141 results about "Biological signal processing" patented technology

The invention relates to the field of signal processing, and discloses a biological signal processing method and device, computer equipment and a storage medium, and the method comprises the steps: collecting a biological signal, and carrying out the preliminary filtering of the biological signal, and obtaining a preliminary filtering signal; performing noise characteristic analysis on the preliminary filtering signal, selecting a proper filtering mode according to an analysis result, and designing filter parameters through fuzzy logic processing; inputting the filter parameters into an adaptive filter to process the signal so as to obtain a filtered biological signal; in combination with a de-noising convolutional neural network model, noise identification and fuzzy logic processing are performed on the filtered biological signals, and filter parameters are dynamically adjusted; and performing multi-round iterative optimization until a target filtering effect is obtained, and outputting an optimized biological signal. According to the biological signal processing method, complex noise in the biological signal is effectively removed, the filtering precision is improved, meanwhile, the signal integrity is kept, and a reliable basis is provided for subsequent biological signal analysis and processing.

The invention provides a biological health system, a physiological state regulation and control method, wearable equipment and a storage medium, and the biological health system comprises a detection assembly which is configured to collect a biological signal of a user in real time; a processing component configured to calculate a physiological state regulation parameter based on the phase of the biological signal, and generate a stimulation signal based on the physiological state regulation parameter; and the output component is configured to output the stimulation signal, so that the coupling effect of the biological signal and the stimulation signal regulates and controls the physiological state of the user. The biological signal of the user is acquired in real time, and the personalized physiological state regulation and control parameter of the user is calculated based on the phase of the biological signal to generate the stimulation signal in real time, so that the physiological state of the user is regulated and controlled through the coupling effect of the stimulation signal and the biological signal.

The invention discloses a motor imagery electroencephalogram decoding method based on a multi-attention bridge structure network, and relates to the field of biological signal processing and motor imagery brain-computer interfaces. The method comprises three modules: 1) a depth separable convolution attention module; 2) bridge structure modules; and 3) a multi-scale parallel space-time convolution module. In order to solve the problems of poor robustness and weak generalization ability in a motor imagery electroencephalogram signal decoding process, a deep separable convolution attention module is utilized to focus important channel space topological characteristics of electroencephalogram signals, and a bridge structure module is utilized to effectively capture local and global time domain characteristics; and multi-dimensional attention on spatial-temporal characteristics of the electroencephalogram signals is realized. And finally, spatial-temporal features of different scales are extracted through a multi-scale parallel spatial-temporal convolution module, so that the decoding recognition rate, robustness and generalization ability of the network are improved, and an effective solution is provided for improving the stability and applicability of a motor imagery brain-computer interface.

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 provides a campus abnormal behavior detection method and device and electronic equipment, and belongs to the technical field of artificial intelligence and biological signal processing, and the method comprises the steps: obtaining the facial expression features, body motion features, voice emotion features and physiological signal data of a target person in a monitoring region in a campus scene; fusing the facial expression features, the voice emotion features, the body action features and the physiological signal data to obtain fused data; performing emotion recognition and behavior prediction on the fused data based on a preset time sequence model to obtain an analysis result; and predicting whether an abnormal behavior occurs based on the analysis result, and performing early warning when the prediction result is that the abnormal behavior occurs. The technical problem that potential safety hazards still exist in actual detection results due to the fact that application of emotion and physiological signals in abnormal behavior detection is not fully considered in the prior art is effectively solved.

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 invention relates to the field of biological signal processing and emotion calculation, and discloses a multi-mode mental stress assessment method and system based on machine learning, and the method comprises the steps: collecting a brain wave signal and a heart rate variability signal of a user; using a CNN-LSTM model to perform multi-modal depth feature extraction on the spectrogram of the brain wave signal and the time sequence of the heart rate variability signal to obtain a brain wave signal depth feature vector and a heart rate variability signal depth feature vector; splicing the brain wave signal depth feature vector and the heart rate variability signal depth feature vector to form a high-dimensional multi-modal feature vector representation; and classifying the multi-modal feature representation by using a support vector machine, and outputting a pressure state judgment result. The real-time pressure state of the user can be accurately predicted, and the technical problem that an existing pressure detection method is not high in detection precision is solved.

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.

The invention provides a cross-frequency-band neuromuscular coupling analysis method based on VMD and MSCNN, and relates to the technical field of biological signal processing, the method comprises the following steps: firstly, carrying out frequency band decomposition on EEG and sEMG signals through VMD, and extracting time-frequency characteristics of each frequency band by using MSCNN; and then, performing coupling analysis on nerve-muscle signals of different frequency bands by using nucleation generalized partial orientation coherence (gPDC), and deeply exploring a complex interaction mechanism across the frequency bands. The method not only can capture the causal relationship and the coupling strength in the same frequency band, but also can reveal the cross-frequency-band nonlinear coupling relationship, and overcomes the limitation of a traditional signal processing method in the process of analyzing a cross-frequency-band interaction mechanism and the defects faced by same-frequency-band coupling analysis. In addition, the method makes full use of the advantages of a deep learning model in extraction of a complex coupling relationship, thereby providing a more comprehensive and accurate analysis view angle for the research of neural rehabilitation evaluation and a neural regulation mechanism.

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.

To provide a new technology for highly reliably determining the existence of a P wave from time series data on ECG signals using signal processing.SOLUTION: A biological signal processing device separates a plurality of heart rate waveforms extracted from time series data on ECG signals into a plurality of to a plurality of heart rate waveform groups that at least include a first heart rate waveform group and a second heart rate waveform group; from each of the first heart rate waveform group and the second heart rate waveform group, acquires a first representative P wave section waveform and a second representative P wave section waveform, which are waveforms of representative P wave sections; and determines that a P wave is included in the time series data on the ECG signals when similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold.SELECTED DRAWING: Figure 12

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 invention relates to the technical field of biological signal processing, and discloses a signal identification method and device, a storage medium and wearable equipment, and the method comprises the steps: obtaining a to-be-identified biological signal, carrying out the coding of the to-be-identified biological signal, and obtaining a to-be-identified target super vector; according to the to-be-identified target super-vector, selecting a target class super-vector from various types of super-vectors, wherein the class super-vector is obtained by encoding a normal sample biological signal corresponding to the normal type; and taking the normal type corresponding to the target class super-vector as an initial recognition result, and adjusting the initial recognition result according to the first similarity between the target class super-vector and the to-be-recognized target super-vector to obtain a final recognition result. Compared with the prior art, the method has the advantages that the determined normal type can be adjusted through the first similarity between the target type super-vector and the to-be-recognized target super-vector, the final recognition result is obtained, recognition is completed, and therefore signal recognition can still be completed under the condition that only a normal sample exists.

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.