Abstract: This disclosure describes systems and methods for providing adaptive base flow scheduling during ventilation of a patient to optimize patient-machine synchrony and accuracy of estimated exhaled as well as inhaled tidal volumes. Further, this disclosure describes systems and methods for providing adaptive inspiratory trigger threshold scheduling during the adaptive base flow scheduling. Further still, this disclosure describes systems and methods for determining an estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the estimated leak flow. Moreover, this disclosure describes systems and methods for determining a change in the estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the change in the estimated leak flow.

Abstract: This disclosure describes systems and methods for controlling pressure and/or flow during exhalation. The disclosure describes novel exhalation modes for ventilating a patient.

Abstract: This disclosure describes systems and methods for providing novel adaptive base flow scheduling during ventilation of a patient to optimize the accuracy of estimated exhaled tidal volume. Further, this disclosure describes systems and methods for providing novel adaptive inspiratory trigger threshold scheduling during the novel adaptive base flow scheduling.

Abstract: This disclosure describes systems and methods for controlling pressure and/or flow during exhalation. The disclosure describes novel exhalation modes for ventilating a patient.

Abstract: This disclosure describes systems and methods for controlling pressure and/or flow during exhalation. The disclosure describes novel exhalation modes for ventilating a patient.

Abstract: Various embodiments of the present disclosure provide systems, methods and devices for respiratory support. As one example, a method for respiratory support is described that includes providing a measured pressure, and calculating a net flow based on at least one measured inlet flow and measured outlet flow. A relationship between a first value related to the measured pressure, a second value related to the measured net flow and a third value related to patient effort is used to provide a prediction of patient effort. An interim value is updated based at least in part on the prediction of the patient effort, and used to help compute a patient effort. A ventilation cycle is initiated using the computed patient effort.

Abstract: This disclosure describes systems and methods for providing adaptive base flow scheduling during ventilation of a patient to optimize patient-machine synchrony and accuracy of estimated exhaled as well as inhaled tidal volumes. Further, this disclosure describes systems and methods for providing adaptive inspiratory trigger threshold scheduling during the adaptive base flow scheduling. Further still, this disclosure describes systems and methods for determining an estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the estimated leak flow. Moreover, this disclosure describes systems and methods for determining a change in the estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the change in the estimated leak flow.

Abstract: This disclosure describes systems and methods for providing adaptive base flow scheduling during ventilation of a patient to optimize patient-machine synchrony and accuracy of estimated exhaled as well as inhaled tidal volumes. Further, this disclosure describes systems and methods for providing adaptive inspiratory trigger threshold scheduling during the adaptive base flow scheduling. Further still, this disclosure describes systems and methods for determining an estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the estimated leak flow. Moreover, this disclosure describes systems and methods for determining a change in the estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the change in the estimated leak flow.

Abstract: This disclosure describes systems and methods for providing novel adaptive base flow scheduling during ventilation of a patient to optimize the accuracy of estimated exhaled tidal volume. Further, this disclosure describes systems and methods for providing novel adaptive inspiratory trigger threshold scheduling during the novel adaptive base flow scheduling.

Abstract: This disclosure describes systems and methods for providing novel adaptive base flow scheduling during ventilation of a patient to optimize the accuracy of estimated exhaled tidal volume. Further, this disclosure describes systems and methods for providing novel adaptive inspiratory trigger threshold scheduling during the novel adaptive base flow scheduling.

Abstract: Various embodiments of the present disclosure provide systems, methods and devices for respiratory support. As one example, a method for respiratory support is described that includes providing a measured pressure, and calculating a net flow based on at least one measured inlet flow and measured outlet flow. A relationship between a first value related to the measured pressure, a second value related to the measured net flow and a third value related to patient effort is used to provide a prediction of patient effort. An interim value is updated based at least in part on the prediction of the patient effort, and used to help compute a patient effort. A ventilation cycle is initiated using the computed patient effort.

Abstract: Various embodiments of the present disclosure provide systems, methods and devices for respiratory support. As one example, a method for respiratory support is described that includes measuring a pressure, providing a measured pressure, measuring an inlet flow and an outlet flow, and providing a measured net flow. A relationship between a first value related to the measured pressure, a second value related to the measured net flow and a third value related to patient effort is used to provide a prediction of patient effort. An interim value is updated based at least in part on the prediction of the patient effort.

Abstract: Various embodiments of the present disclosure provide systems, methods and devices for respiratory support. As one example, a method for respiratory support is described that includes providing a measured pressure, and calculating a net flow based on at least one measured inlet flow and measured outlet flow. A relationship between a first value related to the measured pressure, a second value related to the measured net flow and a third value related to patient effort is used to provide a prediction of patient effort. An interim value is updated based at least in part on the prediction of the patient effort, and used to help compute a patient effort. A ventilation cycle is initiated using the computed patient effort.

Abstract: Various embodiments of the present disclosure provide systems, methods and devices for respiratory support. As one example, a method for respiratory support is described that includes measuring a pressure, providing a measured pressure, measuring an inlet flow and an outlet flow, and providing a measured net flow. A relationship between a first value related to the measured pressure, a second value related to the measured net flow and a third value related to patient effort is used to provide a prediction of patient effort. An interim value is updated based at least in part on the prediction of the patient effort.

Abstract: This disclosure describes systems and methods for controlling pressure and/or flow during exhalation. The disclosure describes novel exhalation modes for ventilating a patient.

Abstract: The systems and methods include controlling pressure and/or flow during exhalation. The systems and methods include novel exhalation for ventilating a patient. The systems and methods decrease the amount of time it takes the patient to passively expire an inspired volume of gas. For example, the systems and method may determine one or more pressure profiles based on one or more criterion and may select and utilize the pressure profile that provides for the fastest rate of lung emptying.

Abstract: This disclosure describes systems and methods for providing adaptive base flow scheduling during ventilation of a patient to optimize patient-machine synchrony and accuracy of estimated exhaled as well as inhaled tidal volumes. Further, this disclosure describes systems and methods for providing adaptive inspiratory trigger threshold scheduling during the adaptive base flow scheduling. Further still, this disclosure describes systems and methods for determining an estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the estimated leak flow. Moreover, this disclosure describes systems and methods for determining a change in the estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the change in the estimated leak flow.

Abstract: In laser-produced plasma (LPP) extreme ultraviolet (EUV) systems, pressure waves and other impulsive disturbances originating from plasma bubble emitting EUV light are created which affect flight of incoming droplets. These disturbances slow the migration of subsequent droplets to the plasma bubble. Because the incoming droplets are slowed, the laser beam does not directly hit the droplet at a primary focus point. This causes the resulting level of EUV light generated to be lower than expected and is manifested as a periodic oscillation in the LPP EUV system. An iterative learning controller (ILC) is used to reduce the periodic oscillations when operating the LPP EUV system in a burst mode or a continuous mode. The ILC, on the rising edge of each burst, uses an error signal collected during a completed burst and the input control signal from the completed burst to update the input control signal for a next burst.

Abstract: In laser-produced plasma (LPP) extreme ultraviolet (EUV) systems, pressure waves and other impulsive disturbances originating from plasma bubble emitting EUV light are created which affect flight of incoming droplets. These disturbances slow the migration of subsequent droplets to the plasma bubble. Because the incoming droplets are slowed, the laser beam does not directly hit the droplet at a primary focus point. This causes the resulting level of EUV light generated to be lower than expected and is manifested as a periodic oscillation in the LPP EUV system. An iterative learning controller (ILC) is used to reduce the periodic oscillations when operating the LPP EUV system in a burst mode or a continuous mode. The ILC, on the rising edge of each burst, uses an error signal collected during a completed burst and the input control signal from the completed burst to update the input control signal for a next burst.

Abstract: This disclosure describes systems and methods for providing adaptive base flow scheduling during ventilation of a patient to optimize patient-machine synchrony and accuracy of estimated exhaled as well as inhaled tidal volumes. Further, this disclosure describes systems and methods for providing adaptive inspiratory trigger threshold scheduling during the adaptive base flow scheduling. Further still, this disclosure describes systems and methods for determining an estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the estimated leak flow. Moreover, this disclosure describes systems and methods for determining a change in the estimated leak flow and adjusting the adaptive base flow scheduling and the adaptive inspiratory trigger threshold scheduling based on the change in the estimated leak flow.