Accurate detection of spontaneous seizures using a generalized linear model with external validation. (6th August 2020)

• Record Type: Journal Article
• Authors: Fumeaux, Nicolas F.; Ebrahim, Senan; Coughlin, Brian F.; Kadambi, Adesh; Azmi, Aafreen; Xu, Jen X.; Abou Jaoude, Maurice; Nagaraj, Sunil B.; Thomson, Kyle E.; Newell, Thomas G.; Metcalf, Cameron S.; Wilcox, Karen S.; Kimchi, Eyal Y.; Moraes, Marcio F. D.; Cash, Sydney S.
• Is Part Of: Epilepsia. Volume 61:issue 9(2020)
• Page Start: 1906
• Page End: 1918
• Publication Date: 2020-08-06
• Subjects: focal epilepsy -- machine learning -- model validation -- quantitative EEG -- seizure detection; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16628
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: 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.