The future of seizure detection

  title={The future of seizure detection},
  author={Wim Van Paesschen},
  journal={The Lancet Neurology},
  • W. Paesschen
  • Published 1 March 2018
  • Medicine
  • The Lancet Neurology
Automatic annotation correction for wearable EEG based epileptic seizure detection
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
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
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
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
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
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
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
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.


Comparison of a single‐channel EEG sleep study to polysomnography
The results show that single‐channel EEG provides comparable results to polysomnography in assessing REM, combined Stages N2 and N3 sleep and several other parameters, including frontal slow wave activity, which establishes that single-channel EEG can be a useful research tool.
Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy
The feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy is demonstrated by comparison with scalp EEG recordings.
Silent Hippocampal Seizures and Spikes Identified by Foramen Ovale Electrodes in Alzheimer’s Disease
The findings in these index cases support a model in which early development of occult hippocampal hyperexcitability may contribute to the pathogenesis of AD.
Validation of a smartphone-based EEG among people with epilepsy: A prospective study
Despite limitations in sensitivity, the SBS2 may become a viable supportive test for the capture of epileptiform abnormalities, and extend EEG access to new, especially resource-limited, populations at a reduced cost.
Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment
The performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm, and seizure detection performance using the wrist-worn PPG device was considerably lower.