65 results about "Counting respirations" patented technology

The invention discloses an intelligent oral snore-ceasing equipment system based on multi-parameter monitoring, and relates to the technical field of intelligent oral snore ceasing. According to the system, physiological data and structural data of a user are collected through a data collection module, and a physiological and structural data set is constructed; the AI dynamic evaluation model module trains an initial model by using a convolutional neural network, continuously optimizes the model and dynamically predicts the snoring symptom risk of an individual by analyzing physiological and structural data; the physiological monitoring module monitors the physiological status of a user in real time, calculates a physiological stability index PSI, judges whether sleep is abnormal or not by combining with a threshold Q1, and adopts an intervention strategy if the sleep is abnormal; the airway disturbance monitoring module judges abnormal breathing by calculating a breathing disturbance coefficient HXY and combining with a threshold Q2, the sleeping posture monitoring module calculates a sleeping posture airway compression coefficient PSRF and combining with a threshold Q3 to judge whether the sleeping posture is reasonable, and if not, a strategy is provided; according to the system, personalized and precise snore stopping intervention can be realized.

The invention relates to the technical field of intelligent monitoring, and particularly discloses a lung function evaluation method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining a universal dynamic threshold range of respiratory rate and respiratory amplitude; continuously collecting breathing data output by the MEMS breathing sensor, and obtaining and updating the moving average value and the moving standard deviation of the breathing frequency and the breathing amplitude in real time so as to calculate the change rate of the breathing frequency and the breathing amplitude in real time; according to the updated moving average value and the moving standard deviation, dynamically adjusting the dynamic threshold range of the breathing frequency and the breathing amplitude; comprehensively considering the adjusted dynamic threshold range and the change rate to carry out breathing abnormity judgment so as to carry out lung function evaluation; according to the method, finally, the adjusted dynamic threshold range and the breathing parameter change rate are comprehensively considered, breathing abnormity judgment is conducted, the lung function evaluation result is generated, breathing abnormity can be recognized more accurately, and the accuracy and reliability of lung function evaluation are improved.

The invention relates to the technical field of respiratory training, in particular to a gamification guide system for respiratory rehabilitation training, which comprises a data acquisition module, a data analysis module, a training adaptation module, a training guide module and a training adjustment module, the breathing state parameter and the thoracic cavity fluctuation reference value are determined, the data analysis module calculates the breathing abnormity characterization value by determining the breathing difference value and the facial feature fluctuation value, divides the breathing rehabilitation training abnormity tendency and calls the corresponding module, and the training adaptation module adjusts the number of breathing force bearing points. The training guide module determines a stress reference value, a training operation characterization value is calculated by combining the stress reference value with the thoracic cavity fluctuation reference value, and the training adjustment module adjusts the number of times of training based on the training operation characterization value. According to the breathing rehabilitation training method and device, the breathing pre-training is carried out, the appropriate training parameters are determined in a targeted mode, the training content is determined according to the real-time training condition, and the breathing rehabilitation training efficiency is improved.

The invention relates to the field of intelligent medical treatment, in particular to an obstructive sleep apnea syndrome detection method and device based on multi-modal data and a program product. Comprising the following steps: S1, acquiring time sequence frame data of infrared thermal imaging of a tester in real time; s2, extracting chest and abdomen position data and air temperature data of a nose and mouth area based on the infrared thermal imaging time sequence frame data; s3, calculating to obtain breathing waveform data through thoracic and abdominal position data in the time sequence frame data; s4, air temperature change curve data are obtained through air temperature data calculation in the time sequence frame data; s5, performing phase correlation on the breathing waveform data and the airflow temperature change curve data to calculate a breathing ratio; comparing the respiration ratio with a preset threshold value, and judging that the obstructive sleep apnea syndrome occurs when the respiration ratio is greater than the preset threshold value. The method can effectively identify the obstructive sleep apnea syndrome, and has a good clinical value.

The present invention proposes a breathing frequency detection method based on time-frequency analysis of Hilbert-Huang transform. First, a WiFi link with strong environmental perception sensitivity is selected to construct a channel state information (CSI) ratio model. Secondly, the filtered CSI ratio time series is projected, and candidate sets of different breathing mode signals are generated by combining amplitude and phase information. Thirdly, the selected breathing modes are subjected to signal variational mode decomposition (VMD) and time-frequency analysis of Hilbert-Huang transform, so as to remove non-human breathing frequency components. On this basis, reconstruction is carried out, and the reconstructed signals are fused using principal component analysis (PCA). Finally, the breathing frequency is calculated through a false peak detection algorithm. The breathing frequency detection algorithm designed by the present invention not only expands the sensing range and eliminates "blind spots" while removing time-varying phase offsets and high-frequency noise and selecting the optimal subcarriers, but also improves the breathing frequency detection accuracy, providing a more accurate detection method for non-contact breathing frequency detection.

A respiratory motion plane alignment system and method based on ultrasound images is disclosed. The system acquires three-dimensional ultrasound images of respiratory organs at at least two different respiratory phases, calculates the motion direction of the respiratory organs based on the three-dimensional ultrasound images, controls and adjusts an ultrasound direction adjustment mechanism to align the scanning plane of the ultrasound probe with the motion direction of the respiratory organs, acquires a three-dimensional model image of the respiratory organs and multiple frames of real-time two-dimensional ultrasound images, performs registration between the multiple frames of real-time two-dimensional ultrasound images of the respiratory organs, and / or registers the three-dimensional model image and the multiple frames of real-time two-dimensional ultrasound images, acquires three-dimensional position data of a target point during respiratory motion, determines the tracking path of the ultrasound probe based on the three-dimensional position data of the target point during respiratory motion, and controls the ultrasound probe to move along the tracking path, thereby achieving long-term stable observation or tracking of target points in the respiratory organs.

The invention relates to the technical field of remote monitoring, in particular to a chronic respiratory disease patient remote monitoring system based on the Internet of Things, which comprises a physiological baseline initialization module, an event response delay measurement and calculation module, a cardiopulmonary coupling phase quantization module and a decompensation risk early warning module. According to the method, an individual blood oxygen and cardiopulmonary coupling health baseline is established to provide accurate reference for subsequent analysis, then the blood oxygen recovery time after apnea is measured and calculated, the physiological response delay characteristic of an individual is quantified, and the characteristic is used for setting a dynamic analysis window to continuously calculate the phase difference between a breathing mode and a blood oxygen signal, so that the phase difference between the breathing mode and the blood oxygen signal is calculated. By continuously comparing the current phase difference with the individual health baseline and triggering early warning when the difference value continuously exceeds a specific range, the early sign of the instability of the cardiopulmonary system can be effectively captured, the conversion from passive response type alarm to active predictive early warning is realized, and a precious time window is provided for preventing decompensation.

The invention relates to an anesthesia puncture visual navigation system combining augmented reality and three-dimensional reconstruction, and belongs to the technical field of anesthesia puncture visual navigation. The anesthesia puncture visual navigation system comprises a physiological movement monitoring unit used for simultaneously collecting respiratory movement cycle data and soft tissue elastic deformation information; a comprehensive motion state data packet is generated by calculating the correlation coefficient of the breathing amplitude and the tissue elasticity modulus change rate, the space deviation caused by breathing motion and soft tissue deformation of a patient to puncture navigation can be effectively compensated, the puncture precision is remarkably improved compared with a traditional static navigation system under the normal breathing state of the patient, and the puncture accuracy is improved. The puncture failure rate and the complication risk are effectively reduced, the problem that precision is further deteriorated due to the fact that correction parameters are still used blindly when registration quality is deteriorated in a traditional system is solved, dependence on personal experience of doctors is reduced, and the problem that the system is completely invalid possibly caused by a single correction mode is solved.

An electronic device and a method for detecting a periodic breathing are provided. The method includes: receiving a respiration signal; calculating a variance degree of the respiration signal; performing a first changepoint detection on the variance degree to obtain a first interval; capturing a first interval signal from the respiration signal according to the first interval; detecting the first interval signal to generate a detection result corresponding to at least one periodic breathing; and outputting the detection result.

The application discloses a kind of multi-camera-based animal vital sign monitoring method, medium, equipment, it is related to animal vital sign monitoring technical field, method includes: using DLC posture estimation network to train animal multi-view video, obtain key point information;According to the key point information of main visual angle, the centroid motion of animal is analyzed and compared with motion threshold, determine animal state, in the continuous image sequence of relative static state time period, according to key point information, obtain region of interest;Corner point is detected in region of interest, and corresponding motion signal is acquired by tracking corner point;Correlation analysis is carried out to motion signal, and sorting is carried out based on information gain, and vital sign signal is obtained by fusing motion signal higher than set threshold;Power spectrum is used to analyze vital sign signal, obtain the energy distribution of vital sign signal, and the value of respiratory rate and heart rate is calculated.The application can effectively reduce artificial intervention, and has the role of promoting to animal experiment method innovation and data analysis.

The present application relates to a respiratory rate calculation method, apparatus and medium based on a wearable device, wherein the method comprises: obtaining an ECG signal and an ACC signal through an electrocardiogram patch on the wearable device; performing baseline filtering on the ECG signal using a high-pass filter, and calculating a first respiratory signal based on the processed ECG signal; selecting an ACC signal whose signal-to-noise ratio meets a preset condition, performing noise filtering using a band-pass filter, and calculating a second respiratory signal based on the processed ACC signal; screening a time domain calculation method and a frequency domain calculation method for calculating the respiratory rate based on the first respiratory signal and the second respiratory signal, and calculating the respiratory rate using the screened calculation method. Through the present application, the problem of how to accurately, continuously and in real time measure the patient's respiratory rate is solved, and the joint calculation of the respiratory rate based on the ECG signal and the ACC signal is realized.

Provided is a respiratory rate measurement device provided with: a patient connection unit for acquiring biological information for detecting respiration from a user; a conversion unit for converting the biological information detected by the patient connection unit into an electric signal; a respiratory rate calculation unit for processing the converted electrical signal and calculating a respiratory rate; and a signal output unit that outputs the calculated respiratory rate by means of at least one of voice, screen display, and communication signals. And a respiratory rate calculation unit that calculates respiratory accompanying information including a respiratory interval for each respiration detected on the basis of the electrical signal or an amplitude of a detected respiratory waveform, calculates an index relating to reliability of the calculated respiratory rate on the basis of the respiratory accompanying information, and outputs the index by means of the signal output unit. The respiratory rate measuring device is configured to calculate the respiratory rate and calculate an index related to the stability of the respiration, thereby determining the condition in which the respiration can be stably detected.

The invention discloses an apnea syndrome evaluation method based on a smart watch. According to the method, a photoelectric volume pulse wave signal and a three-axis acceleration signal in the sleep period of a user are collected through a built-in sensor of the smart watch, and filtering and window segmentation preprocessing is carried out on the signals. Extracting multi-dimensional features such as heart rate, heart rate variability, body movement index and respiratory rate from each analysis window, and constructing a feature vector sequence; and analyzing the feature vector by using a pre-trained machine learning classification model, outputting the apnea event probability and performing binary classification. And finally, quantitatively analyzing the event sequence, calculating an apnea hypopnea index and dividing risk levels. According to the method, the signal processing technology and the machine learning algorithm are combined, intelligent identification and risk assessment of the sleep apnea event are achieved, and an effective solution is provided for portable monitoring.

The application discloses a kind of self-adapting oxygen supply control equipment, belong to oxygen supply control equipment technical field.It includes: acquisition module is used to obtain and time stamp alignment user breathing state signal;Calculation module is used to preprocess respiratory flow and pressure signal, calculate signal quality index;Identification module is used to establish basic parameter set, constructs feature vector, and identifies breathing phase;Determination module is used to calculate respiratory demand score, and determines target oxygen supply interval;Correction module and control module are used to output target oxygen supply parameter vector in target oxygen supply interval by oxygen supply target prediction model, after phase differentiation correction and safety constraint, generate final execution control quantity, and convert into instruction oxygen supply equipment execution.This equipment is through the alignment of multiple source breathing signals, feature extraction and closed-loop safety correction, realizes the accurate adaptation of oxygen supply output and real-time physiological demand.

The present invention discloses a respiratory rate measurement method and device. The implementation of the method includes: collecting pressure map data and determining the chest and abdomen regions in the pressure map data; determining a target unoccupied area according to a preset rule, and obtaining first pressure data of the chest and abdomen regions and second pressure data of the target unoccupied area; performing collaborative filtering processing on the first pressure data by using the second pressure data to obtain an adaptive filtering signal; performing band-pass filtering processing on the adaptive filtering signal to obtain a respiratory wave signal; extracting peak points from the respiratory wave signal to obtain peak intervals; and calculating a respiratory rate value based on the peak intervals. By extracting the positions of the chest and abdomen and using the pressure signals of the unoccupied area to perform collaborative filtering on the pressure signals of the positions of the chest and abdomen, the signal quality is improved to a certain extent, the accuracy of the respiratory rate is increased, and the respiratory rate measurement can be realized in complex scenarios such as multiple people and multiple people and multiple objects. The applicable scenarios are extensive and the accuracy is higher.

The invention provides a non-contact cardiopulmonary coupling analysis method and system based on millimeter wave perception. The method comprises the steps that millimeter wave frequency modulation signals are continuously sent to a subject, initial echo signals are received and converted to obtain converted echo signals, and the distance position to a target is positioned from the converted echo signals; calculating a first phase for the conversion echo signal at each time point of the distance position of the target, calculating a second phase based on each time point and the first phase at a time point before the time point, and calculating a radial displacement signal based on the second phase; calculating a breathing displacement signal and a heartbeat displacement signal based on the radial displacement signal, calculating a breathing instantaneous phase of each time point based on the breathing displacement signal, and selecting at least one target time period based on the breathing instantaneous phase; a heartbeat accompanying change value of the target time period is calculated based on the heartbeat displacement signal of the target time period, and a modulation index is calculated based on the heartbeat accompanying change values of all the target time periods.

There is provided a breath sensing system including: a breath sensor which senses exhaled air, the breath sensor having a light emission unit which emits light that is directed toward a path through which the exhaled air passes, and a light receiving unit which receives at least a part of the light, and which outputs a light receiving signal; a respiration information calculation unit which calculates respiration information; a reliability calculation unit which calculates reliability level information; an output unit which outputs the respiration information and the reliability level information; a receiving unit which receives the respiration information and the reliability level information; a movable unit which is provided with the breath sensor, and which has a variable position; and a notification unit which provides a notification of information related to the movable unit, according to the reliability level information.

This invention discloses a fine-grained respiratory monitoring method and device based on a diffusion generation model using millimeter-wave radar. The method includes: acquiring echo signals received by the millimeter-wave radar and calculating a one-dimensional range image; locating the range cell containing the human chest cavity and extracting continuous phase information; constructing a diffusion chain from phase observation to the respiratory truth value by using the phase as input to the forward diffusion process and the difference between the respiratory truth value and the phase as progressively added noise; designing a radar diffusion denoising device based on an attention mechanism and a feedforward neural network; training the radar diffusion denoising device according to the reverse diffusion process of the diffusion chain, iteratively optimizing the denoising device parameters by reducing the difference between the respiratory waveform output by the denoising device and the respiratory truth value; inputting the signal to be denoised into the radar diffusion denoising device to complete respiratory waveform denoising and calculate the respiratory frequency. This invention improves the accuracy of respiratory waveform reconstruction and the precision of respiratory frequency estimation.

This enables a simple and easy assessment of the reliability of respiratory rate, without requiring a complex system configuration. [Solution] A biological information measuring device is configured comprising: a respiratory rate calculation unit 7c that calculates the respiratory rate of a subject by deriving a respiratory waveform, which is a periodic virtual waveform, from a photoplethysmography waveform obtained from the subject; a reliability data generation unit 7d that calculates the variance of the period or the variance of the amplitude of the respiratory waveform derived by the respiratory rate calculation unit 7c, and generates reliability data that quantifies the reliability of the respiratory rate calculation result by the respiratory rate calculation unit 7c based on the calculated variance; and a display output unit 6 that displays and outputs the reliability data generated by the reliability data generation unit 7d.

This invention relates to the field of sleep monitoring data fusion processing technology, and discloses a sleep monitoring method based on the fusion of non-invasive brain oxygen saturation (AHI) and multiple physiological parameters. The method includes: S1, simultaneously acquiring multiple physiological parameter signals during the subject's sleep; S2, extracting and analyzing the AHI signal to generate multiple characterization markers; S3, identifying respiratory events related to the subject's apnea information from the respiratory signal and pulse oximetry signal, calculating the apnea-hypopnea index (AHI); and performing correlation analysis on the identified respiratory events and multiple characterization markers to calculate a correlation coefficient; S4, obtaining a comprehensive sleep monitoring assessment report based on the correlation coefficient and the AHI to determine the severity of the subject's current respiratory disturbance and the risk of brain tissue hypoxia. By improving the effectiveness of AHI in non-invasive brain oxygen saturation monitoring, the confirmatory nature of non-invasive brain oxygen saturation information in the respiratory correlation diagnosis during sleep monitoring is ensured.

The application relates to the technical field of data processing, in particular to a breathing intensity data processing system and method and a breathing training device, which are applied to guiding a user to perform inhale and exhale training; the method comprises the following steps: collecting breathing signals of multiple breathing cycles; identifying a starting point and an ending point of each cycle; calculating breathing characteristics such as breathing air time ratio and waveform symmetry through feature extraction logic; and adjusting training feedback based on the breathing characteristics; adopting a topological modeling and derivative analysis mode to correct cycle boundaries, and improving boundary recognition accuracy and training individual adaptability; the breathing training device comprises an air pressure adjusting unit, an atomization unit and a data processing module, and can dynamically optimize training output according to the user state.

This invention discloses an adaptive control method and system for nebulized flow rate based on respiratory rate detection, relating to the field of medical device nebulization control technology. The method includes: acquiring and filtering upstream and downstream pressure sequences of the nebulization path; calculating a differential pressure short-circuit index and generating an event flag based on the degree of synchronization fluctuation and differential pressure amplitude of the two sequences; determining a respiratory rate estimation signal and calculating the respiratory rate based on the flag; generating a baseline nebulized flow rate based on the respiratory rate and correcting it with the differential pressure short-circuit index to obtain the target nebulized flow rate; and finally determining the nebulization execution amount. This invention enables adaptive and conservative adjustment of the nebulized flow rate, improving delivery stability.

The invention belongs to the technical field of medical equipment, and discloses a respiration detection device and method based on magnetic field intensity, and the device comprises a device host and a permanent magnet which are separately pasted on the chest and abdomen of a human body through an elastic pasting material. A three-axis fluxgate is arranged in the device host and used for sensing space magnetic field intensity change caused by displacement of the permanent magnet due to respiration, and respiration parameters are calculated according to the space magnetic field intensity change. The surface of the host is provided with mutually vertical indicating lamp bands, so that a user can be visually guided to accurately position the permanent magnet to an area with strongest magnetic field induction, and the detection sensitivity is ensured. The device supports a single-permanent-magnet working mode and a double-permanent-magnet working mode, and the double-permanent-magnet working mode can synchronously detect breathing movement in multiple directions by utilizing a magnetic field superposition principle, so that the signal strength and the anti-interference capability are remarkably improved. According to the invention, non-constraint, high-precision and high-comfort respiration monitoring is realized, the operation is simple and convenient, and the method is suitable for clinical and family scenes.

Video cameras capture video images of livestock and transmit the images to a computer. The computer identifies individual livestock based on biometric markings. The computer performs pixel enhancement to amplify breathing motion and calculates a respiration rate. A sudden increase in respiration rate indicates respiratory compromise, such as pneumonia or BRD.

The invention discloses a respiratory event recognition method and device, a storage medium and a computer program product, and relates to the technical field of medical monitoring, the method comprises the following steps: collecting throat vibration data through a three-axis acceleration sensor of throat snore-ceasing equipment, and generating a vibration data sequence according to the throat vibration data; calculating an environmental noise baseline according to the vibration data sequence, and identifying a breathing emergency based on the environmental noise baseline; and calculating the current breathing amplitude according to the vibration data sequence, and calculating a breathing amplitude ratio based on the current breathing amplitude and a pre-constructed breathing cycle amplitude model so as to identify an apnea event. The throat vibration data is collected through the three-axis acceleration sensor, and the synergistic effect of the environmental noise baseline and the respiratory cycle amplitude model is combined, so that the recognition precision and recognition efficiency of the apnea event and the respiratory emergency are improved.

A respiratory apparatus evaluates accuracy of a pressure sensor, such as when only a single pressure sensor is provided. The accuracy of the pressure sensor may be assessed based on pressure measurement obtained from the pressure sensor and a subordinate or secondary characteristic of the respiratory device such as altitude or atmospheric pressure. A controller or processor may calculate the altitude of the respiratory device based in part on the pressure measurement. In some embodiments, the assessment of the pressure sensor may involve an evaluation of the calculated altitude. In some cases, the assessment of the pressure sensor may involve determining an estimated pressure based on a calculated altitude, and comparing the pressure measurement obtained from the pressure sensor with the estimated pressure.

Apparatus, such as a respiratory therapy device 4000, may generate a signal representing an estimate of a patient breathing pattern when using a flow therapy device with an un-sealed patient interface. The device may include a blower and may generate a flow of air at a controlled flow rate. The apparatus may include a controller. The generation may involve computing an estimate of the breathing pattern as a function of a device flow rate signal; a device pressure signal; a device conductance; a patient conductance; and a coupling parameter that characterizes a degree of coupling between the interface and the patients nares. An estimate of an exit pressure representative of a pressure of a flow of air generated by the therapy device just outside an orifice of the unsealed interface may also be computed. An estimate of a flushing flow rate from the estimated exit pressure may also be computed.

The application provides a method and system for promoting diaphragm recovery by abdominal compression based on respiratory depression recognition, relates to the technical field of respiratory function rehabilitation, and comprises the following steps: acquiring chest and abdominal movement signals and respiratory airflow signals, calculating respiratory state representation parameters to determine respiratory depression, calculating pressure conduction efficiency of a compression point based on the chest and abdominal movement signals and establishing a pressure superposition constraint relationship, generating force coordination control parameters and periodic compression control instructions, and driving a compression device to apply mechanical stress and realize real-time optimization. The method can accurately identify the respiratory depression state, accurately control and adaptively optimize the compression force, and effectively promote diaphragm function recovery.

A system and method for determining the respiratory rate of a subject is provided. A respiratory signal is received, and a derivative of a low-pass filtered version of the respiratory signal is taken to obtain a derivative signal. In one example, a slope sum filter is applied to transform the derivative signal, and multiple respiratory intervals are identified from the transformed signal. The respiratory characteristics in the filtered respiratory signal are obtained using the transformed signal. The appropriate respiratory intervals are selected based on the respiratory characteristics, and a respiratory rate is calculated based on these selected respiratory intervals.

This invention relates to the interdisciplinary field of computer image detection and biomedical engineering, and discloses a method and system for respiratory motion monitoring based on Kinect. The method includes: simultaneously acquiring Kinect 3D point cloud sequences and bioimpedance time-series data; extracting first and second respiratory signals respectively; evaluating Kinect signal quality in real time, and generating continuous respiratory waveforms using a main signal output or dynamic weighted fusion strategy under non-interference, interference, and transitional states respectively; and calculating respiratory rate, relative rate of change of tidal volume, and rhythm variation coefficient. The system includes a Kinect sensing unit, a bioimpedance measurement unit, a signal processing and fusion unit, and a respiratory parameter calculation unit. This invention significantly improves the robustness and reliability of respiratory monitoring in complex scenarios through dual-modal redundant sensing and adaptive fusion mechanisms.