Detecting seizures in EEG recordings using conformal prediction

Charalambos Eliades, Harris Papadopoulos
Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 91:171-186, 2018.

Abstract

This study examines the use of the Conformal Prediction (CP) framework for the provision of confidence information in the detection of seizures in electroencephalograph (EEG) recordings. The detection of seizures is an important task since EEG recordings of seizures are of primary interest in the evaluation of epileptic patients. However, manual review of long-term EEG recordings for detecting and analyzing seizures that may have occurred is a time-consuming process. Therefore a technique for automatic detection of seizures in such recordings is highly beneficial since it can be used to significantly reduce the amount of data in need of manual review. Additionally, due to the infrequent and unpredictable occurrence of seizures, having high sensitivity is crucial for seizure detection systems. This is the main motivation for this study, since CP can be used for controlling the error rate of predictions and therefore guaranteeing an upper bound on the frequency of false negatives.

Cite this Paper

BibTeX
@InProceedings{pmlr-v91-eliades18a, title = {Detecting seizures in {EEG} recordings using conformal prediction}, author = {Eliades, Charalambos and Papadopoulos, Harris}, booktitle = {Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {171--186}, year = {2018}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni and Peeters, Ralf}, volume = {91}, series = {Proceedings of Machine Learning Research}, month = {11--13 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v91/eliades18a/eliades18a.pdf}, url = {https://proceedings.mlr.press/v91/eliades18a.html}, abstract = {This study examines the use of the Conformal Prediction (CP) framework for the provision of confidence information in the detection of seizures in electroencephalograph (EEG) recordings. The detection of seizures is an important task since EEG recordings of seizures are of primary interest in the evaluation of epileptic patients. However, manual review of long-term EEG recordings for detecting and analyzing seizures that may have occurred is a time-consuming process. Therefore a technique for automatic detection of seizures in such recordings is highly beneficial since it can be used to significantly reduce the amount of data in need of manual review. Additionally, due to the infrequent and unpredictable occurrence of seizures, having high sensitivity is crucial for seizure detection systems. This is the main motivation for this study, since CP can be used for controlling the error rate of predictions and therefore guaranteeing an upper bound on the frequency of false negatives.} }
Endnote
%0 Conference Paper %T Detecting seizures in EEG recordings using conformal prediction %A Charalambos Eliades %A Harris Papadopoulos %B Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2018 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %E Ralf Peeters %F pmlr-v91-eliades18a %I PMLR %P 171--186 %U https://proceedings.mlr.press/v91/eliades18a.html %V 91 %X This study examines the use of the Conformal Prediction (CP) framework for the provision of confidence information in the detection of seizures in electroencephalograph (EEG) recordings. The detection of seizures is an important task since EEG recordings of seizures are of primary interest in the evaluation of epileptic patients. However, manual review of long-term EEG recordings for detecting and analyzing seizures that may have occurred is a time-consuming process. Therefore a technique for automatic detection of seizures in such recordings is highly beneficial since it can be used to significantly reduce the amount of data in need of manual review. Additionally, due to the infrequent and unpredictable occurrence of seizures, having high sensitivity is crucial for seizure detection systems. This is the main motivation for this study, since CP can be used for controlling the error rate of predictions and therefore guaranteeing an upper bound on the frequency of false negatives.
APA
Eliades, C. & Papadopoulos, H.. (2018). Detecting seizures in EEG recordings using conformal prediction. Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 91:171-186 Available from https://proceedings.mlr.press/v91/eliades18a.html.

Related Material