Described is an apparatus for oxygenation and/or CO2 clearance of a patient, comprising: a flow source or a connection for a flow source for providing a gas flow, a gas flow modulator, a controller to control the gas flow, wherein the controller is operable to: receive input relating to heart activity and/or trachea gas flow of the patient, and control the gas flow modulator to provide a varying gas flow with one or more oscillating components with a frequency or frequencies based on the heart activity and/or trachea flow of the patient.

The present invention relates to the art of automatic regulation of pulmonary devices for imitating, assisting and/or enforcing the breathing process by converting Bag-Valve-Mask (BVM) or a similar device to enhance both phases of breathing: inhalation and exhalation while applying a variable pressure during the breathing process. It also replaces a mechanical chest compression to the sternum area for automatic pneumatic compression, and it could be complimented with the use of a Tens unit, can be used for extended period of time with a high level of reliability, simplicity, efficacy and low cost. The unique filtration system's goal in this invention is safety of the treating patient as well as assisting personnel. This portable and light device is recommended to be used as a resuscitator for the patients with mild to extremely suppressed or without respiratory drive. The source of power can be electrical, battery operated, manual or a combination thereof.

A mechanical ventilation device comprising at least one electronic controller configured to receive imaging data and transpulmonary pressure data associated with a lung of a patient; perform deformable image registration of the inhalation image and the exhalation image to produce a relative compliance or elasticity map of the lungs; convert the relative compliance or elasticity map of the lungs to a quantitative compliance or elasticity map of the lungs based on the inhale transpulmonary pressure and the exhale transpulmonary pressure; and display the information relating to or derived from the quantitative compliance or elasticity map on a display device.

A method for determining that a patient interface component comprising a vent has been replaced between therapy sessions of treatment of sleep disordered breathing, the method comprising: acquiring or receiving first vent flow rate data representing one or more estimated first vent flow rates of gas through a first vent of a patient interface in use during a first therapy session; acquiring or receiving second vent flow rate data representing one or more estimated second vent flow rates of gas through a second vent of a patient interface in use during a second therapy session after the first therapy session; and identifying, by comparison of the second vent flow rate data to the first vent flow rate data, a difference in resistance to flow through the first vent than through the second vent indicating that the second vent is not the same vent as the first vent.

Systems and techniques for monitoring, predicting and/or alerting for short-term oxygen support needs of patients are presented. A system can include a data collection component that receives multimodal patient data for a patient having a respiratory condition in association with monitoring and treating the respiratory condition in real-time, the multimodal patient data comprising at least physiological data regarding physiological parameters tracked for the patient over a period of time, and current oxygen support data regarding a current oxygen support mechanism of the patient. The system can further include an oxygen support forecasting component that processes the multimodal patient data using an oxygen support forecasting model to generate an output forecast that indicates whether a change to the current oxygen support mechanism is recommended for the patient within a defined upcoming timeframe

Devices, systems and methods are provided for controlling the operation of a breathing assistance device for a user. The controller may include an input for receiving sensor data to measure at least one airflow parameter of the user's airflow; a memory unit that stores at least one machine learning model and at least one classifier or predictor; and a processor that is configured to perform measurements and to generate a control signal for adjusting the operation of the breathing assistance device for a current monitoring time period by: obtaining measured air pressure and/or airflow data and measured FOT data during a current monitoring time period; performing feature extraction on the measured data to obtain feature values that are used by the machine learning model employed by the at least one classifier or predictor to determine a property of the user; and adjusting the control signal based on the determined property.

The present disclosure describes a sleep staging system. The system comprises: one or more sensors configured to generate output signals conveying information related to breathing parameters of subject during a respiratory therapy session; and one or more physical computer processors configured by computer readable instructions to: determine, based on the output signals, one or more breathing features of individual breaths of the subject; determine a distribution of the one or more breathing features over a plurality of time windows, at least one of the time windows having a length of at least 60 seconds; determine sleep states of the subject by mapping the distribution of the breathing features to one or more sleep states using a sleep stage classifier model, the sleep stage classifier model configured to determine the sleep states; and provide feedback indicating the sleep states during the respiratory sleep session.

A ventilation adjustment method and a high-frequency ventilation system, which ensure stable and accurate oxygen concentration control within an oxygen concentration setting range, are disclosed. The ventilation adjustment method includes: determining a first gas flow rate control value and a second gas flow rate control value according to a target output flow rate and an oxygen concentration setting value; determining whether the first gas flow rate control value falls into a first dead zone range and whether the second gas flow rate control value falls into a second dead zone range; if the first gas flow rate control value falls into the first dead zone range, maintaining a first gas flow rate controller turned on in an expiratory phase; and if the second gas flow rate control value falls into the second dead zone range, maintaining a second gas flow rate controller turned on in the expiratory phase.

Ventilation system (having a ventilator and having a diagnostic device, wherein the ventilator comprises a ventilation unit for generating a respiratory gas flow for ventilation and a detection unit (for detecting a ventilation signal characteristic for the respiratory gas flow over time. The diagnostic device comprises a sensor unit for detecting a diagnostic signal over time. The synchronization unit is operationally connected to the detection unit and the sensor unit and is suitable and configured for studying a time curve of the ventilation signal and a time curve of the diagnostic signal respectively for a signal change caused by the same event and bringing the curve of the ventilation signal and the curve of the diagnostic signal into chronological correspondence so that the event occurs simultaneously in both signal curves.

A method includes receiving, from a first sensor, first physiological data associated with a first sleep session of a user. The method also includes receiving, from a sensor, second physiological data associated with a first sleep session of a user. The method also includes determining a first set of sleep-related parameters associated with the first sleep session of the user based at least in part on the first physiological data. The method also includes determining a second set of sleep-related parameters associated with the first sleep session of the user based at least in part on the second physiological data. The method also includes calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters.