing weighted fusion; combining individual feature calibration and a time-series trend model to perform anomaly judgment and correction on the preliminary heart rate values, and outputting the final heart rate value and confidence score. This method, through multi-source fusion and scene-adaptive weighting, achieves multi-scene adaptive, anti-interference, and personalized accurate heart rate monitoring.

The invention relates to the technical field of computer vision and biological signal processing, and discloses a multi-task learning-oriented facial heart rate and signal confidence joint prediction method and system. The method aims at solving the problems that in the prior art, only heart rate prediction precision is concerned, confidence coefficient evaluation is neglected, a multi-task cooperation mechanism is missing, loss function design is unbalanced, and an output result is unreliable in a dynamic environment. The method comprises the following steps: collecting and preprocessing face video data, extracting time sequence features, synchronously executing heart rate regression prediction and signal confidence estimation through a shared feature encoder and an independent decoder, balancing each task weight by adopting a self-adaptive weighted multi-task loss function, and post-processing a prediction result. The system comprises a face video data acquisition module, a time sequence feature extraction module, a multi-task joint learning module, a task collaborative optimization module and a post-processing and output module. According to the technical scheme, synchronous output of heart rate prediction and signal confidence estimation can be achieved, and the reliability and practicability of a detection result are improved.

This invention relates to the field of non-contact biosignal processing technology, providing a method, device, electronic device, and storage medium for determining psychological state assessment parameters. The method includes: inputting millimeter-wave radar signals into a psychological state assessment model to obtain psychological state assessment parameters; the psychological state assessment model includes a physiological feature extraction module, a micromotor feature extraction module, a multimodal feature fusion module, and a psychological state inference module. This invention reconstructs electrocardiogram waveforms through the physiological feature extraction module to obtain detailed physiological features, extracts micromotor features through the micromotor feature extraction module, and integrates features reflecting internal physiological changes and external behavioral manifestations through the multimodal feature fusion module. This forms a more comprehensive and multidimensional fused feature set for comprehensive analysis by the psychological state inference module, greatly improving the comprehensiveness and accuracy of psychological state assessment in unconstrained daily environments.

This invention provides the principle and method for influencing cellular functional excitatory expression through misaligned distribution of acoustic wave frequencies, relating to the interdisciplinary field of biological signal processing. The principle and method for influencing cellular functional excitatory expression through misaligned distribution of acoustic wave frequencies includes the following steps: Step 1: Target cell pretreatment, selecting cells to be regulated, separating, purifying, and culturing to the logarithmic growth phase, adjusting the cell density to... 1×104‑1×106cells / cm2 The cells are placed in a biocompatible culture medium. The method of this invention significantly improves the specificity of regulation. Through a frequency-function specific matching mechanism, frequency groups targeting specific cellular functions are screened. Combined with a multidimensional orbital misalignment design to avoid frequency interference, each frequency precisely activates the corresponding signaling pathway. The single-function excitation expression level of cells is more than twice that of the traditional single-frequency method, and the non-specific functional excitation rate is reduced by more than 80%, while the efficiency of action is greatly improved.

The invention discloses a non-contact blood pressure measurement method and system based on transfer learning, and belongs to the technical field of biological signal processing. In order to solve the problems of scarcity of labeled samples and difficulty in model generalization in non-contact blood pressure measurement, knowledge migration from source domain wearable equipment signals to target domain radar signals is realized by constructing a multi-modal feature encoder and a cross-modal attention module and combining a migration learning strategy of source domain pre-training and target domain staged fine tuning. According to the method, systolic pressure and diastolic pressure can be accurately predicted on a small-scale target domain data set, personalized calibration requirements are eliminated, and the precision and applicability of non-contact blood pressure measurement are improved.

The application relates to the technical field of data processing, and discloses a multi-modal neural biological signal processing method and device, a server and a storage medium. The method comprises the following steps: acquiring a multi-modal neural biological signal to be processed, and preprocessing the multi-modal neural biological signal; inputting the preprocessed multi-modal neural biological signal into a deep learning model, extracting deep features of each kind of modal neural biological signal in the multi-modal neural biological signal based on the deep learning model; inputting the deep features into a feature fusion layer for feature fusion to obtain target fusion features; inputting the target fusion features into a regression layer through a full connection layer, performing vital sign prediction on the target fusion features by using the regression layer, and generating a biological vital sign prediction result. Through implementation of the technical solution, the deep features can be effectively captured, the features do not need to be artificially designed or selected, the effective prediction of the biological representation of the neural signal is realized, and the prediction accuracy of the biological representation is improved.

The present application relates to the field of biological signal processing, in particular to a frequency estimation method based on rootMUSIC and signal dimension conversion, a storage medium and equipment. The method comprises the following steps: chest displacement signals extracted from radar echoes are modeled as the sum of sine waves with integer multiple frequencies of basic respiration and heart rate, and corresponding single-channel analytic signals are generated through Hilbert transform; the single-channel analytic signals are converted into multi-channel data through a signal rearrangement method, the chest wall signal model is equivalent to a uniform linear array multi-shot receiving signal model, so that the vital sign frequency and the signal source direction of arrival establish a one-to-one correspondence; the received data of the multi-shot receiving signal model is processed through an alternating direction multiplier method framework, and a denoised signal matrix is solved; the rootMUSIC algorithm is used for the denoised signal matrix to solve the frequency estimation. The present application converts the signal model, makes the frequency estimation range equivalent to the angle estimation range, and improves the frequency estimation accuracy.

The application discloses a body state monitoring method and a wristband type emergency monitoring ring, relates to the technical field of biological signal processing, and the body state monitoring method comprises the following steps: emitting red light of a preset first wavelength to the wrist part of a human body, collecting reflected light signals, obtaining a waveform physiological signal, determining a monitoring index of the human body, inputting the monitoring index into a warning model, obtaining a stress state probability of the human body, judging whether a warning is triggered or not based on the stress state probability and a preset probability threshold, and if the warning is triggered, issuing an alarm and sending corresponding body state data to a preset medical platform. The wristband type emergency monitoring ring can be worn on the wrist of a player to realize convenient detection, and through real-time monitoring of physiological data of the human body and a warning model, stress state detection and prediction are realized, so that the body state of the player can be monitored conveniently, in real time and accurately, and the effect of body monitoring of the player is improved.

The present invention belongs to the field of biological signal processing technology. The present invention discloses a method and device for generating magnetoencephalography (MEG) evoked signals. The method comprises: determining the alignment cost of all signals in the MEG signal based on the minimum cost path between any two trial signals in the MEG signal, wherein the minimum cost path is used to represent the time-minimized alignment cost between the corresponding two trial signals; determining the temporal similarity weight coefficient of the signal for each trial based on the alignment cost; and generating the MEG evoked signal based on the signal for each trial and the corresponding temporal similarity weight coefficient. The method provided by the present invention effectively solves problems in the prior art, such as the inability to effectively extract signals of interest from MEG signals and the inability to effectively reduce the impact of signal jitter.

The invention discloses a neurovascular coupling dynamic modeling method based on CEEMDAN and GLM fusion, and relates to the technical field of biological signal processing. Adaptive non-parametric frequency band segmentation is carried out on EEG signals by introducing a white noise enhanced ensemble empirical mode decomposition strategy; the individual specific neural oscillation mode (such as beta-gamma mixed rhythm after stroke or abnormal enhancement of gamma frequency band) can be captured without presetting a frequency band boundary. Meanwhile, the intrinsic mode function time-varying frequency spectrum features generated by the CEEMDAN are embedded into the hemodynamic response function reconstruction process of the GLM model, individualized neural oscillation features are dynamically extracted through an energy entropy weighting mechanism, and the space-time heterogeneity of neural blood flow dynamic coupling in the pathological state is quantitatively analyzed. According to the method, the physiological limitation of traditional fixed frequency band division is overcome, the analysis precision of non-stationary signals in a pathological noise environment is remarkably improved through an anti-modal aliasing adaptive decomposition technology, and a dynamic modeling framework is provided for research of a neurovascular coupling mechanism.

The invention relates to a biological signal processing technology, in particular to a noninvasive continuous blood pressure estimation system based on a subject adaptive feature modulation mechanism, which is characterized in that an individual prior embedding vector is generated through an individual prior embedding vector generation module; the subject adaptive feature extraction module performs real-time multi-level dynamic modulation on the preprocessed time sequence physiological signal according to an individual prior embedded vector, and outputs a high-dimensional time sequence feature map; the sequence processing module carries out time sequence dependence modeling and context refining on the high-dimensional time sequence characteristic pattern, and outputs time sequence characteristics; the cross-modal attention fusion module splices the individual prior embedded vectors and the time sequence features; the spliced feature vectors are input into a multi-head attention fusion module, dynamic cross-modal feature weighted fusion is achieved, and high-dimensional feature vectors are obtained; and the blood pressure regression output module is used for mapping the high-dimensional feature vector through a full connection layer and carrying out regression to obtain estimated values of systolic pressure and diastolic pressure. The problem that an existing static mapping normal form cannot deal with individual heterogeneity is solved.

The application relates to the technical field of biological signal processing, and discloses a multi-modal physiological signal coupling analysis method, a system, a terminal and a storage medium. The method comprises the following steps: synchronously collecting multi-modal physiological signals of a subject when the subject performs a specific experimental paradigm; constructing a multi-modal dynamic causal model comprising a neuron dynamics model and an observation model corresponding to each signal; adopting a staged Bayesian inversion strategy to perform parameter estimation, fixing a first type of signal parameter to invert a second type of signal related parameter, fixing the inverted parameter to invert the first type of signal parameter, and obtaining a joint posterior distribution; and finally generating a biomarker of the subject based on the model parameters obtained through inversion. Through the staged inversion strategy, the technical problems of high computational complexity and unstable parameter estimation caused by the large time scale difference of multi-modal data and the large number of model parameters are effectively solved, and the biomarker can be generated and used for clinical motor function evaluation.

This apparatus for continuously measuring biological information continuously obtains an optimized systolic blood pressure estimation value, and can monitor a changing state of blood pressure in hemodialysis treatment or the like. The apparatus comprises: an optical blood flow measuring means for detecting a blood flow pulse wave from a living body; an electrocardiographic signal detecting means for detecting an electrocardiographic signal from the living body; an actual biosignal measuring means for detecting an actually measured biosignal value by actually measuring a target biosignal; a biosignal calculating means for calculating the target biosignal on the basis of a characteristic signal obtained from the optical blood flow measuring means and the electrocardiographic signal detecting means; and a biosignal processing means for outputting the actually measured signal obtained by the actual biosignal measuring means, the calculated signal obtained by the biosignal calculating means, and a biosignal optimized through mathematical optimization using the actually measured biosignal.

The invention relates to the technical field of biological signal processing and intelligent health equipment, in particular to a dream monitoring and visualization method and system based on multi-modal physiological signal fusion. The intelligent monitoring terminal is integrated in the portable sleep eyeshade and comprises a flexible dry electrode array (used for EEG acquisition), an integrated photoelectric sensor (used for PPG signal acquisition), a miniature millimeter wave radar chip (used for non-contact EOG acquisition), a signal processing module, a wireless transmission module, a semiconductor temperature control module and a double-beat audio module; and the user terminal APP is used for receiving, storing and displaying sleep data (a sleep staging chart and REM period information), a dream intensity index (DII) and a dream cloud chart, and generating a sleep health report and trend analysis. The method has the advantages that high-precision recognition is achieved, the problems that a single sensor is prone to interference and high in misjudgment rate are solved through EEG, EOG and PPG three-mode signal fusion judgment, and the REM period recognition accuracy is improved by 40% or above compared with single EEG monitoring.

This invention discloses a method for identifying the physiological state of adult product users based on multi-sensor data acquisition, relating to the fields of intelligent health monitoring and biosignal processing technology. This invention uses non-negative matrix factorization (NMF) technology to decompose multi-channel non-steady-state electromyographic signals collected from the pelvic floor and core muscle groups into multiple muscle coercive elements and their temporal activation coefficients. Each coercive element represents a fixed pattern of muscle cooperating under neural drive, while the activation coefficient reflects the change of this pattern over time. Sexual arousal, as a specific neurophysiological process, induces the activation of coercive elements with specific spatiotemporal patterns. By tracking the activation and evolution of specific coercive patterns, the neural control fingerprint reflecting sexual arousal is extracted from the original signal contaminated by motion noise. This effectively distinguishes between ordinary muscle contractions caused by device use and physical activity and autonomous neuromuscular activities related to sexual responses, overcoming the defects of susceptibility to interference and insufficient specificity.

[Problem] To acquire vital information having enhanced accuracy from reflected waves obtained by transmitting radio waves to a living body. [Solution] As shown in Fig. 1, a biological signal processing device according to the present embodiment uses one radar module employing array antennas (23, 25), and by subjecting a reception signal to beam forming or using null beams, the ratio of complex signals of two reflected waves, one from a region R1 exhibiting large motion due to inclusion of vitals and the other from a region R2 exhibiting small motion due to absence of vitals, is used to extract a vital signal with body motion components removed. In the present embodiment, as a method for acquiring the reflected wave by designating a specific site, such as the region R2 (shoulder) having small motion due to absence of vitals, the reflected wave is multiplied by weight information w1, w2 obtained in advance by beamforming.