Abstract: The present disclosure pertains to automatically predicting a slow wave response of a subject to sensory stimulation during a sleep session. The sensory stimulation may be delivered to the subject upon detection of deep NREM sleep. The sensory stimulation may be auditory, haptic, visual, or other stimulation. The system delivers stimulation to the subject in blocks of stimulation separated from one another be intra-block intervals. The blocks are separated from each other by inter-block stimulations. The system compares the stimulated slow wave activity of the subject to the unstimulated slow wave activity of the subject. The system may update the stimulation parameters based on the comparison and deliver a subsequent block stimulation. Once the comparison indicates that the stimulated slow wave activity is significantly different from the unstimulated slow wave activity, the system may apply continuous fixed sensory stimulation to the user according to the most recent stimulation parameters.

Abstract: The present disclosure pertains to a system for delivering sensory stimulation to a subject, the system comprising: sensors configured to generate output signals indicating physiological parameters of a subject; a sensory stimulator configured to deliver sensory stimulation to the subject; and processors configured to: determine one or more physiological parameters of the subject; determine a target physiological parameter based on the determined one or more physiological parameters; determine one or more stimulation parameters of sensory stimulation to be delivered to the subject based on the target physiological parameter and the determined one or more physiological parameters; and cause the sensory stimulator to deliver the sensory stimulation to the subject based on the determined one or more stimulation parameters.

Abstract: An apparatus and method for employing the macro- and micro-structure of sleep and similar states of consciousness to optimize pain-treatment are disclosed wherein an objective biomarker of pain-related sleep disturbance guides pain treatment from a sleep perspective. Furthermore, this concept can be extended to states of reduced consciousness such as coma or sedation. Additionally, it could be applied on individuals who are non-communicative due to injury, disease, language issues and/or infancy.

Abstract: The present disclosure pertains to systems and methods for encoding and/or decoding brain activity signals for data reduction. In a non-limiting embodiment, first user data associated with a first sleep session of a user is received. The first user data is determined to include at least a first instance of a first sleep feature being of a first data size. A first value representing the first instance during a first temporal interval is determined. First encoding data representing the first value is determine, the first encoding data being of a second data size that is less than the first data size. Second user data is generated by encoding the first user data using the first encoding data to represent the first instance in the second user data, and the second user data is stored.

Abstract: The present disclosure pertains to enhancement of slow wave activity and personalized measurement thereof. Sensory stimulation may be delivered to a subject upon detection of slow wave activity of the subject. The system may obtain a baseline model, which includes baseline information describing slow wave activity increases due to sensory stimulation delivered to an age matched population. The system may generate a personalized model based on the baseline information, the sensory stimulation delivered to the subject, and slow wave activity increases due to sensory stimulation delivered to the subject during prior sleep sessions. The system may then provide the subject with personalized measurements relating to the slow wave activity enhancement.

Abstract: The present disclosure pertains to optimizing sleep score parameters. In one embodiment, a first set of vectors is obtained, where each vector includes parameter values of sleep-related parameters for calculating a sleep score. For each vector of the first set, a correlation value indicating a degree of a correlation between the vector and a user-indicated sleep score associated with a set of parameter values representative of a sleep metric of the user is determined. A second set of vectors is generated based on a first subset and a second subset of the first set of vectors satisfying a first and second criteria, respectively. A correlation value associated with each vector of the second set is determined. For each sleep-related parameter, a parameter value of a given vector of the second set is assigned based on the correlation values associated with the second set.

Abstract: A system for delivering sensory stimulation comprises a sensor configured to measure brain activity information of a patient during a sleep session; a sensory stimulator configured to deliver sensory simulation to the patient during the sleep session; and a computer system. One or more physical processors are programmed with computer program instructions which, when executed cause the computer system to: determine a first stimulation profile, a second stimulation profile, or a combination stimulation profile thereof based on obtained sleep cycle information and/or obtained cognitive domain information; and provide input to the sensory stimulator based on the determined stimulation profile, the provided input causing the sensory stimulator to deliver the sensory simulations to the patient based on the determined stimulation profile during the detected slow wave sleep in the patient.

Abstract: A smart scheduling and information gathering system to promote sleep and mental health in unprecedented emergency situations. The system includes a remote server used for gathering contextual event data from external data sources, capturing physiological data from an environmental sensor, generating a sleep availability rating by analyzing a sleep activity log, generating a scheduling availability rating by analyzing a current-date-and-time in relation to a user calendar managed by the remote server, and generating a behavioral recommendation and a filtering protocol by analyzing the contextual event data, the physiological data, the sleep availability rating, and the scheduling availability rating. The system further includes a user computing device used for outputting the behavioral recommendation, comparing incoming data to the filtering protocol in order to classify the incoming data as authorized data or unauthorized data, and outputting the authorized data.

Abstract: The present disclosure pertains to delivering sensory stimulation to a subject during a sleep session in order to enhance dream recall. The system may detect REM sleep in the subject during a sleep session based on brain activity of the subject. The system may then deliver sensory stimulation to the subject during the sleep session. The system may deliver sensory stimulation at a frequency in a theta range of an EEG signal in order to increase theta power in the brain activity of the subject. In some embodiments, the system may modulate the intensity of the sensory stimulation according to changes in the EEG theta power associated with the subject during the sleep session. In some embodiments, an increase in EEG theta power associated with the subject may promote dream recall.

Abstract: Described embodiments generally relate to a method for improving data accuracy of sleep pattern data. The method comprises receiving first data relating to at least one sleep pattern metric; receiving second data relating to the at least one sleep pattern metric, wherein the second data is data entered by a user; determining the difference between the first data and the second data to calculate a data infidelity value; and in response to the data infidelity value exceeding a predetermined threshold, prompting a user to enter third data relating to at least one metric.

Abstract: 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.

Abstract: The present disclosure pertains to a system and method for automatically detecting rapid eye movement (REM) sleep and delivering sensory stimulation to prolong REM duration, without disturbing sleep. The sensory stimulation may be auditory or other stimulation. The system and method ensure timely delivery of the stimulation and automatically adjust the amount, intensity, and/or timing of stimulation as necessary. REM sleep is detected based on brain activity, cardiac activity and/or other information. REM sleep may be detected and/or predicted by a trained neural network. The amount, timing, and/or intensity of the sensory stimulation may be determined and/or modulated to enhance REM sleep in a subject based on one or more values of one or more intermediate layers of the neural network and one or more brain activity and/or cardiac activity parameters.

Abstract: The present disclosure pertains to shortening sleep latency during the wake to sleep transition. Sensory stimulation is delivered to a subject by sensory stimulators during a sleep session, and a measure of wakefulness of the subject is determined. The sensory stimulators are controlled based on the measure of wakefulness of the subject. The sensory stimulators may be controlled to modulate an intensity of the sensory stimulation delivered to the subject. For example, the sensory stimulators may decrease the intensity of the sensory stimulation as the measure of wakefulness decreases. The system then determines that the measure of wakefulness has reached stable sleep. The sensory stimulators are controlled based on the determination that the measure of wakefulness has reached stable sleep. The sensory stimulators may decrease the intensity of the sensory stimulation at a faster rate once the measure of wakefulness reaches stable sleep.

Abstract: A system (400) for monitoring an individual's sleep includes: (i) a patient monitor (410) configured to obtain a patient waveform, the patient waveform comprising information representative of a vital statistic of the patient; a processor (420) in communication with the patient monitor and configured to: (i) process the patient waveform to generate a segmented waveform; (ii) extract at least one feature from a segment of the waveform in a time domain and/or at least one feature from the segment of the waveform in the frequency domain; (iii) classify, using the at least one extracted feature, a sleep stage of the patient for the segment of the waveform; and (iv) generate, from classified sleep stages for a plurality of segments of the waveform, a sleep quality measurement; and a user interface (480) configured to report the generated sleep quality measurement.

Abstract: The present disclosure pertains to delivering sensory stimulation to a user during a sleep session. In some embodiments, sensors are configured to generate output signals conveying information related to brain activity of the user during the sleep session. Sensory stimulators are configured to provide the sensory stimulation to the user during the sleep session. One or more processors are configured to determine a demographic group for the user; select a stimulation parameter model associated with the demographic group of the user from a set of stimulation parameter models associated with different demographic groups; and control the one or more sensory stimulators to deliver the sensory stimulation to the user based on the stimulation parameter model for the demographic group of the user and the output signals.

Abstract: The present disclosure pertains to a system and method for determining sleep stages during individual sleep cycles based on algorithms and/or parameters that correspond to the individual sleep cycles. The system enables more accurate real-time sleep stage determinations compared to prior art systems. Sleep cycles are detected in real-time based on an electroencephalogram (EEG), and/or by other methods. At the end of a sleep cycle, the system is configured such that the specific algorithms and/or parameters used for the previous sleep cycle to determine sleep stages are replaced by new ones which are specifically adapted for the next sleep cycle.

Abstract: Some embodiments relate to computer-implemented methods and systems for fidelity improvement of sleep disorder data.

Abstract: The present disclosure pertains to a system and method for determining whether a subject is likely to be disturbed by therapy levels of stimulation provided to the subject during sleep sessions. The present system is configured to automatically identify sensitive users using electroencephalogram (EEG) information from a reference sleep session with or without stimulation. For reference sleep sessions without stimulation, the alpha activity in detected deep sleep is used to predict whether the subject is likely to be disturbed by therapy levels of stimulation. For reference sleep sessions with stimulation, the acute increase in EEG delta (e.g., 0.5-4 Hz) power and/or an arousability index are used to predict whether the subject is likely to be disturbed by therapy levels of stimulation.

Abstract: The system receives a raw signal carrying information related to slow wave activity; buffers a portion of the raw signal; determines a timing of slow wave events in the buffered portion of the raw signal; filters the raw signal; determines a timing of slow wave events in the filtered raw signal; compares the timing of the slow wave events in the buffered portion of the raw signal to the timing of the slow wave events in the filtered raw signal; determines a first correction factor associated with reducing slow wave activity in the subject and a second correction factor associated with enhancing slow wave activity in the subject; and adjusts a timing of the stimulation provided to the subject during the sleep session based on the first and/or second correction factors.

Abstract: The present disclosure pertains to a system configured to output an indicator representative of effects of stimulation provided to a subject during a sleep session. The indicator is determined based on a combination of the effect of stimulation on sleep restoration, stimulation quality, sleep architecture factors, and/or other information. The indicator is determined using age matched reference information on deep sleep duration and EEG slow wave activity. The contribution to the indicator associated with sleep architecture factors is determined based on age matched reference information including sleep onset latency, wake after sleep onset, total sleep time, micro-arousal count, sleep stage(s) prior to awakening, and/or other information.