Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals

@article{Avilov2020DeepLT,
  title={Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals},
  author={Oleksii Avilov and S{\'e}bastien Rimbert and Anton Popov and Laurent Bougrain},
  journal={2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  year={2020},
  pages={142-145}
}
  • O. Avilov, Sébastien Rimbert, +1 author L. Bougrain
  • Published 1 July 2020
  • Computer Science, Medicine
  • 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Every year, millions of patients regain conscious- ness during surgery and can potentially suffer from post-traumatic disorders. We recently showed that the detection of motor activity during a median nerve stimulation from electroencephalographic (EEG) signals could be used to alert the medical staff that a patient is waking up and trying to move under general anesthesia [1], [2]. In this work, we measure the accuracy and false positive rate in detecting motor imagery of several deep learning… Expand

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