The future of seizure detection

@article{Paesschen2018TheFO,
  title={The future of seizure detection},
  author={Wim Van Paesschen},
  journal={The Lancet Neurology},
  year={2018},
  volume={17},
  pages={200-202}
}
  • W. Paesschen
  • Published 1 March 2018
  • Medicine
  • The Lancet Neurology
Automatic annotation correction for wearable EEG based epileptic seizure detection
TLDR
An automatic approach to correct for imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms, and a novel approach to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings.
Towards long term monitoring: Seizure detection with reduced electroencephalogram channels
TLDR
The need for further research in electrode reduction is asserted to advance solutions toward portable, reliable devices that can simultaneously provide patient comfort, long-term monitoring and contribute to multi-modal patient care solutions.
EEG Enhancement by Auto DNNs with Regularization of Spatial Feature Loss
TLDR
Experimental results show that auto DNNs with regularization of spatial feature loss can efficiently eliminate the simulated noise in EEG data and makes the mean square error between predicted values and real values as small as 0.06.
Optimal Selection of Customized Features for Implementing Seizure Detection in Wearable Electroencephalography Sensor
TLDR
By removing redundant features, the computational complexity of on-line seizure detection can be greatly reduced, which is desired in wearable device where low-power and real-time operation are required.
Wearable seizure detection devices in refractory epilepsy
TLDR
WSDDs can improve the patient’s care and quality of life, decrease seizure underreporting and they could potentially prevent sudden-unexpected-death in epilepsy, and a business to business to consumer model is better than the current business to consumers model of most WSDDs.
Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels
TLDR
In this study, it is determined whether the recognition of ictal patterns using only behind‐the‐ear EEG channels is possible and an automated seizure detection algorithm was developed using only those behind-the‐ ear EEG channels.
A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy
TLDR
The CHMM model outperforms standard machine learning techniques in the focal dataset and achieves comparable performance to the best baseline method in the pediatric dataset and has the ability to track seizures, which is valuable information to localize focal onset zones.
Live Demonstration: SeizeIT - A wearable multimodal epileptic seizure detection device
TLDR
A small wearable multisensor device for epileptic activity monitoring and seizure detection in the everyday life of a patient and a software application allows streaming of the physiological signals in real-time.
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