Accurate detection of spontaneous seizures using a generalized linear model with external validation
Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed‐loop stimulation or optogenetic control of seizures. It is also of increased importance in high‐thro...
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Published in | Epilepsia (Copenhagen) Vol. 61; no. 9; pp. 1906 - 1918 |
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Main Authors | , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Wiley Subscription Services, Inc
01.09.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Objective
Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed‐loop stimulation or optogenetic control of seizures. It is also of increased importance in high‐throughput, robust, and reproducible pre‐clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high‐performance seizure‐detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes.
Methods
We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold‐out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures.
Results
From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave‐one‐out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held‐out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures.
Significance
This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high‐throughput analysis of large number of seizures. |
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Bibliography: | Fumeaux and Ebrahim contributed equally to this work. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 AUTHOR CONTRIBUTIONS SE, NFF, and SSC conceived and designed the experiments. SE, AK, BFC, MFDM, and EYK developed the experimental paradigm, built the data acquisition system, and conducted the primary experiments. SE and NFF wrote the first draft of the paper. NFF, SE, SBJ, AK, and AA analyzed the data. JXX conducted immunohistochemical stains and animal experiments. KT, TN, CSM, and KW created the Multifocal Epilepsy data set. All authors discussed the results and implications, and reviewed and commented on the manuscript. |
ISSN: | 0013-9580 1528-1167 1528-1167 |
DOI: | 10.1111/epi.16628 |