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.

The present invention provides systems, methods, and articles for stress reduction and sleep promotion. A stress reduction and sleep promotion system includes at least one remote device, at least one body sensor, and at least one remote server. In other embodiments, the stress reduction and sleep promotion system includes machine learning.

The present invention provides systems, methods, and articles for stress reduction and sleep promotion. A stress reduction and sleep promotion system includes at least one remote device, at least one body sensor, and at least one remote server. In other embodiments, the stress reduction and sleep promotion system includes machine learning.

The present invention relates to an integrated sleep diagnosis and treatment device, and more particularly to an integrated apnea diagnosis and treatment device. The present invention additionally relates to method of sleep diagnosis and treatment.

The present invention relates to a method and a respiration device having a respiration unit for generating an airflow for the respiration and having a monitoring unit. The monitoring unit is used to detect a respiration parameter and to classify events in the respiration on the basis of monitoring of the respiration parameter. In this case, the monitoring unit is configured to carry out an event analysis to recognize an occurrence, which is characteristic for Cheyne-Stokes respiration, of chronologically successive events and for this purpose to ascertain the period length thereof and to compare them to one another and to register the presence of Cheyne-Stokes respiration when the compared period lengths each deviate by less than 40% from one another.

Various embodiments are described herein for a controller for controlling the operation of a breathing assistance device that provides breathing assistance to a user. The controller comprises a processor that generates a respiratory index value that is determined during a current monitoring time period to detect a respiratory failure, or predict the respiratory failure when at least one PSG signal is measured. The respiratory index value is compared to a threshold to determine if the control signal needs to be updated to reduce or eliminate respiratory failure that the user is currently experiencing or to prevent a predicted respiratory failure from occurring.

A relaxation mask includes: a main body that defines a pair of eye cavities; and a light diffuser. The light diffuser includes a first lens that is disposed within a first one of the eye cavities. A first ledge is disposed along a top edge of the first lens and extends outwardly therefrom. A first light emitting component is supported on the first ledge and is configured to fire downward into the first lens.

A relaxation mask includes: a main body that defines a pair of eye cavities; and a light diffuser. The light diffuser includes a first lens that is disposed within a first one of the eye cavities. A first ledge is disposed along a top edge of the first lens and extends outwardly therefrom. A first light emitting component is supported on the first ledge and is configured to fire downward into the first lens.