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.

Systems, methods, and devices for automatic generation of a stimulation therapy that mimics electrographic activity in the brain at natural seizure termination define a stimulation therapy to be generated by an implanted component of a medical device system and delivered to a subject through identifying data characterizing a patient's seizures, especially at termination. A machine learning model identifies the seizures or seizure types from which to establish a canonical seizure or seizure type, and an algorithm translates the canonical seizure or seizure type into data that can be used to characterize a stimulation therapy. The systems, methods, and devices, include those configured to deliver the stimulation therapy that emulates the canonical seizure or seizure type when the seizure is detected, with the aim of terminating the seizure sooner than it would terminate without intervention.

Systems and methods are provided for a multi-modal sleep system comprising a data processor for operating in a plurality of operating modes. The data processor may detect at least one sensor providing data to the data processor and determine a sensor type associated with each of the at least one sensor. The data processor may select a mode of operation based on the determined sensor type of the detected at least one sensor and of each of the at least one sensor. A first of the plurality of operating modes may be selected in response to determining that the at least one detected sensor includes a first sensor type or combination of sensor types. The data processor may be configured to receive data from the at least one detected sensor and process the received data according to the selected mode of operation to output a characterization of a user's sleep.

A method and system that is integrated in order to provide an automated control system for the user, which provides messaging to bedroom environmental control systems as a function of the status of the user's sleep state is disclosed herein. The system comprises a sleep monitoring sub-system and a bedroom environmental control sub-system. The sleep monitoring sub-system is configured to transmit the subject's sleep progression data to an interface for the bedroom environmental control system. The bedroom environmental control system is configured to modify a bedroom environment based on the subject's sleep progression data.

A biological signal analysis device includes: an acquiring unit configured to acquire biological signals of a measurement target; a trigger information acquiring unit configured to acquire, from a stimulator configured to apply stimuli to the measurement target, trigger information indicating times at which the stimuli are generated; and a signal processing unit configured to process the biological signals. The signal processing unit is configured to calculate biological information on the measurement target based on the biological signals, maintain only pieces of trigger information corresponding to times at which it is determined that biological signals of the measurement target are generated, from the calculated biological information, delete another piece of trigger information, and use an averaged waveform that is obtained by performing an averaging process on the biological signals that are generated in synchronization with the stimuli based on the pieces of remaining trigger information.

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.

A medical device for monitoring biological parameters through an Abreu Brain Thermal Tunnel (ABTT) is provided. By monitoring and analyzing the temperature of the ABTT, it is possible to diagnosis changes in a patient or subject under a variety of conditions, including predicting the course of medical conditions. Furthermore, since the ABTT is predictive, analysis of the ABTT may be used to control mechanisms for safety when an impending medical condition makes such operation hazardous.

A method includes receiving physiological data associated with a user during a sleep session. The method also includes determining a sleep-wake signal for the user during the sleep session based at least in part on the received physiological data. The method also includes determining one or more sleep-related parameters for the user during the sleep session based at least in part on the sleep-wake signal. The method also includes determining that the user experienced insomnia during the sleep session based at least in part on at least one of the one or more sleep-related parameters. The method also includes identifying a type for the insomnia experienced by the user based at least in part on the one or more sleep-related parameters.

The present disclosure provides a sleep monitoring garment and a sleep monitoring system. The sleep monitoring garment includes a wearable textile structure; a first monitoring band circumferentially extending along the wearable textile structure; a second monitoring band circumferentially extending along the wearable textile structure and with a distance to the first monitoring band; and an interface communicatively coupled with the first monitoring band and the second monitoring band.

The invention relates to a method for adjusting an apparatus for treatment using nuclear magnetic resonances. The chronotype of the user is determined. The apparatus is adjusted on the basis of this determination.