A software framework for real-time multi-modal detection of microsleeps

Abstract

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

DOI: 10.1007/s13246-017-0559-x

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Cite this paper

@article{Knopp2017ASF, title={A software framework for real-time multi-modal detection of microsleeps}, author={Simon J. Knopp and Philip J. Bones and Stephen J. Weddell and Richard D. Jones}, journal={Australasian Physical & Engineering Sciences in Medicine}, year={2017}, volume={40}, pages={739-749} }