A personalized parameter learning method, a sleep-aid device and a non-transitory computer readable medium are provided. The personalized parameter learning method for a sleep-aid device is provided. The personalized parameter learning method includes the following steps. A process device computes a measured sleep quality of a user after operating a sleep-aid device with an inputted parameter setting at least according to a subjective feedback from the user. The processing device generates a plurality of candidate parameter settings according to the measured sleep quality. The processing device generates a plurality of predicting sleep qualities corresponding the candidate parameter settings. The processing device obtains a recommending parameter setting by selecting one of the candidate parameter settings according to the predicting sleep qualities.

Provided is an exacerbation prediction device equipped with a respiration sensing means of continuously sensing respiration data of a patient, a calculation means of calculating stable respiration data that are respiration data during a condition in which a respiratory rate is lowered and stable for a certain period of time from the sensed continuous respiration data of the patient, and a prediction means of predicting occurrence of an acute exacerbation in the patient in accordance with the stable respiration data calculated during a certain period of time.

Systems slow breathing with positive pressure therapy. In embodiments, a current interim breathing rate target is set, and periodically magnitude of a variable pressure waveform scaled to the current interim breathing rate target is increased if breathing rate is greater than the interim rate target to lengthen breath duration. The magnitude of the pressure increase may be a function of the difference between the interim rate target and the breathing rate. The interim rate target may be reduced in response to slowing breathing rate. The waveform cycles, inhalation to exhalation, when airflow decreases to a cycle threshold. Different interim rate targets have different cycle threshold functions that allow easier cycling as the interim rate targets decrease. Similarly, the waveform triggers, exhalation to inhalation, when airflow increases to a trigger threshold. Different interim rate targets have different trigger threshold functions that allow easier triggering as the interim rate targets decrease.

A gas therapy system (1) has a flow line (3, 2), a coupler (6) to a gas source, and an aerosol generator (4) for aerosol delivery, and a patient interface such as a nasal interface (2). A controller (10) is configured to modulate gas flow and aerosol delivery in real time. The controller changes gas flow rate and dynamically reduces aerosol delivery during upper gas flow rates such as 60 LPM, and activates aerosol delivery during lower gas flow rates of for example 10 LPM. The control may also include sensors to detect breathing, so that there is a bias towards increased aerosol delivery during inhalation in addition to during lower level gas flow.

A method of determining a duration of safe apnoea. Information is obtained relating to a respiratory indicator, which can include information relating to a potential respiratory equilibrium, and a duration of safe apnoea is determined from the obtained information.

A system for assessing extubation includes a respiratory assistance device, an artificial intelligence platform, and a hospital information system. The respiratory assistance device is adapted to communicate with a trachea of a patient. The artificial intelligence platform includes a prediction module. A method for assessing extubation includes the following steps. Measured values of respiratory parameters of the patient are recorded by the respiratory assistance device. The recorded times and the measured values of the respiratory parameters corresponding to each of the recording times are transmitted to the artificial intelligence platform. The prediction module analyzes the measured values of respiratory parameters within a predetermined time period according to a prediction model to generate a prediction result. The prediction result is transmitted to the hospital information system and is recorded into a medical record of the patient. With such design, a reference for extubation assessment that is more accurate is provided.

An electronic vaporizer system includes an anesthetic sump containing anesthetic agent, a vaporizer unit that vaporizes the anesthetic agent from the sump and delivers the vaporized agent to a patient breathing circuit, and a gas sensor configured to measure end tidal concentration of the anesthetic agent and exhalation gasses from the patient. A control system is configured to receive the measured end tidal concentration of anesthetic agent and compare the measured end tidal concentration to a desired end tidal concentration to be maintained for the patient. The vaporizer unit is then automatically controlled to deliver an amount of vaporized agent to the patient based on the comparison.

Devices, systems, and methods for detecting a sealing condition between a patient interface and a patient, and adjusting the patient interface to maintain the patient interface in sealing contact with the patient. The patient interface may include a sealing structure to form a seal on the patient, and a positioning structure to secure the sealing structure to the patient. The patient interface may include a sensor coupled to the sealing structure. A processor determines the sealing condition between the sealing structure and the patient based on a signal from the sensor, and adjusts at least one of the sealing structure and the positioning structure to maintain the sealing structure in sealing contact with the patient. A prediction system predicts a leak between the sealing structure and the patient based on the sensor signal. A learning system learns how to fit the sealing structure to the patient to form a seal.

This disclosure describes systems and methods for providing a high pressure controlled proportional assist ventilation breath type during ventilation of a patient. The disclosure describes a novel breath type that reduces ventilator support (or a percent support setting) based on the occurrence of a predetermined number of high pressure alarms.

Process/unit for determining intrinsic breathing activity of a ventilated patient. The process/unit carries out a first ventilating operation, in which a ventilator parameter at a first setting. The process/unit generates a first set of signal values as a function of measured values, which were measured at the first setting. A first breathing activity value is derived using a predefined lung mechanical model and the first set of signal values. The process/unit calculates a value for the reliability that the first breathing activity value agrees with the corresponding actual breathing activity value. Depending on this reliability assessment, the process/unit checks whether a predefined triggering criterion is met. If this criterion is met, then the process/unit triggers a change step, in which the ventilator parameter is set at a second setting. It carries out an additional ventilating operation, in which the ventilator parameter is set at the second setting.