Circadian Technologies, Inc.

Rainer Guttkuhn

Martin Christopher Moore-Ede

Anneke Marlies Heitmann

Udo E Trutschel

A method and apparatus for determining, monitoring and predicting levels of alertness by detecting microsleep episodes includes a plurality of channel processing units and a channel combining unit. Each of the channel processing units receives an information channel which conveys information associated with the mental state of the subject, such as an EEG channel, and classifies the information into a distinct category. Such categories may include microsleep, non-microsleep, one or more of a plurality of stages of sleep, one or more of a plurality of stages of wakefulness, or a transition state characterized by a transition from one of the aforementioned states to another. Each of the channel processing units includes a neural network which has been trained with a set of example input/result vector pairs. The example input/result vector pairs are generated by correlating actual information channel outputs with observed fatigue related events such as nodding off, head snapping, multiple blinks, blank stares, wide eyes, yawning, prolonged eyelid closures, and slow rolling eye movements.

Method of and apparatus for evaluation and mitigation of microsleep events

A method and apparatus for determining, monitoring and predicting levels of alertness by detecting microsleep episodes includes a plurality of channel processing units and a channel combining unit. Each of the channel processing units receives an information channel which conveys information associated with the mental and behavorial state of the subject, such as for example an EEG channel, and classifies the information into a distinct category. Such categories may include microsleep, non-microsleep, one or more of a plurality of stages of sleep, one or more of a plurality of stages of wakefulness, or a transition state characterized by a transition from one of the aforementioned states to another. Each of the channel processing units includes a neural network which has been trained with a set of example input/result vector pairs. The example input/result vector pairs are generated by correlating actual information channel outputs with observed fatigue related events such as nodding off, head snapping, multiple blinks, blank stares, wide eyes, yawning, partial and complete prolonged eyelid closures, and slow rolling eye movements.

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Cambridge, MA, United States of America

Rainer Guttkuhn