65 results about "RR interval" patented technology

This invention relates to the field of depression monitoring and intervention technology, and discloses a depression monitoring and intervention system based on an internet hospital and heart rate variability (HRV). The system includes: a multimodal physiological signal acquisition module for simultaneously acquiring raw HRV signals, skin conductance fluctuations, and respiratory rate data; a signal processing module for preprocessing and feature extraction of the raw signals to obtain multidimensional feature vectors; and a depression level inference module that constructs a multilayer perceptron neural network, taking as input the multidimensional feature vector containing multidimensional HRV features, skin conductance fluctuations, and respiratory rate statistics, and outputting probability values ​​for no depression, mild depression, and moderate to severe depression levels. This invention establishes an intelligent management mechanism for internet hospitals for the first time, collecting multidimensional physiological data of patients with depression in real time through wearable monitoring units. Based on the dynamic trends of physiological data characteristics, it can serve as an auxiliary diagnostic tool for psychiatry, especially suitable for early screening and remote follow-up of depression, reducing the consumption of medical resources.

This utility model relates to an electronic device. The electronic device according to this utility model is characterized by comprising a processor that acquires a heart rate variability signal related to the heart rate of a user sitting on the massage chair from a sensor mounted on the massage chair; the processor determines the user's stress level or parasympathetic nervous system activity based on the heart rate variability signal and the user's breathing rate; the processor determines a suitable target breathing rate for the user based on the stress level or the parasympathetic nervous system activity; and the processor controls the massage chair to induce the user to breathe at the target breathing rate. According to this utility model, the electronic device controls the massage chair based on the target breathing rate, thereby inducing the user to breathe at the determined target breathing rate.

An example method for determining cognitive load is described herein. The method includes receiving a cardiac signal measured by a non-contact sensor; determining a heart rate variability (HRV) metric based on the cardiac signal; and determining a cognitive load of a subject based on the HRV metric. An example system for determining cognitive load is also described herein. The system includes a non-contact sensor; a computing device operably coupled to non-contact sensor, the computing device including a processor and a memory, the memory having instructions thereon, that, when executed, cause the processor to: receive a cardiac signal measured by a non-contact sensor; determine a heart rate variability (HRV) metric based on the cardiac signal; and determine a cognitive load of a subject based on the HRV metric.

The present application relates to the technical field of psychophysiological monitoring, and discloses a video psychophysiological index analysis method and system; the method comprises the following steps: positioning facial key points and partitioning from a video, extracting remote optoelectronic plethysmography pulse wave and action unit breathing characteristics, outputting heart rate and breathing signals through Kalman filtering; obtaining a heartbeat interval through peak value detection, calculating heart rate variability indicators and breathing frequency, and normalizing by establishing an individual baseline in an initial calm period; fusing emotional characteristics and normalized indicators to form an observation vector, inferring a stress state through an unsupervised model and a forward algorithm; detecting index mutation points to identify stimulus events, and adopting a gated recurrent unit network to predict excessive stress risk and grade early warning; storing multi-session data into a database, quantifying desensitization rate, tolerance and recovery capacity through linear regression, weighted scoring and model updating. The present application realizes contactless monitoring, real-time stress evaluation and early warning, and supports quantitative analysis of cross-training effects.

This invention discloses a psychological assessment system based on heart rate variability analysis, belonging to the field of medical data analysis and psychological assessment technology. It solves the problems of existing heart rate variability analysis being limited to physiological indicators and traditional psychological assessments lacking objective physiological support. The system is built on a B / S architecture, integrating two core modules: ECG data reception and recording, and graphical analysis reports. It collects raw ECG data via Bluetooth 4.0, automatically calculates core heart rate variability feature values ​​such as SDNN, LF, and HF, achieves hierarchical and quantitative assessment of heart rate variability, and generates integrated graphical reports. It also features dual-layer access control, encrypted data transmission, and backup and recovery functions. This invention achieves deep integration of physiological data and psychological assessment, improving the scientific rigor and accuracy of psychological assessments. The system is highly stable and adaptable, suitable for clinical psychological screening, employee psychological monitoring, and other scenarios. The corresponding product, "Heart Rate Variability Analysis Software," has obtained a Class II medical device registration certificate.

A system and a method for objectively assessing an experience undergone by an individual. The system comprises: a first sensing device, configured to detect a heart rate (HR) of the individual; a second sensing device, configured to detect a heart rate variability (HRV) of the individual; a third sensing device, configured to detect a body temperature (BT) of the individual; a fourth sensing device, configured to detect an electrodermal activity (EDA) of the individual; and a processing device, configured to: determine a current HR of the individual; determine a current HRV of the individual; determine a current BT of the individual; determine a current EDA of the individual; perform a comparison of the current HR, the current HRV, the current BT and the current EDA with respective recommended maximum thresholds; and provide a result of the comparison, in the form of a graphical representation and / or a summary indicator.

This invention discloses an essential oil-based intervention system for the simultaneous treatment of heart and brain function based on dual feedback of eye movement and heart rate variability, belonging to the field of rehabilitation medicine technology. The system includes: a data synchronization acquisition module, which acquires heart rate variability data and eye movement characteristic data and generates a synchronized physiological data stream; a dual feedback joint assessment module, which assesses the patient's real-time physical and mental state level based on the synchronized physiological data stream; a synchronized intervention decision module, which generates essential oil atomization control instructions and eye movement training control instructions in parallel according to the physical and mental state level; and a synchronized intervention execution module, which drives the essential oil atomization unit and the eye movement training unit to execute corresponding intervention actions synchronously at the hardware level on the time axis. This invention achieves integrated and precise intervention of heart and brain function through dual-modal physiological feedback closed loop and synchronized control, effectively solving the problems of brain separation, delayed intervention, and poor coordination in traditional rehabilitation centers, and improving the synchronous rehabilitation effect of cognitive function and autonomic nervous function in stroke patients.

This application provides a multimodal signal processing method and apparatus, which can be applied to the field of multimodal physiological signal fusion technology. The multimodal signal processing method includes: preprocessing an initial multimodal signal acquired by a sensor to obtain a target multimodal signal; extracting feature data from the target multimodal signal, the feature data including at least cerebral oxygenation feature values, heart rate variability feature values, and peripheral pulse oxygenation feature values; generating a psychological state index based on the cerebral oxygenation feature values ​​and heart rate variability feature values; generating a cardio-cerebral oxygenation coupling index based on the psychological state index, cerebral oxygenation feature values, heart rate variability feature values, and peripheral pulse oxygenation feature values; and obtaining control commands for controlling a target device based on the cardio-cerebral oxygenation coupling index.

This invention discloses a method for assessing sleep quality based on multimodal features in a waking state, comprising: S1, guiding subjects to complete a sleep quality scale, collecting pulse wave signals, facial thermal images, and corresponding temperature matrices of subjects in a resting state, and conducting a psychological alertness task test; S2, extracting heart rate, heart rate variability, and pulse wave morphology features from the pulse wave signals; S3, identifying key points on the face, determining regions of interest, and extracting facial temperature features; S4, analyzing the acquired psychological alertness task test data, statistically analyzing and extracting reaction time and attention maintenance-related indicators; S5, constructing a training dataset; S6, using the training dataset to train a classifier and construct a sleep quality classification model; S7, inputting the multimodal features collected from new users in a waking state into the sleep quality classification model and outputting the assessment results. This invention provides a method for assessing sleep quality in a daytime waking state.

The application discloses a depression disorder risk assessment method combining heart rate variability and behavior data, relates to the field of digital mental health, and comprises the following steps: continuously collecting heart rate variability and multidimensional behavior data of a user through a wearable device, fusing the data into a unified feature vector after cleaning, feature extraction and standardization; constructing a depression risk assessment model based on a random forest through weight training and feature screening, optimizing and explaining feature importance by using cross-validation and SHAP value analysis, dividing a risk level according to a risk probability output by the model, and providing an abnormal prompt.The application has the advantages that HRV data and multidimensional behavior data are collected by combining an ECG device and a smart watch, the data are preprocessed and feature screened, and a depression disorder four-level risk assessment is realized based on a random forest model, so that data reliability, assessment accuracy and clinical operability are achieved.

The present application relates to the technical field of image detection, in particular to a multi-modal data video inspection concentration analysis and early warning method and system, the method comprising collecting facial video data, extracting eye micro-motion features, eyebrow shape features and forehead muscle television features in facial features; collecting heart rate variability data and skin electricity reaction data in physiological signals; performing individualized standardization processing, converting into Z-score feature vectors of deviation degree relative to user's own baseline level value; based on the Z-score feature vectors, constructing time sequence feature vectors and inputting into a multi-modal time sequence fusion network for time sequence collaborative mode analysis, outputting current concentration probability value and concentration prediction value at a specified time point in the future; triggering a hierarchical early warning mechanism based on the current concentration probability value and the concentration prediction value. The present application utilizes information complementation in multi-modal data fusion, thereby improving the robustness and accuracy of state recognition.

This invention discloses a method for processing and analyzing VR emotion regulation task monitoring data based on multimodal data, belonging to the field of VR emotion monitoring technology. The method includes: collecting multi-source physiological time-series data such as heart rate variability, skin conductance response, EEG rhythm, and eye movement trajectory through a VR scene emotion association mapping gateway; eliminating data sampling frequency differences and time offsets through multi-source physiological time-series alignment processing; then filtering key feature subsets through high-dimensional feature dimensionality reduction and distillation; utilizing emotion fluctuation gradient tracking to analyze the dynamic fluctuation patterns of analytical features with VR scene changes; establishing a mapping relationship between analytical features and VR scene parameters; generating an emotion-scene association feature matrix; and finally, completing comprehensive processing and analysis of monitoring data based on this matrix to output dynamic emotion change association data. The method achieves deep integration of multimodal data and VR emotion regulation tasks through step-by-step feature extraction, fluctuation tracking, and scene mapping processes, improving the accuracy and targeting of data processing.

This application relates to the field of medical data processing technology, and discloses a multi-parameter physiological signal fusion analysis system and method for endocrinology. The system includes acquisition, processing, and output modules. The method first acquires the subject's blood glucose and heart rate variability sequences, calculates the physical lag time using a first-order difference cross-correlation function, and performs temporal compensation. Next, it uses ensemble empirical mode decomposition to process the heart rate variability sequence, uses the topological closure index to screen the optimal modal components, and determines the regulatory state. Subsequently, it introduces a virtual damping model based on blood glucose rate of change weights to normalize the optimal components and generate phase space trajectories. Finally, it calculates the hysteresis loop area or phase space divergence index based on the regulatory state. This invention solves the problems of temporal asynchrony and modal aliasing of multi-source signals, and provides objective indicators for assessing endocrine regulatory function by quantifying neuro-metabolic coupling characteristics.

The present application belongs to the technical field of arrhythmia auxiliary classification, and particularly relates to an arrhythmia classification system based on double-band waveform feature extraction. The system comprises the following steps: extracting a single heartbeat from an original ECG signal to obtain a heartbeat sequence; predicting the probability of each sampling point in the heartbeat sequence belonging to different waveform types, and determining the starting point and ending point of each waveform to further extract key wave components; fusing the key wave components with overall heartbeat morphological features and RR interval information to obtain heartbeat-level feature representation; using a multi-head self-attention mechanism to extract information in different subspaces of the heartbeat-level feature representation, capture inter-beat dependence, and obtain sequence-level feature representation; and based on the sequence-level feature representation, realizing arrhythmia classification of the original ECG signal. The present application designs a heartbeat-level feature extraction stage and an inter-beat feature interaction stage, and uses the two consecutive stages to realize multi-scale feature extraction and inter-beat information interaction, thereby enhancing the abnormal heartbeat recognition capability.

The present application relates to the technical fields of intelligent wearable device and health monitoring, in particular to a health detection control method of intelligent wearable watch, comprising: multi-source data acquisition step: synchronously acquiring physiological indexes and situation indexes, and acquiring apparent physiological data and situation characteristic data in real time through a sensor network; situation semantic analysis step: based on situation characteristic data analysis, calculating situation metabolic equivalent expected value through a motion dynamics model; compensation evaluation solving step: extracting heart rate variability and recovery rate characteristics, calculating the difference between actual metabolic level and situation metabolic equivalent expected value to obtain compensation deviation degree, and comprehensively generating compensation exhaustion index; adaptive hardware control step: dynamically generating control instruction sequence based on the numerical interval of compensation exhaustion index, and adjusting the working state of the sensor network; the present application significantly reduces the overall power consumption of the system, eliminates invalid false alarms, and improves the monitoring specificity and robustness in complex application environments.

Disclosed is a method for monitoring attainment of flow state for activity by user. The method comprises: receiving first sensor data from eye-tracking means (202), and second sensor data from heart rate monitoring device (204), wherein first and second sensor data are collected while user is engaged in activity; determining eye fixation frequency and eye fixation percentage, and determining heart rate and heart rate variability, by processing first and second sensor data, respectively; determining focus index (304) of user, based on eye fixation frequency and eye fixation percentage; determining stress index (306) of user, based the heart rate and heart rate variability; determining flow index (308) of user, based on difference between focus index and stress index; and detecting that user has attained flow state for activity, when determined flow index is equal to or greater than predefined flow index for activity.

This invention provides a method, system, and device for monitoring anesthesia status based on multi-source physiological signal analysis, relating to the field of medical monitoring technology. The method includes: acquiring electroencephalogram (EEG) signals, real-time heart rate, heart rate variability coefficient, and real-time mean arterial pressure of a patient under anesthesia; performing preliminary filtering of the EEG signals using high-pass and low-pass filters, and normalizing the amplitude of the EEG signals to obtain new EEG signals; acquiring several modal functions; extracting multidimensional features from each modal function; forming a feature set; training a machine learning model using the feature set, and using the trained machine learning model to predict the patient's anesthesia status in real time, outputting anesthesia stage classification results; determining the comprehensive stability coefficient of the anesthesia status; determining the anesthesia depth instability fluctuation coefficient; and performing anesthesia status monitoring. According to this invention, the accuracy of anesthesia status monitoring can be improved.

This application relates to the field of human bioelectrical signal measurement, specifically to a method, system, and apparatus for determining the voltage value of an electrocardiogram (ECG) signal at a predetermined future time. The method involves acquiring an ECG signal over a predetermined time period and processing it in segments to enable data extraction; determining the QT interval trend slope, the proportion of low-frequency energy in the ST segment, and the standard deviation of the RR interval sequence of heart rate variability in the processed ECG signal, and assigning weighting coefficients; determining the average voltage value at each moment within the predetermined time period based on the processed ECG signal; and finally determining the voltage value of the ECG signal at the predetermined future time based on the determined trend slope, low-frequency energy proportion, sequence standard deviation, weighting coefficients, and average value. The method provided by this application allows for accurate determination of the voltage value of a user's ECG signal at a predetermined future time.

This invention provides a multi-dimensional monitoring and assessment method for sleep disorders at high altitudes, comprising: S1, concurrently collecting respiratory signals, blood oxygen saturation and pulse wave (PPG), heart rate / ECG, body movement and position, and environmental parameters such as altitude / air pressure and temperature and humidity during nighttime sleep; S3, performing sleep / wake discrimination and detecting apnea, hypoventilation, and blood oxygen desaturation events based on structured data, and statistically analyzing event metrics; S4, calculating the frequency domain characteristics of heart rate variability (HRV) from RR interval sequences to obtain a sympathetic nerve activity proxy index; S5, weightedly fusing the event metrics, oxygenation metrics, and sympathetic proxy into a comprehensive score R and performing hierarchical interpretation; S6, fitting an individualized risk trajectory model to the cross-night scoring sequence and outputting short-term predictions. This method enables the construction of individualized adaptation trajectories and short-term prediction models in a real-world high-altitude environment, shifting from "static results" to "process evaluation."

The present application relates to the technical field of non-contact vital sign monitoring, and specifically discloses a heart signal detection method based on feature decoupling millimeter wave radar based on contrast learning. The present application realizes the automatic separation of the rhythm feature and the morphological feature of the heart signal from the millimeter wave radar signal by constructing an adaptive filtering module, a sample generator, a multi-scale feature extraction module, a double-path feature decoupling module, a physical constraint module and a contrast learning loss function. The present application uses contrast learning to construct a positive sample pair, guides the model to learn a feature representation that is invariant to heart rate changes and waveform deformation, and combines physiological prior constraints to improve the robustness and interpretability of the features. The present application effectively solves the shortcomings of traditional methods in signal separation, noise suppression and generalization ability, and can be widely applied to heart rate variability analysis, arrhythmia detection and other heart health monitoring tasks.

This invention discloses a method and system for effectively reducing X-ray radiation dose during electrocardiogram (ECG) scanning, relating to the field of artificial intelligence technology. The method includes: acquiring real-time ECG signals of the subject, identifying the R-wave and its occurrence time, and calculating the RR interval between adjacent R-waves; dividing the subject's cardiac cycle based on the RR interval and the R-wave occurrence time, and determining the intense cardiac motion phase and diastolic phase of each cardiac cycle; during the intense cardiac motion phase, controlling the X-ray tube mA value to decrease to a dose reduction mA value below the source mA value; when the current time enters the diastolic phase, controlling the X-ray tube mA value to recover from the dose reduction mA value back to the source mA value; after determining that the X-ray tube mA value has recovered and stabilized at the source mA value, a trigger signal is activated, and the acquisition of projection data is initiated based on the trigger signal. This method helps solve the problem that existing technologies cannot effectively reduce X-ray radiation dose during ECG scanning.

This application discloses a method for monitoring heart rate variability (HRV) and smart glasses, relating to the field of wearable device technology. The method includes: acquiring a collected PPG signal; calculating an HRV index based on the PPG signal; if the HRV index deviates from a preset benchmark data by a factor greater than or equal to a preset threshold, selecting at least one target intervention mode from a set of preset intervention modes, wherein the multiple intervention modes include a vibration-guided mode, an audio-adjusted mode, and an environmental adjustment mode; and outputting a corresponding control signal according to the target intervention mode, the control signal being used to generate vibration, play audio, and / or send environmental adjustment commands. This application improves the practicality and effectiveness of HRV monitoring.

This invention belongs to the field of intelligent sleep state monitoring technology, and particularly relates to an intelligent sleep state monitoring and analysis device, pillow, and method. The device includes two sound sensors and a three-axis accelerometer, as well as a main control module with built-in signal processing. The two sound sensors acquire dual-channel snoring signals, and suppress environmental noise through time delay estimation and beamforming. The three-axis accelerometer is attached to the back of the neck to acquire three-dimensional vibrations caused by breathing and heartbeat, and separates the breathing waveform, heartbeat waveform, and motion artifact components through bandpass filtering and principal component analysis / independent component analysis. The intelligent evaluation algorithm integrates snoring characteristics, respiratory rate, heart rate, and heart rate variability to output sleep stages and apnea events. This invention also provides a pillow incorporating this device and an intelligent sleep state monitoring and analysis method. This invention has a simple structure, can be integrated into a pillow, does not interfere with natural sleep, and is suitable for long-term use in homes and rehabilitation centers.

An ovarian health evaluation method is implemented by a first electronic device. The first electronic device may display a first interface, where the first interface includes a first control; receive a first operation performed on the first control; obtain first data, where the first data includes heart rate variability data of a user within a preset time period; determine an ovarian health evaluation result based on user vital sign data, where the user vital sign data includes the first data, and the ovarian health evaluation result indicates an ovarian health status of the user within the preset time period; and display a second interface after the preset time period, where display content of the second interface includes the ovarian health evaluation result.

Systems and methods are provided for delivering closed-loop or partially closed-loop baroreflex activation therapy (BAT) to treat conditions associated with autonomic dysfunction. A pulse generator delivers stimulation while physiological data, such as ECG, heart rate variability, bioimpedance, and physical activity levels, is collected from one or more sensors. The data is analyzed to assess autonomic nervous system activity, and stimulation parameters are dynamically adjusted in response. In some embodiments, adjustments are made on a beat-to-beat basis using ECG input. Artificial intelligence algorithms may be used to predict patient-specific responses, optimize therapy, and evaluate effectiveness. The method may further include integrating patient-reported symptoms, wirelessly transmitting data, or adjusting stimulation duty cycle based on time of day. The system may further incorporate patient-reported symptoms, a clinician dashboard for remote monitoring, and a multi-channel lead. Stimulation may be delivered non-invasively or via an implantable device.

Disclosed herein are example devices for detecting one or more parameters of a cardiac signal. The devices include one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The devices further include processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

In embodiments of the present disclosure, a device and method for determining a heart rate of a fetus and a heart rate variability of the pregnant woman and the fetus are contemplated. The method includes generating a spectral transform from a waveform representative of a hemodynamic parameter specific to the individual, determining candidate remnant signals included in an area independent of exclusion areas of the spectral transform, identifying a candidate remnant signal from the candidate remnant signals, comparing the candidate remnant signal with a target signal representative of oxygen levels present in blood stream of the individual, the target signal is derived from the hemodynamic parameter, and determining that the candidate remnant signal is associated with a heart rate of the fetus that is disposed in the individual upon identifying an inverse correlation between the candidate remnant signal and the target signal of the individual.

This invention discloses a non-sensory assessment system for driver fatigue based on heart rate variability. The system includes a multimodal sensing acquisition module, a kinematic physics decoupling module, a generative adversarial network (GAN) anti-artifact reconstruction module, an attention extremum point locking module, and a graph network multidimensional state mapping module. The system simultaneously acquires human cardiopulmonary micro-vibration signals, a two-dimensional matrix of body pressure distribution, and three-dimensional aliased angular velocity signals; it maps the extracted body pressure features to a viscoelastic transfer function matrix and performs vector space difference calculation to output a physical space decoupling signal; it uses the physical space decoupling signal as a spatial constraint condition for a conditional generative adversarial network to reconstruct and output a high-fidelity impact map sequence; it utilizes a multi-scale attention mechanism to lock peaks, construct a discrete sequence of cardiac cycles, and extract heart rate variability feature parameters; it combines body pressure features to construct a nonlinear topological graph data structure and performs graph convolution operations to output a multidimensional health assessment map.

This invention relates to the field of heart rate variability (HRV) characteristic analysis technology, and discloses a physical education classroom teaching load monitoring system based on HRV. The system includes: guiding users to complete combined movements; extracting four temporal feature points to fit the device time deviation curve; analyzing four-dimensional data of photoplethysmography (PPG) pulse waves to distinguish wearing status; and combining dynamic stable baselines and disease-specific parameters to complete multimodal data acquisition. It employs a temporal feature-based exercise state classification method to divide exercise states, adaptively switching preprocessing algorithms and parameters, identifying preset individual anaerobic thresholds, and introducing HRV feature interpretation logic for smooth transition state switching. During rest periods, it extracts dynamic features of the RMSSD recovery curve, assesses the degree of recovery based on the user's personal historical recovery model, and groups classes based on current recovery, historical physical fitness, and cumulative load. It also employs a three-level directional voice warning mechanism to generate a multi-dimensional teaching analysis report after class, including individual performance and session effectiveness.

A state information determination method and device, and a control method and device are disclosed, and relate to the technical field of medical data processing. The state information determination method comprises: determining a state evaluation parameter corresponding to a to-be-tested body based on a preset analysis time interval, and determining first state information corresponding to the to-be-tested body based on the state evaluation parameter. The state evaluation parameter comprises at least one of a first heart rate characteristic parameter, a first heart rate variability characteristic parameter, a second heart rate characteristic parameter and a second heart rate variability characteristic parameter. The first heart rate characteristic parameter and the first heart rate variability characteristic parameter correspond to a first time interval, and the second heart rate characteristic parameter and the second heart rate variability characteristic parameter correspond to a second time interval. The present disclosure can improve the accuracy of the determined first state information and the comparability at different times, thereby providing favorable conditions for assisting in determining the disease state of the to-be-tested body and assisting in predicting the progress and the like based on the determined first state information.