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Real-time epileptic seizure detection using reservoir computing

Pieter Buteneers UGent, Benjamin Schrauwen UGent, David Verstraeten UGent and Dirk Stroobandt UGent (2009) Seizure Prediction, 4th International workshop, Abstracts.
abstract
1 Purpose : This study proposes the use of a new classification algorithm, Reservoir Computing, to develop a real-time and accurate epileptic seizure detection system. 2 Methods: Reservoir Computing (RC) is a training method for recurrent neural networks where only a simple linear readout function is trained and where the neural network, the reservoir, is randomly created. As input for this reservoir we use a selection of different EEG features currently existing in seizure detection literature. This selection was made during training using a basic feature selection method. The output of the reservoir was trained using a ridge regression algorithm. 3 Results : In this study intracranial rat data from two different types of generalized epilepsy are detected: absence and tonic-clonic epilepsy. For both seizure types our approach resulted in an area under the Receiver Operating Characteristics curve (AUC) of 0.99 on the test data. For absences an average detection delay of 0.3s was noted, for tonic-clonic seizures this was 1.5s. The SWD detection method was tested on 15 hours of EEG-data coming from 13 GAERS rats, from which 10% was used for training. Our method outperformed the other implemented methods from which the best method was developed by Fanselow et al. in 2000 and resulted in an AUC of 0.96 and an average detection delay of more than 3 seconds. To evaluate the tonic-clonic seizure detection method 4 hours and 23 minutes of data of 4 rats was used. 20% of the total dataset was used for training, the rest was used for testing. Again our method outperformed other methods where the best method by White et al. in 2006 which resulted in a AUC of 0.82. 4 Conclusion : This study shows that it is possible to perform seizure detection using the described Reservoir Computing method and that it outperforms existing methods.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
in
Seizure Prediction, 4th International workshop, Abstracts
conference name
4th International workshop on Seizure Prediction
conference location
Kansas City, MO, USA
conference start
2009-06-04
conference end
2009-06-07
project
Home-MATE
language
English
UGent publication?
yes
classification
C3
additional info
uploaded file is poster version
copyright statement
I have retained and own the full copyright for this publication
id
1886942
handle
http://hdl.handle.net/1854/LU-1886942
date created
2011-08-09 12:54:48
date last changed
2011-08-11 16:51:46
@inproceedings{1886942,
  abstract     = {1 Purpose : This study proposes the use of a new classification algorithm, Reservoir Computing, to develop a real-time and accurate epileptic seizure detection system.
2 Methods: Reservoir Computing (RC) is a training method for recurrent neural networks where only a simple linear readout function is trained and where the neural network, the reservoir, is randomly created. As input for this reservoir we use a selection of different EEG features currently existing in seizure detection literature. This selection was made during training using a basic feature selection method. The output of the reservoir was trained using a ridge regression algorithm.
3 Results : In this study intracranial rat data from two different types of generalized epilepsy are detected: absence and tonic-clonic epilepsy. For both seizure types our approach resulted in an area under the Receiver Operating Characteristics curve (AUC) of 0.99 on the test data. For absences an average detection delay of 0.3s was noted, for tonic-clonic seizures this was 1.5s. 
The SWD detection method was tested on 15 hours of EEG-data coming from 13 GAERS rats, from which 10\% was used for training. Our method outperformed the other implemented methods from which the best method was developed by Fanselow et al. in 2000 and resulted in an AUC of 0.96 and an average detection delay of more than 3 seconds. 
To evaluate the tonic-clonic seizure detection method 4 hours and 23 minutes of data of 4 rats was used. 20\% of the total dataset was used for training, the rest was used for testing. Again our method outperformed other methods where the best method by White et al. in 2006 which resulted in a AUC of 0.82.
4 Conclusion : This study shows that it is possible to perform seizure detection using the described Reservoir Computing method and that it outperforms existing methods.},
  author       = {Buteneers, Pieter and Schrauwen, Benjamin and Verstraeten, David and Stroobandt, Dirk},
  booktitle    = {Seizure Prediction, 4th International workshop, Abstracts},
  language     = {eng},
  location     = {Kansas City, MO, USA},
  title        = {Real-time epileptic seizure detection using reservoir computing},
  year         = {2009},
}

Chicago
Buteneers, Pieter, Benjamin Schrauwen, David Verstraeten, and Dirk Stroobandt. 2009. “Real-time Epileptic Seizure Detection Using Reservoir Computing.” In Seizure Prediction, 4th International Workshop, Abstracts.
APA
Buteneers, P., Schrauwen, B., Verstraeten, D., & Stroobandt, D. (2009). Real-time epileptic seizure detection using reservoir computing. Seizure Prediction, 4th International workshop, Abstracts. Presented at the 4th International workshop on Seizure Prediction.
Vancouver
1.
Buteneers P, Schrauwen B, Verstraeten D, Stroobandt D. Real-time epileptic seizure detection using reservoir computing. Seizure Prediction, 4th International workshop, Abstracts. 2009.
MLA
Buteneers, Pieter, Benjamin Schrauwen, David Verstraeten, et al. “Real-time Epileptic Seizure Detection Using Reservoir Computing.” Seizure Prediction, 4th International Workshop, Abstracts. 2009. Print.