A wheel chart for visualizing sleep pressure on a person's body by manipulating a series of moveable circular disks positioned on a common axis wherein the wheel includes a first disk representing a circadian (C) process and second disk representing a homeostatic (S) process. The disks are rotatable relative to each other to simulate how the C and S processes increase and decrease sleep pressure.

The present disclosure provides systems and method of analyzing whether a sleep epoch is a REM sleep epoch or a wake epoch. In accordance with aspects of the present disclosure, a computer-implemented method includes accessing cardiopulmonary coupling data spanning a sleep period for a person, identifying an epoch in the sleep period corresponding to very-low frequency coupling in the cardiopulmonary coupling data, accessing high-frequency coupling data and/or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch, and designating the epoch as a REM sleep epoch or as a wake epoch based on the high-frequency coupling data and/or the low-frequency coupling data corresponding to the epoch, where the epoch is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

Cardiac data defining at least inter-beat interval (IBI) sequences is received. Tagging data that defines tags of sleep-states for the IBI sequences is received. A sleep-state classifier is generated using the cardiac data and the tagging data, the generating may include: extracting the IBI sequences from the cardiac data; training a convolutional neural network (CNN) using as input the cardiac data and the tagging data to generate intermediate data; and iteratively training a recurrent neural network (RNN) configured to produce state data as output, the iterative training of the RNN using i) the intermediate data as an initial input and ii) the intermediate data and a previous state data as subsequent input.

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.

Embodiments are disclosed for sleep staging using machine learning. In an embodiment, a method comprises: receiving, with at least one processor, sensor signals from a sensor, the sensor signals including at least motion signals and respiratory signals of a user; extracting, with the at least one processor, features from the sensor signals; predicting, with a machine learning classifier, that the user is asleep or awake based on the features; and computing, with the at least one processor, a sleep or wake metric based on whether the user is predicted to be asleep or awake.

A management apparatus includes an obtainer that obtains detected information from a sensor that detects sleep of a user; a sleep determiner that determines whether the user is in a sleep state, based on the detected information; a manager that is connected to an appliance via a network, and obtains state information indicating an operation state of the appliance; an appliance determiner that determines whether the state information is different from predetermined normal information indicating a normal operation state of the appliance, when the sleep determiner determines that the user is in the sleep state; and a notification controller that outputs a notification signal, when the appliance determiner determines that the state information is different from the normal information.

Millimeter (mmWave) mapping systems and methods are disclosed for generating one or more point clouds and determining one or more vital signs for defining a human psychological state. A point cloud comprising point cloud data defining a person or an object detected within a physical space is generated based on one or more mmWave waveforms of an mmWave sensor. A posture of the person within the portion of the physical space is determined from the point cloud data. One or more vital signs of the person is determined based on the mmWave waveform(s). An electronic feedback is provided representing a human psychological state of the person as defined by the point cloud data and the one or more vital signs of the person.

The method of the present invention quantifies the severity of a subject's neurodegenerative disorder. The subject answers a questionnaire which results in a patient-reported outcome dataset. Benchmark tests are carried out by the subject performing one or more tasks resulting in a task result dataset. Continuous sensors collect data resulting in a sensor dataset. Short assessment tests of the subject are conducted resulting in a short assessment dataset. The patient-reported outcome dataset, task result dataset, sensor dataset, and short assessment dataset are aggregated into an output dataset that includes non-motor outcome measures and motor outcome measures. A single score is generated that quantifies the severity of a neurodegenerative disorder of the subject based on the output dataset.

Methods and systems are provided for detecting patient motion during a diagnostic scan. In one example, a method for a medical imaging system includes obtaining output from one or more patient monitoring devices configured to monitor a patient before and during a diagnostic scan executed with the medical imaging system, receiving a request to initiate the diagnostic scan, tracking patient motion based on the output from the one or more patient monitoring devices, and initiating the diagnostic scan responsive to patient motion being below a threshold level.

Described herein are noninvasive electrical stimulation devices, systems and methods for stimulation of the Vagus nerve through its auricular branch to provide beneficial physiological responses in subjects, including alleviation, mitigation or elimination of symptoms of various disorders, including metabolic and inflammatory disorders.