@inproceedings{d48c450e6e524d71aa5aaa5517fe32f4,
title = "A deep learning approach to phase-space analysis for seizure detection",
abstract = "Many epileptic patients do not respond to medication or surgery. Recent technology has demonstrated that closed-loop responsive neurostimulation therapy is a realistic treatment for epileptic patients. However, ambulatory care of epileptic patients requires a highly accurate automated seizure detection algorithm. In this research, we implement a method for epileptic seizure detection based on nonlinear phase space analysis and deep convolutional neural networks (CNN). The underlying dynamics of scalp electroencephalography (sEEG) are extracted through time delay embedding and phase-space reconstruction. These features are used for training a CNN with a regression output to predict time until seizure. In experiments using EEG data collected in clinical environments from forty patients, our deep learning approach achieved high accuracy in predicting time until seizure onset, with a root mean squared error (RMSE) of 14.1 minutes and adjusted R-squared of .95 on out of sample testing data.",
keywords = "Deep Learning, Epilepsy, Phase-Space, Seizure Detection",
author = "Patrick Luckett and Thomas Watts and McDonald, \{J. Todd\} and Lee Hively and Ryan Benton",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2019 ; Conference date: 07-09-2019 Through 10-09-2019",
year = "2019",
month = sep,
day = "4",
doi = "10.1145/3307339.3342131",
language = "English",
series = "ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics",
publisher = "Association for Computing Machinery, Inc",
pages = "190--196",
booktitle = "ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics",
}