12 results about "Airway pressures" patented technology

This invention relates to the field of medical device technology and provides a method, system, and medical device for measuring peak cough flow rate. The method includes real-time acquisition of inspiratory and expiratory flow rate signals and airway pressure signals; monitoring changes in the expiratory flow rate signal to identify burst cough events and lock a cough analysis window; calculating the difference in inspiratory and expiratory flow rates within the cough analysis window to obtain a baseline cough flow rate sequence; calculating the airway pressure signal within the cough analysis window using a system leakage factor to obtain a leakage flow rate compensation sequence; determining coefficients based on the ratio of tubing and patient airway resistance parameters and multiplying the expiratory flow rate signals to obtain a tubing resistance compensation sequence; superimposing the above sequences to obtain a corrected flow rate sequence; and extracting extreme value features and evaluating the effectiveness of the corrected flow rate sequence generated after a preset number of iterations to determine the peak cough flow rate value. This invention solves the problems of inaccurate and inefficient measurement results in existing peak cough flow rate measurements.

This invention relates to the field of high-frequency ventilator technology and discloses a high-frequency ventilator operating method based on flow control. The high-frequency ventilator sequentially outputs multiple average airway flow rates of the same or different magnitudes and with the same or different durations according to a set periodic pattern. The airway flow rate output is stable during the expiratory and inspiratory phases, and the average airway flow rate can be output at the same or different magnitudes during different inspiratory and expiratory phases. The output gas flow rate of the high-frequency ventilator can be independently adjusted periodically between different ventilation flow rate levels within different durations. In high-frequency ventilation, the flow control-based high-frequency ventilation method can precisely control the gas flow rate and time. Flow control ventilation can better control changes in airway pressure. By setting appropriate flow rates and times, the delivery volume of the device can be accurately estimated to reduce airway pressure and risks, and lower the possibility of barotrauma.

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 present application belongs to the technical field of medical general equipment, and relates to a ventilator phase switching method and device based on respiratory effort recognition, a ventilation equipment and a storage medium. The method comprises the following steps: monitoring a patient's proximal ventilation flow and airway pressure; according to a set logical enabling condition, avoiding an undesirable recognition period; the undesirable recognition period is an initial unstable stage after the ventilator inflation or breathing starts; outside the undesirable recognition period, based on the waveform trajectory change characteristics of the ventilation flow and the airway pressure, the time when the patient's inspiratory effort ends, the expiratory effort starts or the inspiratory effort starts is recognized; and according to the recognition result, the ventilator is controlled to switch between the inflation phase and the exhalation phase. The present application can improve the sensitivity and accuracy of respiratory effort recognition, improve the time synchronization of ventilation triggering and switching, reduce the phenomenon of man-machine asynchronization, and improve the comfort and effectiveness of mechanical ventilation support.

This invention discloses a method for estimating airway resistance and compliance. The method includes: acquiring multiple sets of sampled data and acquiring a preset discrete nonlinear airway resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a single sampling time. Based on the multiple sets of sampled data and a pre-established parameter update model, preset parameters in the discrete nonlinear airway resistance model are solved to obtain parameter values. Based on the parameter values ​​and the substitution relationships between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, linear airway resistance, nonlinear airway resistance, and lung compliance are solved. The linear airway resistance and lung compliance are used to effectively control the ventilation modes of anesthesia machines and ventilators to suit various usage situations for different users. Furthermore, devices, equipment, and media for estimating airway resistance and compliance are also proposed.

This invention provides a non-invasive respiratory support airway control method and system integrating intrapulmonary oscillation expansion and nitric oxide delivery. The invention is executed on a system including a mask interface, inspiratory and expiratory branches on the same airway platform, a controller, an airway output control unit, a nitric oxide source, a mixing unit, and pressure, flow rate, and concentration monitoring units. It acquires airway pressure, airway flow rate, and nitric oxide, nitrogen dioxide, and oxygen concentrations to form a set of airway monitoring indicators. Based on the indicator set and ventilation and oxygenation indicators, it determines the operating strategy and generates target pressure and target flow rate. When oscillation is involved, it generates and outputs oscillation waveform parameters. It determines the inspiratory phase window and injects nitric oxide at a set delivery concentration within the window. It corrects the delivery concentration with nitric oxide concentration and interlocks an alarm when nitrogen dioxide or oxygen concentration exceeds limits. During strategy switching, it freezes the injection and purges the system, resuming injection after the access conditions are met.

This invention discloses a method and application for monitoring and early warning of ventilators during emergency transport, comprising: S1: collecting ambulance bump data and airway pressure time-series data and preprocessing them; S2: extracting bump interference features and airway pressure features respectively; calculating airway pressure features after interference suppression; calculating pure airway pressure features; S3: calculating bump time-series features, and then completing the classification through a bump intensity differentiation mechanism to obtain bump patterns; calculating cross-modal fusion features to generate initial calibration parameters and final calibration parameters; S4: calculating calibrated airway pressure time-series data; and then combining the pure airway pressure features to calculate ventilation trend abnormality markers and the proportion of numerical abnormality, and generating graded early warning levels; S5: graded early warning and visualization display. This invention can solve the problem of poor monitoring and early warning accuracy in traditional ventilator monitoring and early warning methods due to insufficient consideration of vehicle bumps during transport.

A patient ventilation interface has a throat body defining a venturi throat open to ambient air, a nasal pillow disposed about the venturi throat to define a plenum chamber between the venturi throat and the nasal pillow, a nozzle arranged to output ventilation gas to the venturi throat, and a pressure sensing tube having a pressure sensing port positioned in fluid communication with the plenum chamber. The nasal pillow is an integral part of the throat body. An expected error in a sensed patient airway pressure P sense and a nozzle flow rate V' n of the nozzle can be corrected by applying a correction factor P delta indexed to the sensed patient airway pressure P sense . Delivery of ventilation gas output by the nozzle can be controlled in response to the corrected patient airway pressure.

The embodiment of the application discloses a respiration recognition method, which is applied to a ventilation device. The method comprises the following steps: acquiring airway pressure and gas flow rate in the process of mechanical ventilation; determining cross-correlation data corresponding to the airway pressure and the gas flow rate according to the airway pressure and the gas flow rate; and recognizing the respiration state of a patient according to the change of the cross-correlation data.

Device for guiding respiratory gas, characterized in that means for ventilation and means for coughing support are integrated simultaneously and, in addition to a ventilation pressure intended for ventilation, an increased airway pressure is generated at least temporarily, wherein air is stacked in the lungs over several breaths and wherein the coughing aid (10) is a controlled negative pressure generator.

The application discloses a kind of infant anesthesia respiratory depression early warning system and method based on multi-modal fusion, it is related to data processing technical field, including: collection respiratory impedance signal, electrocardiogram signal, end-tidal carbon dioxide waveform, tidal volume, airway pressure, anesthetic drug real-time concentration and operation phase marker;According to the interval of continuous R wave in electrocardiogram signal, calculate instantaneous heart rate, construct the comb notch filter with instantaneous heart rate as base frequency, and filter respiratory impedance signal to obtain artifact-free respiratory signal;Based on artifact-free respiratory signal, tidal volume and airway pressure, calculate respiratory rhythm characteristics, generate rising branch slope, plateau oscillation value and alpha angle value by end-tidal carbon dioxide waveform;According to the stimulation intensity coefficient mapped by anesthetic drug real-time concentration and operation phase marker, calculate drug-stimulation balance index;The application realizes the early perception and dynamic early warning of respiratory depression risk during infant anesthesia.

A cardiopulmonary resuscitation airway pressure automatic feedback ventilation method and a ventilator. The method includes: obtaining the current airway pressure signal when compressing the heart of the target individual at a preset sampling frequency; dynamically correcting the current airway pressure signal based on body parameters of the target individual to obtain the target airway pressure signal; continuously analyzing the target airway pressure signal to obtain the compression frequency, compression phase, and signal information for the target individual; training a machine learning model with preset compression parameters and ventilation rule parameters; inputting the compression frequency, compression phase, and signal information for the target individual, and mode setting parameters of the ventilation equipment, into the machine learning model to determine the ventilation probability and ventilation time window of the target airway pressure signal, and controlling the ventilation equipment to perform automatic ventilation on the target individual according to the ventilation time window.