Abstract: Methods and systems are provided for assisting a user of a wearable biosignal monitoring device (2) in adjusting the device to achieve optimum fit and positioning. The biosignal monitoring devices considered use integrated bio sensors (5) to monitor the user’s physiological activity for various purposes such as tracking daily activity patterns, determining mood, and monitoring sleep stages, among others. It is determined either during device setup or during primary use of the device whether the current fit and positioning of the device (2) enable the bio sensors (5) to properly sense the physiological signals needed for the device to perform its primary function. The user is then informed either after initial device setup whether adjustments need to be made in order to optimize device function during primary use, or is informed after primary use whether adjustments need to be made in order to improve device function during future primary use.

Abstract: The present invention relates to an agenda generation system (10), comprising: at least one first sensor unit (20); an input unit (30); a processing unit (40); and at least one output unit (50); wherein the at least one first sensor unit is configured to acquire physiological data of a subject over an extended period of time prior to a current time point and provide the physiological data to the processing unit, wherein the physiological data is assigned to different times during the extended period of time, and wherein the physiological data comprises one or more of: heart rate, ventricular high rate, blood pressure, respiration rate, skin conductance, step count; wherein the at least one first sensor unit is configured to acquire sleep data of the subject over the extended period of time and provide the sleep data to the processing unit, wherein the sleep data is assigned to different times during the extended period of time, and wherein the sleep data comprises one or more of: duration of sleep; total d

Abstract: A method of managing and reducing the recall frequency of disturbing dreams comprises monitoring (100; 200; 300) electroencephalography (EEG) activity of a subject, detecting (102; 206; 302) an indicator of a disturbing dream of the subject based on power in a theta or alpha band of the EEG activity of the subject, and providing stimulation (104; 212; 308) to the subject at a frequency lower than the theta band in response to detecting the indicator of the disturbing dream.

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 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 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 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.

Abstract: The present disclosure pertains to manipulating electrical activity in the brain of a subject to facilitate wakefulness. The system comprises: a sensory stimulator; a sensor configured to generate output signals conveying information related to brain activity, activity of the central nervous system, and/or activity of the peripheral nervous system of the subject; and a processor configured to: receive a target wake-up moment for the subject; determine one or more activity parameters of the subject during the sleep session; determine whether the one or more activity parameters indicate the subject is in deep sleep a predetermined amount of time before the target wake-up moment; and, responsive to the one or more activity parameters indicating the subject is in deep sleep, cause the one or more sensory stimulators to guide the activity parameters and facilitate/accelerate a transition from deep sleep to light sleep before the target wake-up moment.

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: A method is provided for measuring a wake up indicator, where the wake up indicator gives the likelihood of a user waking up undesirably. It is based on the knowledge that waking up during deep NREM sleep is not desirable due to sleep inertia and on the recognition that it is also undesirable to wake up during REM sleep due to atonia. A sleep inertia estimation is determined to estimate if the user is in NREM sleep and an atonia estimation is determined to estimate if the user is in REM sleep. The wake up indicator is determined from the sleep inertia estimation and the atonia estimation. The wake up indicator thus may be used as an indicator for the time when it is suitable to wake up the user in a way which avoids arousal from a deep sleep state or from REM sleep, during which atonia may arise.

Abstract: The present disclosure pertains to a system and method for determining sleep onset latency in a subject. The system is configured to generate output signals conveying information related to brain activity in the subject, determine sleep stages of the subject based on the output signals, determine a sleep onset moment in the subject based on the determined sleep stages, determine a sleep intention moment for the subject by: (i) detecting eye blinks in the subject based on the output signals, and determining the sleep intention moment responsive to the detected eye blinks ceasing for a predetermined period of time; and/or (ii) determining whether brain activity power in a target frequency band has breached a threshold power level based on the output signals, and determining the sleep intention moment responsive to a breach; and determine the sleep onset latency based on the sleep onset moment and the sleep intention moment.

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: A system for delivering sensory stimulation a sensor; a sensory stimulator configured to deliver sensory stimulation to a patient during a sleep session, the sensory stimulation having varying stimulation intensity levels; and a computer system. One or more physical processors being programmed with computer program instructions which, when executed cause the computer system to: determine sleep stage information of the patient based on brain activity information of the patient during the sleep session from the sensor; provide input to the sensory stimulator based on the determined sleep stage information of the patient, the provided input causing the sensory stimulator to deliver the sensory stimulation to the patient; obtain stimulation response information from the patient, the stimulation response information including patient brain response to the delivered sensory stimulation; and determine a range of the stimulation intensity levels within which the patient brain response reaches a threshold.

Abstract: The present disclosure pertains to a system configured to provide sensory stimulation to a subject during a sleep session. The system includes one or more sensory stimulators configured to provide sensory stimulation to the subject; one or more sensors configured to generate output signals conveying information related to brain activity of the subject; and one or more processors configured to detect individual slow waves in the subject; control the one or more sensory stimulators to provide sensory stimulation to the subject based on the detected individual slow waves; predict a timing for occurrence of a predicted slow wave based on the previously detected individual slow waves; and responsive to not detecting the predicted slow wave at the predicted timing control the one or more sensory stimulators to provide sensory stimulation at the predicted timing for occurrence of the predicted slow wave.

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 manipulating electrical activity in the brain of a subject to facilitate wakefulness. The system comprises: a sensory stimulator; a sensor configured to generate output signals conveying information related to brain activity, activity of the central nervous system, and/or activity of the peripheral nervous system of the subject; and a processor configured to: receive a target wake-up moment for the subject; determine one or more activity parameters of the subject during the sleep session; determine whether the one or more activity parameters indicate the subject is in deep sleep a predetermined amount of time before the target wake-up moment; and, responsive to the one or more activity parameters indicating the subject is in deep sleep, cause the one or more sensory stimulators to guide the activity parameters and facilitate/accelerate a transition from deep sleep to light sleep before the target wake-up moment.

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.

Abstract: The present disclosure pertains to a system and method for determining sleep onset latency in a subject. The system is configured to generate output signals conveying information related to brain activity in the subject, determine sleep stages of the subject based on the output signals, determine a sleep onset moment in the subject based on the determined sleep stages, determine a sleep intention moment for the subject by: (i) detecting eye blinks in the subject based on the output signals, and determining the sleep intention moment responsive to the detected eye blinks ceasing for a predetermined period of time; and/or (ii) determining whether brain activity power in a target frequency band has breached a threshold power level based on the output signals, and determining the sleep intention moment responsive to a breach; and determine the sleep onset latency based on the sleep onset moment and the sleep intention moment.

Abstract: The present disclosure pertains to facilitating sleep improvement for a user. In a non-limiting embodiment, user data associated with a sleep session of a user is received from one or more sensors. Based on the user data, one or more sleep metrics associated with the sleep session are generated. One or more reference sleep metrics are determined based on prior user data obtained from one or more prior sleep sessions. One or more immediate values related to the sleep session is/are determined based on a comparison of the sleep metrics with the reference sleep metrics. A sleep session score value is generated based on the immediate values, and the sleep session score value and the sleep metrics are caused to be presented on via an output device.

Abstract: The present disclosure pertains to a system configured to provide sensory stimulation to a subject (12) during a sleep session. The system includes one or more sensory stimulators (16) configured to provide sensory stimulation to the subject; one or more sensors (18) configured to generate output signals conveying information related to brain activity of the subject; and one or more processors (20) configured to detect individual slow waves in the subject; control the one or more sensory stimulators to provide sensory stimulation to the subject based on the detected individual slow waves; predict a timing for occurrence of a predicted slow wave based on the previously detected individual slow waves; and responsive to not detecting the predicted slow wave at the predicted timing control the one or more sensory stimulators to provide sensory stimulation at the predicted timing for occurrence of the predicted slow wave.