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

Abstract: An apparatus and method for predicting the occurrence of sleep disorders and particularly insomnia, by long term monitoring of daily habits causing stress and sleep reactivity, and by coaching for correcting behaviors that can trigger the sleep disorder's occurrence and suggest interventions to mitigate the problem.

Abstract: A method for cluster-based recommendation generation regarding sleep disorders. A server system transmitting query program code to a client device, wherein the query program code is executable by the client device to transmit one or more response objects encoding a response and further responses to the server system. The server system receiving the one or more response objects from the client device and determining the response and further responses encoded in the response objects. A clustering module of the server system identifying one or more clusters of sleep disorder user data that most closely relate to the determined responses. A recommendation module of the server system identifying a sleep disorder based on the determined responses and clusters. The recommendation module generating one or more recommendations based on the identified sleep disorder, the determined responses and the identified clusters.

Abstract: A method of determining a sleep phenotype of a subject includes defining a stimulus profile, defining a response profile, administering the stimulus profile to the subject, collecting response data of the subject in accordance with the response profile; comparing the response data to a set of reference data, and determining sleep phenotype based on the comparison of the response data to the reference data. The method is characterized by determining sleep phenotype based on measuring responses to administered stimuli rather than observing typical physiological data observed during periods of sleep.

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: 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 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 a system configured to detect slow waves in a subject during a sleep session. The system generates output signals conveying information related to brain activity of the subject. The system is configured to detect individual sleep stages of the subject, the individual sleep stages including a deep sleep stage; and, responsive to detecting the deep sleep stage, generate a harmonic representation of the output signals for a period of time during the sleep session that includes the deep sleep stage; identify two or more points of significance on the harmonic representation of the output signals; and analyze a shape of the harmonic representation of the output signals around the two or more points of significance to determine whether the shape of the harmonic representation of the output signals around the two or more points of significance corresponds to a shape of a slow wave.

Abstract: The present disclosure pertains to a system configured to a system configured to detect slow waves based on adjusted slow wave detection criteria and time delivery of the sensory stimulation to correspond to slow waves detected based on the adjusted criteria. The system is configured to adjust slow wave detection criteria to enhance detection of slow waves in a subject. Slow wave detection using adjustable slow wave detection criteria produces more stimulation relative to prior art systems because more individual stimuli are provided if more slow waves are detected. In some embodiments, the system includes one or more of a sensory stimulator, a sensor, a processor, electronic storage, a user interface, and/or other components.

Abstract: The present disclosure pertains to a system configured to a system configured to detect slow waves based on adjusted slow wave detection criteria and time delivery of the sensory stimulation to correspond to slow waves detected based on the adjusted criteria. The system is configured to adjust slow wave detection criteria to enhance detection of slow waves in a subject. Slow wave detection using adjustable slow wave detection criteria produces more stimulation relative to prior art systems because more individual stimuli are provided if more slow waves are detected. In some embodiments, the system includes one or more of a sensory stimulator, a sensor, a processor, electronic storage, a user interface, and/or other components.

Abstract: The present disclosure pertains to a system configured to detect slow waves in a subject during a sleep session. The system generates output signals conveying information related to brain activity of the subject. The system is configured to detect individual sleep stages of the subject, the individual sleep stages including a deep sleep stage; and, responsive to detecting the deep sleep stage, generate a harmonic representation of the output signals for a period of time during the sleep session that includes the deep sleep stage; identify two or more points of significance on the harmonic representation of the output signals; and analyze a shape of the harmonic representation of the output signals around the two or more points of significance to determine whether the shape of the harmonic representation of the output signals around the two or more points of significance corresponds to a shape of a slow wave.