Abstract: A multi-night titration (MNT) process to find an optimal single therapeutic pressure of a CPAP device. This single therapeutic pressure can then be used on an on-going basis by the patient after the titration period. The MNT process differs from current auto adjusting processes used for titration (or ongoing use) in that the MNT process does not respond locally by adjusting pressures to individual events. With existing devices, the continuous adjustment of supplied air pressure always responds to one or a small number of events and thus fails to compensate for a patient's adaptation thereto, resulting in the supply of a less than optimal therapeutic pressure to the patient. While auto adjusting processes often capture and respond well to short-term and transient conditions, the MNT process of the current disclosure seeks to capture long term trends and find the most suitable average single pressure for a patient.

Abstract: 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.

Abstract: Described is a system including an air pressure supply arrangement, a sensor and a titration device. The air pressure supply arrangement provides air pressure to a patient's airways. The sensor detects input data corresponding to a patient's breathing patterns of a plurality of breaths. The titration device receives and analyzes the input data to determine existence of breathing disorder and corresponding characteristics. The titration device generates output data for adjusting the air pressure supplied to the patient as a function of an index of abnormal respiratory events included in the input data.

Abstract: 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.

Abstract: A system, apparatus and methods are provided for supplying gases to a user. The supply includes a sub-therapeutic mode and a pressure support mode for delivering therapy to a user. A flow diversion device or valve switches from a first mode corresponding with the sub-therapeutic mode of the system to a second mode corresponding with the pressure support mode of the system. In the first mode, the valve opens a larger flow path between the interior of the user interface and ambient air than in the second mode.

Abstract: A system and method for providing ventilatory support to a patient suffering from sleep disordered breathing (SDB), in particular, non-obstructive SDB, such as Cheyne Stokes respiration and hypoventilation, including obesity hypoventilation syndrome. The system and method monitor obtain data corresponding to a breathing pattern of a patient and determine when the patient experiences oscillating breathing or low spontaneous ventilation. In particular, the system and method determine when the patient's real-time breathing patterns fall below a predetermined baseline threshold, and subsequently initiate a short series of mechanical breaths to be delivered to the patient.

Abstract: A multi-night titration (MNT) process to find an optimal single therapeutic pressure of a CPAP device. This single therapeutic pressure can then be used on an on-going basis by the patient after the titration period. The MNT process differs from current auto adjusting processes used for titration (or ongoing use) in that the MNT process does not respond locally by adjusting pressures to individual events. With existing devices, the continuous adjustment of supplied air pressure always responds to one or a small number of events and thus fails to compensate for a patient's adaptation thereto, resulting in the supply of a less than optimal therapeutic pressure to the patient. While auto adjusting processes often capture and respond well to short-term and transient conditions, the MNT process of the current disclosure seeks to capture long term trends and find the most suitable average single pressure for a patient.

Abstract: A multi-night titration (MNT) process to find an optimal single therapeutic pressure of a CPAP device. This single therapeutic pressure can then be used on an on-going basis by the patient after the titration period. The MNT process differs from current auto adjusting processes used for titration (or ongoing use) in that the MNT process does not respond locally by adjusting pressures to individual events. With existing devices, the continuous adjustment of supplied air pressure always responds to one or a small number of events and thus fails to compensate for a patient's adaptation thereto, resulting in the supply of a less than optimal therapeutic pressure to the patient. While auto adjusting processes often capture and respond well to short-term and transient conditions, the MNT process of the current disclosure seeks to capture long term trends and find the most suitable average single pressure for a patient.

Abstract: Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

Abstract: A multi-night titration (MNT) process to find an optimal single therapeutic pressure of a CPAP device. This single therapeutic pressure can then be used on an on-going basis by the patient after the titration period. The MNT process differs from current auto adjusting processes used for titration (or ongoing use) in that the MNT process does not respond locally by adjusting pressures to individual events. With existing devices, the continuous adjustment of supplied air pressure always responds to one or a small number of events and thus fails to compensate for a patient's adaptation thereto, resulting in the supply of a less than optimal therapeutic pressure to the patient. While auto adjusting processes often capture and respond well to short-term and transient conditions, the MNT process of the current disclosure seeks to capture long term trends and find the most suitable average single pressure for a patient.

Abstract: 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.

Abstract: Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

Abstract: A respiratory therapy system is configured to supply breathing gases to a patient and comprises a controller configured to control the pressure of breathable gas delivered to the patient. The system can control the flow of breathable gas to the patient taking into account whether or not a breath is detected, and whether or not the patient is awake or asleep. The system is configured to: detect when the patient is inhaling and to control the flow generator to deliver breathable gas at an inspiration pressure (IPAP), detect when the patient is exhaling and to control the flow generator to deliver breathable gas at an expiration pressure (EPAP), the EPAP being lower than the IPAP, and detect when the patient is at least one of awake and/or asleep.

Abstract: A multi-night titration (MNT) process to find an optimal single therapeutic pressure of a CPAP device. This single therapeutic pressure can then be used on an on-going basis by the patient after the titration period. The MNT process differs from current auto adjusting processes used for titration (or ongoing use) in that the MNT process does not respond locally by adjusting pressures to individual events. With existing devices, the continuous adjustment of supplied air pressure always responds to one or a small number of events and thus fails to compensate for a patient's adaptation thereto, resulting in the supply of a less than optimal therapeutic pressure to the patient. While auto adjusting processes often capture and respond well to short-term and transient conditions, the MNT process of the current disclosure seeks to capture long term trends and find the most suitable average single pressure for a patient.

Abstract: A system, apparatus and methods are provided for supplying gases to a user. The supply includes a sub-therapeutic mode and a pressure support mode for delivering therapy to a user. A flow diversion device or valve switches from a first mode corresponding with the sub-therapeutic mode of the system to a second mode corresponding with the pressure support mode of the system. In the first mode, the valve opens a larger flow path between the interior of the user interface and ambient air than in the second mode.

Abstract: A system, apparatus and methods are provided for supplying gases to a user. The supply includes a sub-therapeutic mode and a pressure support mode for delivering therapy to a user. A flow diversion device or valve switches from a first mode corresponding with the sub-therapeutic mode of the system to a second mode corresponding with the pressure support mode of the system. In the first mode, the valve opens a larger flow path between the interior of the user interface and ambient air than in the second mode.

Abstract: A system, apparatus and methods are provided for supplying gases to a user. The supply includes a sub-therapeutic mode and a pressure support mode for delivering therapy to a user. A flow diversion device or valve switches from a first mode corresponding with the sub-therapeutic mode of the system to a second mode corresponding with the pressure support mode of the system. In the first mode, the valve opens a larger flow path between the interior of the user interface and ambient air than in the second mode.

Abstract: A system comprises a respiratory delivery arrangement adapted to cover at least one respiratory orifice of a patient. The system also comprises a first conduit having a first end and a second end, the second end connected to the respiratory delivery arrangement. A positive pressure is provided to the respiratory orifice via the first conduit and a second conduit having a third end and a fourth end, the fourth end connected to the respiratory delivery arrangement. An exhaled gas is extracted from the respiratory orifice by one or both of a valve configured to redirect flow through the respiratory delivery arrangement and a venturi opening.

Abstract: Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

Abstract: Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.