228 results about "Biomedical signal" patented technology

The invention discloses a PPG-based blood pressure detection method, and particularly relates to the technical field of biomedical signal detection, and the method specifically comprises the following steps: 1, collecting a PPG original waveform through a PPG sensor; step 2, preprocessing the collected PPG signals; step 3, carrying out feature extraction on the preprocessed PPG signal; 4, the extracted PPG features serve as input, reference blood pressure parameters serve as output, an LSTM network model is constructed, the network weight is trained, and the mapping relation between the features and the blood pressure is established; step 5, obtaining a personalized model; and 6, collecting a large-scale multi-source heterogeneous data sample, and optimizing the model by adopting a transfer learning technology and an incremental learning technology. By processing and modeling PPG signals, accurate detection of blood pressure parameters is achieved, non-invasive continuous blood pressure monitoring can be achieved, and the long-term monitoring requirements of old people and patients with cardiovascular diseases are met.

The invention relates to the technical field of biomedical signal processing and intelligent health monitoring, and particularly discloses an intelligent heart rate monitoring method and system based on heart sound and electrocardiosignals, heart sound signals and electrocardiosignals of a user are collected in real time, quality evaluation is conducted on the collected signals through signal quality indexes, and accurate signals are extracted; extracting time domain and frequency domain features of the first heart sound and the second heart sound of the heart sound signal, fusing multiple features by adopting wavelet transform and a main frequency analysis algorithm, and calculating a heart sound abnormal coefficient to evaluate the abnormal degree of the heart sound signal; the vibration amplitude and frequency characteristics of P waves in the electrocardiosignals are extracted, and an electrocardiograph abnormal coefficient is calculated through a vibration amplitude abnormal coefficient and a vibration frequency fluctuation coefficient and used for evaluating the stability of the electrocardiosignals; based on the heart sound abnormal coefficient and the electrocardiogram abnormal coefficient, a decision tree model is constructed, a heart rate abnormal index is dynamically calculated, dynamic monitoring of the heart rate is achieved, a dynamic early warning mechanism is conducted, and heart rate abnormal information is output.

The invention discloses a pressure sensitivity classification evaluation method based on heart rate variability and wearable equipment, and relates to the technical field of biomedical signal processing. According to the method, the collected signals can be processed to form the RR interval sequence and the resultant acceleration, the motion state is judged, the motion state mark is set, the RR interval sequence and the motion state mark are aligned according to the timestamp, the structured data are generated, the pressure sensitivity level of an individual is judged based on the structured data, and the pressure sensitivity level of the individual is calculated. The subjective influence of pressure sensitivity evaluation can be avoided, and the classification precision and adaptability are improved.

The invention discloses a remote photoplethysmography (rPPG) signal processing method and system based on a long and short term space-time convolution network, and belongs to the crossing field of biomedical signal processing and computer vision. In order to solve the problem of signal distortion caused by illumination fluctuation, motion artifacts and skin color differences, the method constructs a multi-scale space-time modeling framework: extracting local space-time features of transient changes of facial capillaries by adopting a 3D convolutional network, capturing long-range periodic features of heart rate rhythm in combination with a 1D expansion convolutional network, and establishing a multi-scale space-time modeling framework; the spatial-temporal characteristics are dynamically fused through the self-attention weight and the gating residual structure, and the anti-interference capability is improved. In the preprocessing stage, a face area is positioned through MTCNN, motion artifacts are compensated by using an optical flow equation, and signal purity is enhanced by combining a skin color mask and a YUV color space. The system adopts a deep separable convolution and parallel acceleration strategy to realize light weight, and optimizes the network through time domain MSE loss and frequency domain KL divergence. Experiments show that the phase error of the method is reduced by 40% in a dynamic scene, the signal amplitude of a deep skin color group is improved by 60%, the method is suitable for non-contact health monitoring equipment, and the robustness and the measurement precision of the rPPG technology in a complex environment are remarkably improved.

The invention relates to the technical field of biomedical signal processing, and discloses a multi-wavelength PPG signal motion artifact elimination algorithm and system. The method comprises the following steps: acquiring a multi-wavelength PPG signal by using acquisition equipment; inputting the acquired noise-containing multi-wavelength PPG signal into a filter; the filter processes an input signal and then generates a denoised PPG signal; extracting a period of the denoised signal in real time by using an autocorrelation function; inputting the denoised PPG signal and a period extracted in real time into a BSS algorithm to find an optimal noise reference signal; taking the denoised PPG signal and the optimal noise signal as a first input and a second input of adaptive filtering to eliminate motion artifacts; the motion artifacts are eliminated by using the algorithm, and finally the PPG signals with the motion artifacts eliminated are output; the method overcomes the problems of dependence on additional hardware, low correlation of noise reference signals, complex algorithm and the like in the prior art.

The invention discloses an electrocardiosignal processing method and system, and relates to the field of biomedical signal processing.The method comprises the steps that electrocardiosignals are obtained, heart beat cutting is conducted on the electrocardiosignals, multiple heart beats are obtained, and position information of QRS waves, T waves and ST wave bands of each heart beat is obtained through a Pan-Tompkins algorithm; extracting dynamic characteristics of each heart beat based on the position information; and evaluating the importance of the dynamic features by using a random forest algorithm, screening key features, and carrying out series fusion to obtain comprehensive feature information. According to the method, through multi-dimensional feature extraction and fusion, subtle changes in the complex electrocardiosignals are captured more comprehensively, and therefore the accuracy and efficiency of electrocardiosignal analysis are improved.

The invention discloses a sleep classification method and system based on time-frequency combination, and belongs to the technical field of biomedical signal processing and artificial intelligence. In order to solve the problems that an existing method is high in manual dependence, insufficient in time-frequency feature fusion and low in long-time-sequence modeling efficiency, sleep stage classification is carried out mainly through automatic time-frequency feature extraction, a dynamic attention mechanism with memory enhancement and a lightweight multi-branch neural network structure. According to the method, efficient modeling and accurate classification of the multi-scale electroencephalogram signals can be achieved, the deep sleep recognition capability and the overall classification accuracy are improved, meanwhile, the model parameter quantity and the reasoning delay are remarkably reduced, and good real-time performance and clinical applicability are achieved.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

An apparatus for generating diagnostic hypotheses based on electrocardiogram (ECG) data, comprising a processor and a memory containing instructions configuring the processor to generate, using a generative model trained on a corpus, a set of diagnostic hypotheses, wherein generating the set of diagnostic hypotheses includes creating labels, each represents a diagnostic feature associated with diagnostic hypotheses, receive a biomedical signal, identify a biomedical feature as a function of the biomedical signal, select a diagnostic hypothesis from the set of diagnostic hypotheses by matching the biomedical feature against the diagnostic feature, query, as a function of at least a matched label, a medical repository to validate the diagnostic hypothesis, wherein the medical repository includes patients' electronic health records (EHRs), and output the diagnostic hypothesis upon a positive validation of the diagnostic hypothesis.

The invention relates to the technical field of biomedical signal processing, and particularly discloses a fetal growth and development detection method and system based on intellectualization, and the method comprises the steps: obtaining a mixed physiological signal and a fetal movement signal, obtaining a fetal movement interference coefficient and an interference peak period through cross-correlation analysis, constructing an artifact monitoring vector, and predicting a signal deterioration probability; artifact separation is carried out; distinguishing physiological and non-physiological types of the artifact signal, and extracting a signal degradation event; and if the degradation event exists, analyzing a conduction relationship between the event and equipment sampling parameters through a rule engine, generating a main conduction path, constructing an artifact strategy mapping model, and outputting a parameter correction rule. The system comprises a fetal movement analysis module, an artifact separation module, a degradation analysis module and an equipment correction module. According to the method, the multi-dimensional feature analysis and the intelligent model are combined, so that the fetal physiological signal purity and the anomaly detection accuracy can be improved.

The invention discloses a motion posture recognition detection method based on transfer learning, belongs to the field of biomedical signal processing and machine learning, and aims at improving the robustness of a model under the condition of electrode position change by constructing an adversarial training mechanism and aligning data distribution before and after electrode offset in a feature space. The problem of data distortion caused by electrode displacement in the electrical impedance tomography EIT technology is solved, and the accuracy of posture recognition is improved.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention belongs to the technical field of biomedical signal processing, and particularly relates to a single-channel pulse signal enhancement method fusing angular domain features and time sequence information, which comprises the following steps: S1, collecting wrist pulse signals by using a single-finger pulse diagnosis bionic hand provided with a pressure sensor; s2, an improved Teager energy operator and a self-adaptive weighted fusion strategy are used for conducting denoising on the pulse signals; s3, adopting an improved Euclidean distance weighted Gramer angle field method to carry out Gramer field conversion on the pulse signals; and S4, using an improved attention score weighting and local standard deviation adjusting method to carry out fusion enhancement on the GASF graph and the GADF graph. According to the method, the denoising performance of the pulse signals is effectively improved through the improved Teager energy operator and the self-adaptive weighted fusion strategy, the time domain dynamic characteristics and structural direction information of the fused image are reserved through the improved Euclidean distance weighted Gramb angle field and the improved GASF and GADF fusion method, and the denoising performance of the pulse signals can be effectively improved through the improved Euclidean distance weighted Gramb angle field and the improved GASF and GADF fusion method. Therefore, a structured enhanced representation mode with angular domain and time sequence information is constructed.

The invention relates to the technical field of audio signal processing and biomedical signal analysis, and still has a further optimized space for the recognition of anti-noise requirements, signal individual differences and complex pathological modes in a noise environment. The invention provides a double-path CNN heart sound classification method based on time-frequency and double-spectrum fusion features, and the method comprises the steps: carrying out the preprocessing of an original heart sound signal of a data set which is classified into a normal heart sound and an abnormal heart sound, and obtaining a to-be-recognized heart sound signal; based on dynamic continuous wavelet transform, adaptively selecting parameters to extract time-frequency characteristics, introducing bispectrum analysis, capturing nonlinear characteristics, generating a dual-channel characteristic pattern, and efficiently storing the dual-channel characteristic pattern in an HDF5 format; and based on a designed double-path convolutional neural network structure, respectively processing the extracted time-frequency and double-spectrum features, performing classification after fusion, and training a model in combination with category weighted loss and an optimization strategy to obtain a heart sound classification result. The heart sound recognition accuracy can be improved.

The invention relates to a three-dimensional electrocardio scatter diagram analysis method and device based on heart rate variability and a medium, and relates to the technical field of biomedical signal processing. According to the method, the heart rate data of the user can be mapped into the three-dimensional points in the three-dimensional coordinate system after being preprocessed, the coordinate axes of the three-dimensional coordinate system are the time domain index, the frequency domain index and the nonlinear index, the heart rate change is displayed from three dimensions, compared with a traditional two-dimensional scatter diagram, the heart rate change characteristics can be more comprehensively and stereoscopically displayed, and the user experience is improved. According to the method, more potential heart disease related information is captured, heart rate data features are deeply excavated by constructing a three-dimensional electrocardiogram scatter diagram containing more heart rate change information, heart rate scatter indexes are quantified, and risk levels of heart diseases such as arrhythmia and atrial fibrillation are scientifically and accurately evaluated according to the scatter indexes. And the accuracy and reliability of heart disease risk assessment are improved.

The invention provides an electroencephalogram abnormal signal detection method based on step-by-step identification and multi-agent decision, and relates to the technical field of biomedical signal processing.The method comprises the steps that electroencephalogram signal data of an epilepsy patient are collected, and the data are analyzed according to a preset channel sequence and cut into a plurality of signal segments with the same length; extracting a multi-dimensional feature from each signal segment; inputting the time domain feature, the frequency domain feature and the inter-channel synchronization feature of each signal segment into an isolated forest model, and screening based on an abnormal proportion threshold to obtain at least one potential abnormal segment; based on the depth scattering feature, the wavelet transform feature, the time domain feature and the frequency domain feature of each potential abnormal segment, executing a multi-agent integration decision on each potential abnormal segment to obtain a comprehensive abnormal score corresponding to each potential abnormal segment; and determining an abnormal signal in the electroencephalogram signal data based on the comprehensive abnormal score corresponding to each potential abnormal segment. According to the method, the false alarm rate of electroencephalogram abnormal signal detection is remarkably reduced.

The invention discloses a brain feature extraction method, a brain feature extraction system, brain feature extraction equipment and a medium for an MRI (Magnetic Resonance Imaging) image, and relates to the technical field of intelligent biomedical signal processing. Deep features of a plurality of MRI sequence images are fully excavated, and the extracted imaging features related to brain lymphatic system functions and brain structure morphologies include diffusion degree values when bilateral brain tissues in the brain of a patient are converted into fiber bundles and fiber bundle reference values causing fiber bundle deviation when the brain of the patient discharges. The reference value of the fiber bundle can reflect which parameters are diffusion parameters when bilateral brain tissues are abnormal, and the diffusion degree value and the diffusion parameters reflected by the imaging characteristics are closely related to the epilepsy language barrier function of the patient and can be used as standards for predicting the language barrier of the epilepsy patient.

The invention provides a fetal hypoxia early risk identification method and system based on a large pulse pressure phenotype, and relates to the technical field of biomedical signal processing and heart sound signal analyse.The method comprises the steps that fetal heart sound signals and body surface vibration signals at the abdominal wall of a pregnant woman are collected through a multi-channel sensing array; a preprocessing means is adopted to improve the signal-to-noise ratio of a target signal and suppress an interference signal; in the sliding time window, a large pulse pressure phenotypic feature set and heart sound time phase parameters are extracted; based on the combined criterion, fetal hypoxia risk judgment is carried out; and outputting the graded early warning information, and supporting the playback of the risk event fragment. According to the method, fetal real heart sound mechanical vibration serves as an information source, hypoxia early warning is achieved based on a large pulse pressure phenotype, individualized base lines (including day and night layering) and gestational week self-adaptive correction are supported, the anti-interference capacity is high, the 7 * 24-hour continuous monitoring requirement in a hospital / family can be met, and the sensitivity and specificity of hypoxia recognition are improved.

The invention relates to the technical field of biomedical signal processing, in particular to an electrocardiogram reconstruction method and system based on machine learning, and the method comprises the following steps: carrying out iterative initialization; respectively carrying out data preprocessing on the PPG signal and the ECG signal; based on the convolutional neural network and a Transform encoder, constructing a signal conversion model; inputting the preprocessed data into a signal conversion model, carrying out model training, and outputting a predicted electrocardiogram signal; calculating a loss function between the predicted electrocardiogram signal and the target real signal, and optimizing the model parameters based on the loss function; when the number of iterations is smaller than a threshold value, continuously updating model parameters according to a gradient descent method and circularly training, and evaluating model performance and calculating performance indexes in a verification stage; after iteration is completed, the trained network parameters are stored, and reconstructed electrocardiogram signals are output. The electrocardiogram reconstruction method provided by the invention aims to solve the technical problem of accurately reconstructing the electrocardiogram through the photoplethysmogram.

The invention discloses an emotional state recognition method, system and device, and relates to the technical field of biomedical signal processing and artificial intelligence emotional computation.The method comprises the steps that electroencephalogram signals of a to-be-detected individual are collected; extracting power spectral density (PSD) and differential entropy (DE) features of the electroencephalogram signals on each frequency band according to a preset frequency; performing structured combination on the PSD and DE features corresponding to each frequency band, and constructing a dual-channel spectrum feature matrix; inputting the dual-channel spectrum feature matrix into a pre-trained shared encoder, extracting emotional feature representation of the individual, and obtaining an emotional state recognition result according to the emotional feature representation; according to the method, the depression emotion state of a new individual can be evaluated without participation of target domain data, the robustness and recognition precision of the model under cross-individual and low-labeling conditions are effectively improved, and the method has relatively high popularization and application potential.

The invention provides a multi-frequency biological resistance antibody component prediction method and device and a storage medium, and relates to the technical field of biomedical signal processing. According to the method, multi-frequency current testing is carried out on different physiological segments of a subject, an initial training data set is constructed in combination with basic information of the subject, the fine capturing ability of the multi-frequency bioelectrical impedance technology on human body physiological information is brought into full play, and individual features are fused to improve data dimensions and relevance; the quality of training data is guaranteed through data preprocessing, and a reliable foundation is laid for model training; and a component prediction model is constructed and optimized by a multi-task regression loss function, so that volume component prediction indexes can be efficiently output, and the prediction accuracy and stability of different indexes can be improved. According to the method, the problems of single data dimension and insufficient model precision in traditional volume component prediction are effectively solved, and a systematic and efficient scheme is provided for precise volume component prediction.

The invention relates to the technical field of biomedical signal processing, in particular to a BCG signal data processing method based on a TimeGAN model, and the method comprises the steps: obtaining an original BCG signal data set X, and carrying out the preprocessing of the original BCG signal data set X, and obtaining time-sequence formatted BCG data; the preprocessing comprises denoising, interpolation completion, normalization and time alignment; the time sequence formatted BCG data are input into the trained TimeGAN model, and an enhanced BCG signal Xfault is generated; and carrying out noise suppression processing on the enhanced BCG signal Xfault to obtain a de-noised enhanced BCG signal Xdeniosed, and carrying out noise suppression processing on the enhanced BCG signal Xfault to obtain a de-noised enhanced BCG signal Xdeniosed. According to the invention, the TimeGAN model is utilized to generate the enhanced BCG signal, and the frequency domain denoising technology is combined, so that the signal quality and the time sequence consistency are improved. The method effectively complements missing data, inhibits noise, enhances data availability, and improves the accuracy and reliability of health monitoring and medical diagnosis.

The invention belongs to the technical field of biomedical signal processing, and discloses an SEEG attack detection method based on a time-frequency space-dynamic graph attention network, which comprises the following steps: constructing an epilepsy brain network sequence, defining SEEG lead points as nodes of a graph, and calculating directed connection edge weights among the nodes through a multiband directed transfer function; constructing a node feature matrix based on epilepsy induced epilepsy index features; arranging the multiband epilepsy brain network according to a time sequence to form a dynamic sequence; extracting space-spectrum correlation by using a GATv2 dynamic attention mechanism, and dynamically weighting node connection weight through a learnable attention matrix; and in combination with a time sequence convolutional network and a time attention mechanism, key time sequence features are extracted, identified and focused. According to the method, a good dynamic propagation characteristic learning capability is obtained, an SEEG pathological connection mode is disclosed, a model mechanism corresponds to an epileptic seizure propagation mechanism, and the method has a good application prospect in the aspects of epileptic seizure detection and epileptic seizure auxiliary positioning.

The invention relates to the technical field of biomedical signal processing and intelligent detection, and particularly discloses a PPG peak detection method based on a signal quality mask and dual-stage verification, and the method comprises the steps: obtaining an original PPG signal sampling sequence and a three-axis acceleration signal, and carrying out the data preprocessing of the original PPG signal sampling sequence; calculating a signal quality index SQI per second, and calculating a body motion variable coefficient CV; carrying out linear fusion on the SQI and CV to obtain a comprehensive quality score Score model; screening local candidate peaks according to a sliding window; backward verification and dynamic correction are carried out; updating a start bit of the sliding window according to the physiological rhythm, confirming a peak value and counting the peak value into a peak value list; setting a score threshold, and rejecting abnormal peaks in the peak list according to a Score model; according to the method, the candidate peak value is searched through the signal quality mask and the sliding window, and a two-stage verification mechanism of the final candidate peak value is determined through backward verification, so that the peak value detection accuracy is high, the motion interference resistance is high, and the real-time performance is good.

The invention provides a sleep physiological parameter real-time monitoring method based on flexible piezoelectric sensing, and relates to the technical field of biomedical signal processing.The method comprises the steps that a human body sleep biological mechanical signal is collected through a flexible piezoelectric sensor array, and after the signal is converted through a charge amplifier, phase-space reconstruction denoising and self-adaptive gain control are carried out; wherein the gain is dynamically adjusted based on the weight of the user, the rigidity coefficient of the mattress and the real-time signal amplitude; extracting heartbeat wave crest intervals, respiratory cycles and body movement frequency domain energy characteristics from the preprocessed signals; inputting the features into a cascaded convolutional neural network and a long-short-term memory network model, and synchronously outputting the heart rate, the respiration rate and the heart rate variability; in combination with the historical health data of the user and the cardiovascular health mapping model, sleep quality indexes and disease risk scores are generated through graph convolutional network analysis, and the monitoring frequency is dynamically adjusted and an apnea alarm is triggered.

The invention discloses a multi-dimensional night urination behavior monitoring system based on rhythm features, and relates to the technical field of biomedical signal processing, and the system specifically comprises a data acquisition module, a rhythm feature extraction module, a urine volume dynamics modeling module, a clustering monitoring module and a real-time feedback module; collecting individual data in real time through IoT equipment and preprocessing the individual data; calculating a nocturnal urination frequency index, a nocturnal urination time concentration ratio, a urination interval rhythm variation coefficient and a deviation index based on the individual data, and performing rhythm feature extraction; performing quadratic polynomial least square fitting on the night accumulated urine volume, and calculating a urine volume acceleration index by using an obtained second derivative to quantify a urine volume generation trend; the method comprises the following steps: constructing and preprocessing a night urine multi-dimensional digital phenotypic vector matrix, fitting a Gaussian mixture model based on an expectation maximization algorithm of a Bayesian information criterion, automatically mapping individuals into four types of subtypes according to cluster center features, and outputting individual subtype labels and confidence coefficients; and obtaining a comprehensive risk score through normalized risk assessment.

The invention discloses a depression identification method and system based on short-time multi-mode signal joint detection, and relates to the technical field of biomedical signal processing and disease detection. Comprising a data acquisition module used for acquiring a short-time electrocardiosignal, a short-time electroencephalogram signal and a short-time voice signal; the preprocessing module is used for performing preprocessing operation on the electrocardiosignals, the electroencephalogram signals and the voice signals; the signal fusion module is used for carrying out deep mining of interaction information on the preprocessed electrocardiosignals, electroencephalogram signals and voice signals based on a dependency relationship on a time sequence to form fusion features; and the classification module is used for performing depression identification and classification on the fusion features by using a classification model. By integrating short-time electrocardio, electroencephalogram and voice signals, potential information among the signals is deeply mined, and the accuracy, reliability and efficiency of depression recognition are improved.

The invention discloses an emotion classification method and system for heart electromagnetic signals of heart rate variability based on topology analysis, and relates to the technical field of biomedical signal processing and emotion calculation, and the emotion classification method comprises the following steps: preprocessing magnetocardiogram signals or electrocardiogram signals; positioning R waves, extracting an RR interval sequence, and extracting statistics of topological feature points of the persistent graph as topological features; and performing fusion through the time sequence branch, the topological branch and the fusion layer to generate a fusion vector, and classifying the emotional state based on the fusion vector. According to the method, the signal quality and the anti-interference capability are remarkably improved, topological data analysis (TDA) is innovatively introduced, quantitative characterization of a heart rate variability nonlinear dynamic structure is achieved, and the capturing capability of the model to a key physiological mode is enhanced; the problem of overfitting under small samples is effectively relieved, the accuracy, robustness and cross-individual generalization ability of emotion classification are remarkably improved, and a new method is provided for emotion calculation.

The invention relates to the technical field of biomedical signal data processing, and particularly discloses a data processing method and system for a blood pressure electrical stimulator, and the method comprises the steps: synchronously collecting an electrocardiosignal, a blood pressure waveform signal and an electrical stimulation parameter signal of a testee at a moment through multiple channels; extracting an electrical stimulation switch moment sequence; constructing a standardized individual response data vector; establishing an individualized physiological response curve model by adopting a generalized linear regression method; and next stimulation parameters are adjusted through a feedback adjustment mechanism. In the prior art, an electrical stimulation method which depends on a fixed rule or empirical parameter setting, especially in an application scene in which physiological response differences exist among individuals, high-resolution physiological response data modeling cannot be realized. Due to the fact that data segmentation, low-sample modeling and predictive regulation and control can be carried out based on event driving, an individualized stimulation and response model is constructed, and the flexibility and safety of control over the blood pressure electrical stimulator are improved.