72 results about "Respiratory flow" patented technology

The invention relates to a method for identifying a hypoventilation type, computer equipment and a storage medium. The method comprises the following steps: acquiring breathing state data of a target user; determining breathing characteristic information according to the breathing state data; the breathing characteristic information comprises a breathing peak change rate, a breathing pressure change rate and a breathing flow ratio; and according to the breathing peak change rate, the breathing pressure change rate and the breathing flow ratio, the hypoventilation type of the target user is determined. By means of the collected breathing state data, whether a hypoventilation event occurs or not can be judged, the specific hypoventilation type can be determined in combination with the characteristics of the hypoventilation type, only the breathing-related data is collected, the obtained data is not affected by motion artifacts and changes of the elastic inertia of the pipeline, and the accuracy of hypoventilation is improved. The accuracy of the data for determining the hypoventilation type is improved, so that the accuracy of identifying the hypoventilation type is improved.

The invention discloses a system and a method for monitoring and analyzing a breathing mode of an individual. The system integrates a first sensing device for a nasal cavity and a second sensing device for an oral cavity through monitoring equipment worn on the head and the face. The core of the method is that synchronous quantitative calculation of mouth and nose breathing flow is achieved for the first time through the schemes of hardware decoupling (parallel ultrasonic measurement of oral geometry and flow velocity) and algorithm decoupling (calculation of nasal cavity flow and area from coupling signals in combination with user calibration). According to the invention, the core biomarker ONFR is obtained, and the problem of ambiguity of oral respiration and apnea in the prior art is solved. The invention further discloses a multi-level precision framework, artifact recognition based on double IMUs and a'digital phenotype 'risk early warning technology based on cloud AI. The invention has important application value in the fields of sleep health, sports physiology and the like.

The invention discloses an underground breathing flow monitoring method and system based on adaptive filtering, and the method comprises the steps: collecting an airflow signal at the mouth and nose of a miner in real time through an MEMS hot film sensor, and synchronously collecting a body vibration signal through an acceleration sensor, and obtaining original breathing data containing dynamic interference and corresponding vibration data; the method comprises the following steps: preprocessing original breathing data containing dynamic interference and corresponding vibration data to obtain a digital breathing flow signal and a standardized reference vibration signal; jointly inputting the digital respiratory flow signal and the standardized reference vibration signal into a filtering processing model based on an LMS adaptive algorithm to obtain a target respiratory flow signal; performing data analysis on the target respiratory flow signal, and calculating to obtain a real-time respiratory flow value, respiratory frequency and tidal volume parameters of the monitored object; the real-time breathing flow value, the breathing frequency and the tidal volume parameters obtained through calculation are locally displayed in real time, and monitoring of breathing flow data during underground operation is achieved.

The invention relates to a respiratory flow sensor comprising a flow tube, which has a longitudinal axis, a first flow tube portion and a second flow tube portion, comprising a flow resistor, which is disposed between the first flow tube portion and the second flow tube portion in the flow channel, and comprising two ports for extracting the pressure difference generated by the flow resistor. The first port opens into the first flow tube portion via a first connecting line and said first port is disposed at the first flow tube portion. The second port opens into the second flow tube portion via a second connecting line and said second port is disposed at the second flow tube portion. The ports each comprise a connecting line portion, which extends substantially parallel to the longitudinal axis of the flow tube. The openings of the ports are aligned in the same direction.

The invention relates to a parameter adjusting method and device of a breathing machine, computer equipment and a storage medium. The method comprises the following steps: acquiring gas state data and environment state data of the breathing machine; according to the gas state data and the environment state data, target adjusting parameters of the breathing machine are determined; the target adjusting parameters comprise at least one of target tidal volume, target breathing flow, breathing pressure rise time, target water tank temperature and target water tank humidity; and according to the target adjustment parameter, performing parameter adjustment on the breathing machine. Closed-loop feedback control of the gas state data and the environment state data is synchronously achieved, and the target tidal volume, the target breathing flow, the breathing pressure rising time, the target water tank temperature and the target water tank humidity are cooperatively adjusted, so that the systematic precision and the dynamic response efficiency of parameter regulation and control of the breathing machine are remarkably improved.

The invention provides a portable respiratory flow measuring instrument calibration method and system, and the method comprises the steps: sequentially connecting a gas generation device, a to-be-calibrated portable respiratory flow measuring instrument and a high-precision gas flow calibration instrument in series, constructing a stable gas flow channel, and ensuring that gas flow sequentially flows through each instrument; starting a gas generation device, and synchronously acquiring data after the gas flow is stable, namely an original measurement physical quantity vector x, a theoretical flow value and actual flow data; repeating the acquisition process to obtain a data set covering the full scale; the collected data are input into terminal equipment, a proper machine learning algorithm is selected for training according to the instrument principle, the target is to learn the relation between x and x, and a flow function which is applicable to the full scale and finally forms correction is obtained; in actual operation, the instrument calculates theoretical flow through x collected in real time, the trained flow is called for correction, and a high-precision flow value is output. The technical problems that in the prior art, equipment is complex, cost is high, operation is tedious, efficiency is low, intelligence and precision are insufficient, and scene adaptability is single are solved.

The invention relates to a method and a system for monitoring breathing flow of underground operating personnel based on a differential pressure transducer, and the method comprises the steps: installing the differential pressure transducer in a breathing mask or a breathing pipeline of the underground operating personnel, and collecting an original pressure difference signal; denoising and correcting the original pressure difference signal to obtain a corrected pressure difference signal; according to a preset flow calculation model, the corrected pressure difference signal is converted into corresponding respiratory flow data, and the preset flow calculation model is a correlation mapping model of pressure difference and respiratory flow; and performing anomaly detection on the respiratory flow data to obtain an anomaly alarm signal. The limitation that traditional flow monitoring needs a complex device is broken through, and the accuracy of respiratory flow calculation is improved.

This application provides a method, system, and device for ventilator mask recognition based on transfer learning. The method collects time-series data of respiratory flow and pressure using flow and pressure sensors inside a home ventilator; a data preprocessing module cleans the time-series data to obtain high-quality respiratory flow and pressure time-series data; a data segmentation module divides the preprocessed time-series data into discrete flow and pressure data segments; a transfer learning module transfers the parameters of the source model to the target model, and the target model is trained and fine-tuned using the segmented discrete flow and pressure data segments to obtain the final mask type recognition model. The final mask type recognition model can accurately predict the ventilator mask type based on respiratory flow and pressure signals. This application utilizes transfer learning to improve the robustness of the model to mask type recognition across individuals and devices.

The invention relates to an adaptive breathing phase estimation method, which comprises the following steps of: firstly, acquiring data through a breathing flow sensor, preprocessing a breathing signal by adopting a Kalman filter, removing noise interference in the breathing signal, and ensuring that the acquired breathing data is accurate and reliable; further, the extreme value of the lung volume curve is calculated, then potential phase conversion points are recognized, and division of breathing phases is achieved; then, through a real-time anomaly detection mechanism, non-rhythm respiration data is quickly rejected, and only rhythm respiration data is reserved for estimating a respiration phase. According to the algorithm, the safety of the breathing assisting system in the breathing training process can be ensured, and human body confrontation caused by control logic confusion is avoided; and finally, estimating the respiratory rhythm change of the human body based on a prediction algorithm of physiological features, and completing the accurate recognition of the respiratory phase. The method provided by the invention can effectively adapt to human respiratory rhythm changes, and can provide powerful support for real-time control and stable operation of a respiratory assist system.

A respiratory flow therapy apparatus including a sensor module can measure a flow rate of gases or gases concentration provided to a patient. The sensor module can be located after a blower and / or mixer. The sensor module can include at least an ultrasonic transmitter, a receiver, a temperature sensor, a pressure sensor, a humidity sensor and / or a flow rate sensor. The receivers can be immersed in the gases flow path. The receivers can cancel delays in the transmitters and improve accuracy of measurements of characteristics of the gases flow. The receivers can allow for detection of a fault condition in a blower motor of the apparatus.

Methods and apparatus infer or indicate sleep stage(s) of a patient from a respiratory flow rate signal of the patient. The method may include applying a plurality of detection pathways to a signal representing a respiratory flow rate of the patient, wherein each detection pathway is configured to generate start events and end events indicating start times and end times of episodes respectively of a corresponding sleep stage, wherein each start event and each end event has a priority; and combining the start events and end events based on their priorities to produce an indication of the sleep stage of the patient. The apparatus may include a sensor configured to generate a signal representing a property of a flow of air within a patient interface; and a processor configured to implement a method of inferring a sleep stage of the patient from the signal.

The present application relates to the technical field of sleep intervention, in particular to an intelligent sleep instrument and a sleep instrument control method, the system comprising a respiratory mutation recognition module, a slow wave initiation linkage module, a pharyngeal cavity contraction evaluation module, an intervention section positioning module and a delay trigger control module. In the present application, the extreme points densely appearing in the continuous cycle are accurately positioned as the key nodes of respiratory abnormalities through the identification of the mutation of nasal respiratory flow, and the slow wave initiation stage closely related to the respiratory mutation is screened out and marked as the linkage period in combination with the power curve change trend of the electroencephalogram signal. The pharyngeal cavity state is dynamically evaluated through the change of acoustic echo in the period, the intelligent postponement and delay processing of the intervention behavior are realized through the comparison of the trigger point of the intervention action and the boundary of the platform section, the accuracy of the sleep intervention timeliness and the adaptability of the intervention execution are improved, and the respiratory event control effect and the sleep continuity are significantly improved.

The invention discloses an intelligent control method and system for a noninvasive respirator, and relates to the technical field of intelligent control of respirators. According to the method, airway flow and pulse wave signals are obtained through signal collection and respiratory phase recognition, the end-expiratory pause time period is positioned, a time-frequency image is constructed and preprocessed to obtain a high-resolution enhanced time-frequency energy image, and cardiac oscillation components are recognized and separated to obtain pure respiratory flow signals. The autonomous inspiration effort is judged through double triggering conditions, and air supply control is achieved; verifying parameters are evaluated and adaptively updated based on the respiratory cycle, and personalized configuration parameters are locked; the problems of false triggering and leakage triggering air supply caused by cardiac airway flow oscillation interference generated by cardiac pulsation in the ventilation process of the noninvasive breathing machine are effectively solved, and the accuracy and reliability of air supply triggering of the breathing machine are remarkably improved.

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.

An apparatus for treating respiratory disorders in a patient is disclosed. The apparatus includes a pressure generator configured to deliver a flow of air at positive pressure through a patient interface to an airway of the patient, a sensor configured to generate a signal representative of a respiratory flow of the patient, and a controller. And a controller configured to control the pressure generator to deliver a ventilation therapy with a base pressure and pressure support through the patient interface, an apnea being detected from the signal representative of a respiratory flow of the patient, controlling the pressure generator to deliver one or more detected breaths to the patient during the apnea, determining patency of the airway of the patient from a waveform of the respiratory flow signal responsive to one of the one or more detected breaths, an effective duration of the apnea is calculated based on the patency of the airway, and a setpoint of the base pressure for the ventilation therapy is adjusted in response to the apnea based on the effective duration of the apnea.

The application discloses a CBCT-based child obstructive sleep apnea syndrome identification system and relates to the technical field of medical networks, and comprises the following steps: acquiring a CBCT image and physiological characteristic parameters of a child, performing airway segmentation and reconstructing an airway surface model; establishing an airway respiratory flow field based on the model, setting boundary conditions for simulation, extracting dynamic characteristics to construct first discriminant characteristics; inputting the first discriminant characteristics into a classification model to determine whether the first discriminant characteristics are in a fuzzy discriminant interval; if the first discriminant characteristics are not in the interval, directly outputting an obstructive sleep apnea syndrome type; if the first discriminant characteristics are in the interval, extracting airway cross-section structure information, fusing the first discriminant characteristics to obtain second discriminant characteristics; and matching the characteristics with preset rules to determine whether to adopt enhanced classification or structure registration for secondary discrimination, finally outputting a child apnea syndrome type, and improving the reliability and accuracy of the final diagnosis result.

The application discloses a kind of methods for predicting dust miner's breath flow based on random forest algorithm, including dust miner's breath flow prediction system, the dust miner's breath flow prediction system is by miner's breath characteristic parameter monitoring component, respirator and algorithm prediction module Composition, further include: the miner's breath characteristic parameter monitoring component includes heart rate detector, blood oxygen detection device, temperature tester and portable individual breath monitor, the respirator is viscoelastic silica gel half mask, the respirator is equipped with exhalation valve, the half mask inside and outside both sides are evenly distributed with pressure sensor, the algorithm prediction module is by information receiving unit, central processing unit and information output unit Composition, miner's breath characteristic parameter real-time upload to information receiving unit, and import central processing unit and algorithm prediction are carried out data processing after, and output by information output unit, the method prediction model of the present application is robust, and prediction accuracy is high.

The application discloses a combustion mode-based metabolite testing device and method. The metabolite testing device comprises a combustion control module, a respiratory gas flow control module, an exhaled gas heating and humidifying control module, a respiratory gas separation module, a control unit and a computer. The combustion control module is used for simulating human exhaled gas; the respiratory gas flow control module is used for simulating human respiratory flow; the exhaled gas heating and humidifying control module is used for simulating the temperature and humidity state of human exhaled gas; and the respiratory gas separation module is used for simulating the human mouth and nose breathing process. The control unit is responsible for the control of motors and valves, and the computer is responsible for data acquisition, calculation analysis and result display. The testing method is to set human metabolic parameters, connect a human metabolic tester and test, and finally perform quality inspection analysis on the metabolic parameters of the human metabolic tester according to the test result. The application can be applied to the application scene of human metabolic tester accuracy evaluation, and realizes easy, economical and effective verification of the human metabolic tester.

This invention discloses a gas mixing flow control method and system for a ventilator, comprising: A1: collecting and preprocessing time-series data of airway pressure and respiratory flow; A2: constructing a respiratory noise reduction and enhancement gating mechanism to extract noise-reduced respiratory features; A3: extracting sleep posture features based on the noise-reduced respiratory features; A4: calculating the probability distribution of sleep posture states at each time step based on the sleep posture features using global average pooling and a classification layer, and selecting the state code corresponding to the maximum probability to obtain the sleep posture state sequence; A5: extracting comprehensive respiratory features; A6: extracting gas mixing features based on the comprehensive respiratory features; A7: outputting the gas mixing flow result based on the gas mixing features and controlling the operation of the ventilator's oxygen solenoid valve and air fan. This invention can solve the problem of traditional methods failing to address the mismatch between ventilator parameter adjustments and the turning rhythm of elderly COPD patients.

The invention comprises a method of operating a breathing apparatus comprising measuring a baseline breath flow parameter being respiratory rate and / or tidal volume or a parameter derived therefrom, varying the flow rate provided by the breathing apparatus, measuring a current breath flow parameter being respiratory rate and / or tidal volume or a parameter derived therefrom, comparing the baseline and current breath flow parameters, and altering operation of the breathing apparatus based on the comparison. The invention also comprises a breathing apparatus that implements the above method.

The application provides a respiratory impedance detection method, comprising the steps of: generating an oscillating airflow at a fixed frequency f, collecting respiratory pressure and respiratory flow data of a subject under the oscillating airflow respectively; segmenting the data length of the respiratory pressure and respiratory flow according to the repetition rate respectively; performing Fourier transform on each segmented respiratory pressure and respiratory flow, calculating the respiratory impedance and respiratory reactance of each data segment at different frequency values, and obtaining an initial respiratory impedance-frequency graph and an initial respiratory reactance-frequency graph respectively; performing correlation denoising on the multiple respiratory impedance and respiratory reactance at each frequency value respectively, and obtaining a denoised respiratory impedance-frequency graph and a denoised respiratory reactance-frequency graph; and performing K-means clustering calculation on the denoised respiratory impedance-frequency graph and the denoised respiratory reactance-frequency graph respectively, and obtaining a respiratory impedance-frequency curve and a respiratory reactance-frequency curve. The detection method provided by the application has small errors and high accuracy.

The invention relates to a respiratory flow sensor comprising a flow pipe, which has a flow channel and, on the free ends thereof, hose connections for hoses, comprising a cover flap, which is arranged in the flow channel and divides the flow channel into a first flow channel portion and into a second flow channel portion, and comprising connections, which lead into the flow channel on both sides of the cover flap and serve for the reduction of the differential pressure created by the cover flap. Provision is made in the flow channel for a water drainage device, which comprises drainage grooves. The cover flap has a hinge region, which is formed by a first slot and a second slot, which is embodied in an H-shaped manner.

The application discloses a thoracico-abdominal motion respiration flow monitoring method and device, and relates to the field of respiration monitoring. The method comprises the following steps: acquiring a pressure value of a patient pressure monitoring point; the pressure monitoring point comprises a thoracic pressure monitoring point and an abdominal pressure monitoring point; performing data analysis according to the pressure value to determine a displacement change amount of the pressure monitoring point relative to an initial spatial coordinate; performing nonlinear fitting according to the displacement change amount to determine a thoracic volume change amount and an abdominal volume change amount; determining a respiration parameter according to the thoracic volume change amount and the abdominal volume change amount; the respiration parameter comprises a total lung ventilation, a thoracic respiration contribution ratio, an abdominal respiration contribution ratio and a thoraco-abdominal phase difference. The application realizes low-cost and high-precision respiration flow monitoring.

To accurately detect an occurrence of snoring.SOLUTION: A medical device includes: a generation device for generating a high-frequency data (302) from a flow data (301) representing a time series of a respiratory flow of a patient; a determination part for determining an evaluation value of the high-frequency data in each period for each of a plurality of periods (304_1-304_n); a selection part for selecting one period of the plurality of periods based on the evaluation values of the plurality of periods; and a detection part for detecting an occurrence of snoring of the patient by analyzing a portion of the selected one period of the flow data.SELECTED DRAWING: Figure 3

The invention relates to the technical field of medical instruments, in particular to an anesthesia device convenient for anesthetic concentration adjustment, which comprises a dilution air supply part, one end of the dilution air supply part is communicated with an anesthesia evaporation tank, and one end, far away from the dilution air supply part, of the anesthesia evaporation tank is communicated with a breathing tube. The end of the breathing tube away from the anesthetic vaporizer communicates with a mask part and a waste gas tank; a pneumatic assembly is arranged on the breathing tube, a negative pressure output assembly is arranged in the pneumatic assembly, a transmission assembly is arranged between the negative pressure output assembly and the pneumatic assembly, and when the amount of gas exhaled by a user is increased, the pneumatic assembly adjusts the negative pressure output assembly through the transmission assembly so as to increase the flow speed of anesthetic steam. The turbine mechanism is directly driven by the breathing flow of a user to adjust the flow speed of anesthesia steam in a linkage mode, the rotating speed change is monitored in real time, the valve opening degree is dynamically adjusted, and rapid response and dynamic adjustment of the anesthetic concentration during sudden breathing change are achieved.

To provide a respiration assisting device that can accurately capture the respiration waveform of a patient, and that enables appropriate control of blower operation in accordance with the respiration flow of the patient.SOLUTION: A respiration assisting device of the present disclosure includes: a body housing 100A forming an air flow path 132; a blower 131 which is provided in the flow path 132 and generates an air flow; and a differential pressure sensor 164 for measuring a flow rate of the air flow flowing in the flow path 132. The flow path 132 includes: a guide path 132a for guiding the air from an air inlet 113; and a blower installation chamber 132b formed such that a flow path cross-section area increases from a downstream end of the guide path 132a. The differential pressure sensor 164 includes: a first measurement port 164a installed in the guide path 132a; and a second measurement port 164b installed in the blower installation chamber 132b. The flow rate of the air flow is measured on the basis of the pressure difference between the two points of a position of the first measurement port 164a and a position of the second measurement port 164b.SELECTED DRAWING: Figure 6A

The invention relates to the technical field of intelligent monitoring, in particular to a medical risk monitoring method based on an artificial intelligence model, and the method comprises the steps: collecting thoracic impedance, tissue oxygenation spectrum, microcirculation pressure, respiratory flow and body surface vibration in real time; judging tissue oxygenation abnormity, and generating a suspected risk result; screening an abnormal candidate parameter group based on the risk result and a historical stable sample; candidate parameter fluctuation, frequency band energy, synchronism and pressure offset are calculated, and the risk type is judged; adjusting an oxygen supply threshold, an impedance tolerance and a fluctuation range according to the risk type; and correcting a fluctuation range in combination with risk time distribution and parameter correlation or generating an intervention prompt in combination with an artificial intelligence model. The multi-source physiological parameter fusion analysis model based on artificial intelligence is constructed, and the problems that due to single static data analysis and lack of multi-parameter collaborative judgment, tiny abnormal fluctuation of a postoperative patient cannot be captured in time, and risk events are lagged to find are effectively solved.

The invention relates to patient attachment detection in a respiratory flow therapy system. Systems and methods may determine whether a patient is attached to a breathing device, such as via a patient interface, from a flow parameter signal in the time domain. In addition, these processes may classify patient attachment status into one of four categories: already detached, being attached, already attached, or being detached. The system may include a non-sealed patient interface, such as a nasal cannula in nasal high flow therapy or any other patient interface. Data for the patient to use the respiratory system may provide a long-term trend of treatment compliance and use information and / or progression of respiratory function and / or other physiological functions of the patient.

Audio signals, collected with equipment commonly available to individuals (e.g., a mobile device), can be used to analyze a patient's breathing. An audio signal associated with the patient's breathing for a time period can be detected with the mobile device and used to approximate the patient's respiratory flow for the time period. For example, the audio signal can be analyzed by determining a representation of an audio frequency of the audio signal, splitting the audio frequency of the audio signal into distinct time steps, determining points comprising a weighted mean frequency at each time step, applying a frequency-to-flow rate linear transformation at each time step to approximate the respiratory flow versus time, and plotting a graphical representation of the respiratory flow versus time. The respiratory flow for the time period can be tagged with a factor related to the patient and saved in a database for future analysis.

The invention relates to a breathing machine self-adaptive ventilation pressure control method and a breathing machine. The method comprises the following steps: acquiring a target respiratory flow signal of a current respiratory cycle in a current ventilation treatment stage; predicting and generating expected inspiration time and expected tidal volume of the next respiratory cycle based on the target respiratory flow signal; on the basis of the expected inspiration time, the expected pressure gradient during ventilation in the next respiratory cycle is configured, and on the basis of the expected tidal volume, expected pressure support during ventilation in the next respiratory cycle is configured; based on the expected pressure gradient and the expected pressure support, the ventilation pressure of the next respiratory cycle is configured, so that the pressure gradient during ventilation of the next respiratory cycle is matched with the expected pressure gradient, and the pressure support during ventilation of the next respiratory cycle is matched with the expected pressure support. Self-adaptive ventilation pressure control can be effectively achieved, and ventilation convenience, man-machine synchronism, compliance and treatment effectiveness of a user are improved.