Abstract: A method and system for detecting sleep apnea involves determining the sleep stage, and detecting an apnea event based on a physiological sensor signal using selection of a detection algorithm which is dependent on the determined sleep stage. By taking account of the sleep stage when performing an automated apnea detection process, the accuracy of the apnea detection is improved.

Abstract: The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep.

Abstract: The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface.

Abstract: Systems and methods for providing sensory stimuli induce and/or enhance sleep and/or slow wave neural activity of a subject during sleep. Operation of the systems and methods is based on measured information related to cardiac attributes and/or respiratory attributes of the subject, and corresponding cardiac parameters and/or respiratory parameters based thereon. Attributes may be measured and/or monitored via one or more sensors, e.g. worn on an extremity of the subject and/or placed at a distance from the subject. Sensory stimulation delivered to the subject during specific targeted periods of sleep may enhance slow wave neural activity.

Abstract: A sleep monitoring device (100) is disclosed for measuring a biophysical variable (131) of a living-being (150), comprising at least two pressure sensors (101-103) spatially arranged in a predefined planar geometry. The living-being rests on the supporting layer (199) contacted by the sensors that generate sensor signals in response to pressure caused by the living being via the supporting layer. A processing unit (110) determines the position of the living-being on the supporting layer based on differences between the sensor signals; a magnitude attenuation factor is determined based on the position of the living-being and a position of a sensor; the biophysical variable is determined from a sensor signal generated by that sensor, including a magnitude correction based on the magnitude attenuation factor. The effect of the invention is that only few sensors are needed to measure a biophysical variable accurately, irrespective of the living-being's position on the supporting layer.

Abstract: The present invention relates to a device (16) and method for monitoring a physiological state of a subject (32). To reduce the energy consumption but still provide a high accuracy, the proposed device comprises a sensor interface (18) for obtaining from a sensor (20) a sensor signal indicative of a vital sign of a subject; a power storage interface (22) for obtaining a charge value indicative of a charge state of a power storage (24) powering the sensor; a duty cycle module (28) for controlling the duty cycle of the sensor based on the charge value; and a processing unit (26) for extracting at least one feature indicative of a physiological state of the subject from the sensor signal.

Abstract: A sleep monitor for monitoring baby sleep uses sleep state classification based on heartbeat feature respiration features. The sleep monitor automatically retrains the classification during use of the sleep monitor. Training examples for use in this training process are generated automatically by detecting time instants whereat the baby in the bed is in a wake state, based on signals from the at least one of a sound feature detector a movement feature detector (112) and an open eye detector (114). The retraining may comprise using time sequence from the end of detection of wake states to assign a class to heartbeat feature and/or respiration feature values during that time sequence for the training process. In an embodiment, the retraining comprises clustering detected heartbeat feature and/or respiration feature values detected outside the detected wake states.

Abstract: The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface.

Abstract: The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep.

Abstract: An actigraphy method includes receiving a physiological parameter signal as a function of time for a physiological parameter other than body motion (such as electrocardiography or a respiration monitor), computing a body motion artifact (BMA) signal as a function of time from the physiological parameter signal (for example, using a local signal power signal, a local variance signal, a short-time Fourier transform, or a wavelet transform over epochs of duration on order a few minutes or less), and computing an actigraphy signal as a function of time from the BMA signal, for example by applying a linear transform to the BMA signal and optionally applying filtering such as median removal and/or high-pass filtering.

Abstract: An electronic switch for controlling a device 170 by switching a function of the device at least in dependence on a sleep stage of a human. The switch includes an EEG data interface configured to receive brain activity data from an EEG sensor 120 configured to monitor electrical activity of the brain of the human during a training phase, an EEG sleep classifier 125 configured to classify sleep stages of the human from the received brain activity data, and a body data interface configured to receive body activity data from an alternative sensor 130 configured to monitor a bodily function of the human both during the training phase and during a subsequent usage phase.

Abstract: Systems and methods for providing sensory stimuli induce and/or enhance sleep and/or slow wave neural activity of a subject during sleep. Operation of the systems and methods is based on measured information related to cardiac attributes and/or respiratory attributes of the subject, and corresponding cardiac parameters and/or respiratory parameters based thereon. Attributes may be measured and/or monitored via one or more sensors, e.g. worn on an extremity of the subject and/or placed at a distance from the subject. Sensory stimulation delivered to the subject during specific targeted periods of sleep may enhance slow wave neural activity.

Abstract: An actigraphy method includes receiving a physiological parameter signal as a function of time for a physiological parameter other than body motion (such as electrocardiography or a respiration monitor), computing a body motion artifact (BMA) signal as a function of time from the physiological parameter signal (for example, using a local signal power signal, a local variance signal, a short-time Fourier transform, or a wavelet transform over epochs of duration on order a few minutes or less), and computing an actigraphy signal as a function of time from the BMA signal, for example by applying a linear transform to the BMA signal and optionally applying filtering such as median removal and/or high-pass filtering.

Abstract: A sleep monitoring device (100) is disclosed for measuring a biophysical variable (131) of a living-being (150), comprising at least two pressure sensors (101-103) spatially arranged in a predefined planar geometry. The living-being rests on the supporting layer (199) contacted by the sensors that generate sensor signals in response to pressure caused by the living being via the supporting layer. A processing unit (110) determines the position of the living-being on the supporting layer based on differences between the sensor signals; a magnitude attenuation factor is determined based on the position of the living-being and a position of a sensor; the biophysical variable is determined from a sensor signal generated by that sensor, including a magnitude correction based on the magnitude attenuation factor. The effect of the invention is that only few sensors are needed to measure a biophysical variable accurately, irrespective of the living-being's position on the supporting layer.

Abstract: An electronic switch for controlling a device (170) by switching a function of the device at least in dependency on a sleep stage of a human, the switch comprising an EEG data interface configured to receive brain activity data from an EEG sensor (120) configured to monitor electrical activity of the brain of the human during a training phase, an EEG sleep classifier (125) configured to classify sleep stages of the human from the received brain activity data, a body data interface configured to receive body activity data from an alternative sensor (30) configured to monitor a bodily function of the human both during the training phase and during a subsequent usage phase, the alternative sensor being different from the EEG sensor, an alternative sleep classifier (135) and a machine learning system (140), the machine learning system being configured to train the alternative sleep classifier (135) to classify a sleep stage of the human from the received body activity data, the learning system using sleep stages