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Wakefulness

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The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness.

Accident; analysis and prevention
Slowed eyelid closure coupled with increased duration and frequency of closure is associated with drowsiness. This study assessed the utility of two devices for automated measurement of slow eyelid closure in a standard poor performance condition (al...

Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines.

NeuroImage
A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decodin...

Detection and prediction of driver drowsiness using artificial neural network models.

Accident; analysis and prevention
Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsine...

The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks.

PloS one
Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascade...

Multi-neuron intracellular recording in vivo via interacting autopatching robots.

eLife
The activities of groups of neurons in a circuit or brain region are important for neuronal computations that contribute to behaviors and disease states. Traditional extracellular recordings have been powerful and scalable, but much less is known abo...

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE journal of biomedical and health informatics
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, whic...

Quiet sleep detection in preterm infants using deep convolutional neural networks.

Journal of neural engineering
OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analy...

Modeling brain dynamic state changes with adaptive mixture independent component analysis.

NeuroImage
There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formati...

Sleep-wake classification via quantifying heart rate variability by convolutional neural network.

Physiological measurement
OBJECTIVE: Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series.

Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness.

Accident; analysis and prevention
Monitoring car drivers for drowsiness is crucial but challenging. The high inter-individual variability observed in measurements raises questions about the accuracy of the drowsiness detection process. In this study, we sought to enhance the performa...