The present invention provides a connector for push-connecting a cartridge containing vaporizable material, such as a 510-cartridge, to a power-supplying device. The connector includes a housing containing electrical contacts, a cartridge-receiving end for receiving a conductive end of the cartridge, and a flexible, preferably silicone, ring connected to the cartridge receiving end of the housing for retaining the conductive end of the cartridge inserted therethrough against the electrical contacts. The ring is preferably part of a silicon boot that houses the housing.

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.

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.

Method and apparatus obtain information about a patient and/or a respiratory therapy system that is configured to deliver respiratory therapy to the patient. The respiratory therapy system may include a flow generator configured to generate a supply of pressurized air along an air circuit to a patient interface. A sound signal representing a sound in the air circuit may be processed to obtain cepstrum data. A time series of delay estimates based on acoustic signatures of the cepstrum data may be generated. Each acoustic signature may represent a reflection of sound from a patient interface along the air circuit. Variation in the time series of delay estimates may be analysed. One or more output indicators based on the variation may be generated. The one or more output indicators may concern patient and/or system status.

A system for monitoring the interaction of a mask with a patient's face in real-time during a pressure therapy treatment session and providing recommendations for improving such interaction. A sensing arrangement obtains real-time relative position data regarding the relative position of the mask relative to face of the patient and a computing arrangement in communication with the sensing arrangement for receiving the real-time relative position data from the sensing arrangement. The computing arrangement includes a computational model that uses the real-time relative position data to determine information regarding the interaction between the mask and the face of the patient, and an interpretation and analysis unit that: relates the information regarding the interaction between the mask and the face of the patient to functional parameters, determines actionable information and/or insights to improve the interaction, and provides as output the actionable information and/or insights.

A method of controlling a medical device is disclosed for delivering respiratory therapy to a user to treat sleep-disordered breathing, for instance obstructive sleep apnea, Cheyne-Stokes respiration etc. by estimating the user's CO2 percentage or concentration from a dynamic lung model driven by an observed respiration signal. The estimated user's CO2 percentage or concentration can be used to predict breathing events, such as hypopnea and apnea. The predictive capacity can be used for adjusting the respiratory therapy as required or for applying a ramp cycle therapy, in an attempt to reduce the prevalence and adverse effects of the breathing events. In other examples a variable ventilation therapy is provided in which pressure is supplied between first and second pressures, with the pressure being increased over more than one breath, and then dropped relatively rapidly, for example during expiration of a single breath.

A method of controlling a medical device is disclosed for delivering respiratory therapy to a user to treat sleep-disordered breathing, for instance obstructive sleep apnea, Cheyne-Stokes respiration etc. by estimating the user's CO2 percentage or concentration from a dynamic lung model driven by an observed respiration signal. The estimated user's CO2 percentage or concentration can be used to predict breathing events, such as hypopnea and apnea. The predictive capacity can be used for adjusting the respiratory therapy as required or for applying a ramp cycle therapy, in an attempt to reduce the prevalence and adverse effects of the breathing events. In other examples a variable ventilation therapy is provided in which pressure is supplied between first and second pressures, with the pressure being increased over more than one breath, and then dropped relatively rapidly, for example during expiration of a single breath.

Systems and methods for novel ventilation that allows the patient to trigger or initiate the delivery of a breath are provided. Further, systems and methods for triggering ventilation based on signal distortion of a monitored patient parameter are provided.