Abstract: A method for detecting an arousal in a sleep session of a subject that includes receiving a first signal comprising information about a first probability distribution of sleep stage classes during the first epoch. The method further comprises receiving a second signal comprising information about a second probability distribution of the sleep stage classes during the second epoch, determining a continuity metric representative of a difference between the first probability distribution and the second probability distribution, and determining a presence of an arousal based on the continuity metric. Further, there is provided an arousal detection system for detecting an arousal in a sleep session of a subject. The arousal detection system comprises a processing system and a sensor interface. The processing system is configured to perform the computer-implemented method.

Abstract: Disclosed are techniques for wireless positioning. In some aspects, a user equipment (UE) may obtain one or more global navigation satellite system (GNSS) measurements. The UE may obtain map data. The UE may obtain a most likely position of the UE based on one or more prediction algorithms and one or more neural network (NN) models applied to the one or more GNSS measurements and the map data.

Abstract: A computer-implemented method for estimating sleep stages that comprises receiving an electromyography (EMG) signal representative of electric activity of a muscle of the subject during the sleep session and providing only the EMG signal as an input to a machine learning model. The method comprises estimating a sleep stage during the sleep session based on an output of the machine learning model. The machine learning model is trained by providing, as a first input, a reference EMG signal representative of electric activity of a muscle of a reference subject during a reference sleep session, and providing, as a second input, a reference sleep stage signal representative of sleep stages of the reference subject during the reference sleep session. The machine learning model is trained by using the first input and the second input to estimate sleep stages based on only an EMG signal.

Abstract: A mechanism for generating output data using a generative model. The generative model comprises a plurality of score-based neural networks, each configured to generate an initial score processable, using a sampling technique, to produce an instance of example data. The initial scores are combined to define a combined score. The combined score is processed using the sampling technique to generate the output data. Each score-based neural network is trained using a different set of training data.

Abstract: A mechanism for estimating sleep stages of an subject's sleep session using a machine-learning algorithm. An EOG signal, produced during the sleep session, is obtained. For each of a plurality of samples, a subset of one or more other samples is obtained. The subset is identified by determining temporal dependencies between the sample and the other sample(s) using temporal information trained into the machine-learning algorithm. Each sample is processed using its corresponding subset of one or more other samples to produce an estimate sleep stage for the sample. This produces a plurality of sleep stages for the sleep session of the subject.

Abstract: A sleep monitoring system (100, 500), including: a sensor (114) to sense physiological changes of a subject during a sleep study period and form corresponding sleep study information; a stochastic neural network (129, 329) to: receive the sleep study information; and derive a plurality of sleep sample evaluations each different from each other and based on a same sampling of the received sleep study information; a controller (120, 190, 194) to: receive the plurality of sleep sample evaluations and calculate a robust estimate of one or more sleep parameters thereon; calculate an estimate of uncertainty for at least one of the one or more sleep parameters; determine whether the estimate of uncertainty for the at least one of the one or more sleep parameters is greater than a threshold value; and render the estimate of uncertainty on a rendering device (192) of the system based upon the determination.