Automated detection of activity onset after postictal generalized EEG suppression

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dc.citation.articleNumber327en_US
dc.citation.journalTitleBMC Medical Informatics and Decision Makingen_US
dc.citation.volumeNumber20en_US
dc.contributor.authorLamichhane, Bishalen_US
dc.contributor.authorKim, Yejinen_US
dc.contributor.authorSegarra, Santiagoen_US
dc.contributor.authorZhang, Guoqiangen_US
dc.contributor.authorLhatoo, Samdenen_US
dc.contributor.authorHampson, Jaisonen_US
dc.contributor.authorJiang, Xiaoqianen_US
dc.date.accessioned2021-02-08T18:38:01Zen_US
dc.date.available2021-02-08T18:38:01Zen_US
dc.date.issued2020en_US
dc.description.abstractBackground: Sudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization. Methods: We address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording. Results: We evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES. Conclusion: Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES—otherwise difficult to detect due to EEG noise and artifacts—using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.en_US
dc.identifier.citationLamichhane, Bishal, Kim, Yejin, Segarra, Santiago, et al.. "Automated detection of activity onset after postictal generalized EEG suppression." <i>BMC Medical Informatics and Decision Making,</i> 20, (2020) BioMed Central: https://doi.org/10.1186/s12911-020-01307-7.en_US
dc.identifier.digitals12911-020-01307-7en_US
dc.identifier.doihttps://doi.org/10.1186/s12911-020-01307-7en_US
dc.identifier.urihttps://hdl.handle.net/1911/109825en_US
dc.language.isoengen_US
dc.publisherBioMed Centralen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/.en_US
dc.titleAutomated detection of activity onset after postictal generalized EEG suppressionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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