JP-2023517062-A5 -

Dates

Publication Date

20230525

Application Date

20210305

Description

Figure 1 is a functional block diagram of a system for determining one or more sleep-related parameters in a sleep session, based on a partial implementation of the present disclosure.Figure 2 is a perspective view of at least a portion of the system of Figure 1, a user, and a bed partner, based on a partial implementation of the present disclosure.Figure 3A shows the respiratory waveform of an individual during sleep, based on a partial implementation of this disclosure.Figure 3B shows selected polysomnography channels (pulse oximetry, flow rate, thoracic movement, and abdominal movement) of an individual in non-REM sleep with normal breathing for approximately 90 seconds, based on a partial implementation of the present disclosure.Figure 3C shows a polysomnography of an individual before respiratory therapy, based on a partial implementation of this disclosure.Figure 3D shows flow data when an individual is experiencing a series of total obstructive apnea, based on a partial implementation of this disclosure.Figure 4A shows flow rate data related to the user of a respiratory therapy device, based on a partial implementation of this disclosure.Figure 4B shows pressure data related to the user of a respiratory therapy device, based on a partial implementation of this disclosure.Figure 4C shows pressure data related to a user of a respiratory therapy device having an expiratory pressure reduction module, based on a partial implementation of the present disclosure.Figure 5A shows Cartesian coordinates plotting the average device pressure and the average total flow rate, expressed in liters/minute, for a partial implementation of the present disclosure.Figure 5B shows a characteristic curve fitted to the Cartesian coordinates plotted in Figure 5A, based on a partial implementation of the present disclosure.Figure 6 shows the intentional leak characteristic curve of a user-related respiratory therapy device based on a partial implementation of this disclosure.Figure 7 is a flowchart illustrating a method for detecting the intentional leak characteristic curve of a respiratory therapy device, based on a partial implementation of this disclosure.Figure 8 shows a flowchart illustrating a method for determining the intentional leak characteristic curve of a respiratory therapy device, based on a partial implementation of this disclosure.Figure 9 shows flow rate ("Q") and flow-volume ("V") data during respiration under respiratory therapy, based on a partial implementation of this disclosure.Figure 10 shows flow rate and flow-volume data during two breaths under respiratory therapy, based on a partial implementation of this disclosure.Figure 11 shows an intentional leak characteristic curve fitted to a scatter plot of pressure value ("P"; cmH₂O ) versus flow rate value ("Q"; liters per 10 seconds) based on a partial implementation of the disclosure.Figure 12 shows four types of impedances (Z1, Z2, Z3, and Z4) that affect the intentional leakage characteristic algorithm in some implementations of this disclosure. The flow sensor 134 outputs flow data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. An example of a flow sensor (e.g., flow sensor 134) is described in International Publication WO2012/012835, which is incorporated herein by reference in its entirety. In some implementations, the flow sensor 134 is used to determine the airflow from the respiratory therapy device 122, the airflow through the conduit 126, the airflow through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled to or integrated with the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be, for example, a rotary flow meter (e.g., Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot-wire sensor, a vortex sensor, a membrane sensor, or any combination thereof. In some implementations, the flow sensor 134 is configured to measure airflow (e.g., intentional "leaks"), unintentional leaks (e.g., mouth leaks and/or mask leaks), patient flow (e.g., air inflow and outflow from the lungs), or a combination thereof. In some implementations, the flow data can be analyzed to determine the user's cardiogenic oscillations. In one example, a pressure sensor 132 can be used to determine the user's blood pressure. In step 820, a first time associated with the user's first breath and a second time associated with the user's second breath are identified. In some implementations, the first and second times can be identified by analyzing a plurality of flow -capacity values corresponding to at least a subset of a plurality of flow values . In some implementations, the plurality of corresponding flow-capacity values are determined by taking the time integral of at least a subset of the plurality of flow values . An exemplary method for identifying the