A system includes a respiratory therapy device, a sensor, and a control system. The respiratory therapy device supplies pressurized air to an airway of a user during a plurality of sleep sessions. The sensor generates data including current data that is associated with a current sleep session of the plurality of sleep sessions and historical data that is associated with one or more prior sleep sessions of the plurality of sleep sessions. The control system includes one or more processors configured to execute machine-readable instructions to: analyze the historical data to determine a behavior pattern associated with the user; analyze the current data to determine a condition of the user interface in relation to the user; and generate an alarm based at least in part on the behavior pattern associated with the user and the condition of the user interface in relation to the user.

A wearable device has a flexible and extendable body configured to encircle a portion of a body of a user, an electronics module with a concave space between two ends, each end attachable to the flexible and extendable body with a flexible retention mount to allow rotation of the flexible and extendable body relative to the electronics module and to transfer tension force from the flexible and extendable body to the electronics module, and a bio-signal sensor disposed on the flexible and extendable body to contact at least part of the body of the user and to receive bio-signals from the user.

The present invention relates to methods and devices to consolidate memory and/or cognitive functions by monitoring brain rhythms and delivering a stimulus at an appropriate stage of sleep cycle.

Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

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.

Methods, systems and apparatus for inducing and verifying the level of neural entrainment at any target frequency, through a combination of biofeedback mechanisms, data analysis, and modulation of synergistic combinations of adaptable stimuli.

Methods, systems and apparatus for inducing and verifying the level of neural entrainment at any target frequency, through a combination of biofeedback mechanisms, data analysis, and modulation of synergistic combinations of adaptable stimuli.

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.

An information processing apparatus includes a storage that stores analysis method information indicating an analysis method of brain waves of a user and notification method information indicating a method for notifying a state of brain waves in association with each of a plurality of user states indicating a symptom or ability of the user, a selection part that selects an improvement target state that is a user state that is to be improved by a target user, an acquisition part that acquires brain wave information of the target user, a state identification part that identifies a state of brain waves indicated by the brain wave information according to an analysis method associated with the selected improvement target state, and a notification part that notifies the target user of the state of the brain waves on the basis of the notification method associated with the selected improvement target state.