Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings

Abstract Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that ut...

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Autores principales:	Michael L. Martini, Aly A. Valliani, Claire Sun, Anthony B. Costa, Shan Zhao, Fedor Panov, Saadi Ghatan, Kanaka Rajan, Eric Karl Oermann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d773d42d7cf9413393bd341e9a21fbe5
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spelling	oai:doaj.org-article:d773d42d7cf9413393bd341e9a21fbe52021-12-02T14:15:53ZDeep anomaly detection of seizures with paired stereoelectroencephalography and video recordings10.1038/s41598-021-86891-y2045-2322https://doaj.org/article/d773d42d7cf9413393bd341e9a21fbe52021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86891-yhttps://doaj.org/toc/2045-2322Abstract Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients. Consensus labels from a panel of epileptologists were used to evaluate model efficacy. Self-supervised dynamic thresholding exhibited improvements in positive predictive value (PPV; difference: 39.0%; 95% CI 4.5–73.5%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.03) with similar sensitivity (difference: 14.3%; 95% CI − 21.7 to 50.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.42) compared to static thresholds. In some models, training on as little as 10 min of SEEG data yielded robust detection. Cross-testing experiments reduced PPV (difference: 56.5%; 95% CI 25.8–87.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.002), while multimodal detection significantly improved sensitivity (difference: 25.0%; 95% CI 0.2–49.9%; Wilcoxon–Mann–Whitney test; N = 14; p < 0.05). Self-supervised dynamic thresholding improved the efficacy of real-time seizure predictions. Multimodal models demonstrated potential to improve detection. These findings are promising for future deployment in epilepsy monitoring units to enable real-time seizure detection without annotated data and only minimal training time in individual patients.Michael L. MartiniAly A. VallianiClaire SunAnthony B. CostaShan ZhaoFedor PanovSaadi GhatanKanaka RajanEric Karl OermannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael L. Martini Aly A. Valliani Claire Sun Anthony B. Costa Shan Zhao Fedor Panov Saadi Ghatan Kanaka Rajan Eric Karl Oermann Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
description	Abstract Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients. Consensus labels from a panel of epileptologists were used to evaluate model efficacy. Self-supervised dynamic thresholding exhibited improvements in positive predictive value (PPV; difference: 39.0%; 95% CI 4.5–73.5%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.03) with similar sensitivity (difference: 14.3%; 95% CI − 21.7 to 50.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.42) compared to static thresholds. In some models, training on as little as 10 min of SEEG data yielded robust detection. Cross-testing experiments reduced PPV (difference: 56.5%; 95% CI 25.8–87.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.002), while multimodal detection significantly improved sensitivity (difference: 25.0%; 95% CI 0.2–49.9%; Wilcoxon–Mann–Whitney test; N = 14; p < 0.05). Self-supervised dynamic thresholding improved the efficacy of real-time seizure predictions. Multimodal models demonstrated potential to improve detection. These findings are promising for future deployment in epilepsy monitoring units to enable real-time seizure detection without annotated data and only minimal training time in individual patients.
format	article
author	Michael L. Martini Aly A. Valliani Claire Sun Anthony B. Costa Shan Zhao Fedor Panov Saadi Ghatan Kanaka Rajan Eric Karl Oermann
author_facet	Michael L. Martini Aly A. Valliani Claire Sun Anthony B. Costa Shan Zhao Fedor Panov Saadi Ghatan Kanaka Rajan Eric Karl Oermann
author_sort	Michael L. Martini
title	Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
title_short	Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
title_full	Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
title_fullStr	Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
title_full_unstemmed	Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
title_sort	deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d773d42d7cf9413393bd341e9a21fbe5
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Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings

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