Digital Semiology: A Prototype for Standardized, Computer-Based Semiologic Encoding of Seizures Article Swipe
Tal Benoliel
,
Tal Gilboa
,
Paz Har-Shai Yahav
,
Revital Zelker
,
Bilha Kreigsberg
,
Evgeny Tsizin
,
Oshrit Arviv
,
Dana Ekstein
,
Mordekhay Medvedovsky
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fneur.2021.711378
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fneur.2021.711378
Video-EEG monitoring (VEM) is imperative in seizure classification and presurgical assessment of epilepsy patients. Analysis of VEM is currently performed in most institutions using a freeform report, a time-consuming process resulting in a non-standardized report, limiting the use of this essential diagnostic tool. Herein we present a pilot feasibility study of our experience with “Digital Semiology” (DS), a novel seizure encoding software. It allows semiautomated annotation of the videos of suspected events from a predetermined, hierarchal set of options, with highly detailed semiologic descriptions, somatic localization, and timing. In addition, the software's semiologic extrapolation functions identify characteristics of focal seizures and PNES, sequences compatible with a Jacksonian march, and risk factors for SUDEP. Sixty episodes from a mixed adult and pediatric cohort from one level 4 epilepsy center VEM archives were analyzed using DS and the reports were compared with the standard freeform ones, written by the same epileptologists. The behavioral characteristics appearing in the DS and freeform reports overlapped by 78–80%. Encoding of one episode using DS required an average of 18 min 13 s (standard deviation: 14 min and 16 s). The focality function identified 19 out of 43 focal episodes, with a sensitivity of 45.45% (CI 30.39–61.15%) and specificity of 87.50% (CI 61.65–98.45%). The PNES function identified 6 of 12 PNES episodes, with a sensitivity of 50% (95% CI 21.09–78.91%) and specificity of 97.2 (95% CI 88.93–99.95%). Eleven events of GTCS triggered the SUDEP risk alert. Overall, these results show that video recordings of suspected seizures can be encoded using the DS software in a precise manner, offering the added benefit of semiologic alerts. The present study represents an important step toward the formation of an annotated video archive, to be used for machine learning purposes. This will further the goal of automated VEM analysis, ultimately contributing to wider utilization of VEM and therefore to the reduction of the treatment gap in epilepsy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fneur.2021.711378
- OA Status
- gold
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3202910814
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3202910814Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fneur.2021.711378Digital Object Identifier
- Title
-
Digital Semiology: A Prototype for Standardized, Computer-Based Semiologic Encoding of SeizuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-05Full publication date if available
- Authors
-
Tal Benoliel, Tal Gilboa, Paz Har-Shai Yahav, Revital Zelker, Bilha Kreigsberg, Evgeny Tsizin, Oshrit Arviv, Dana Ekstein, Mordekhay MedvedovskyList of authors in order
- Landing page
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https://doi.org/10.3389/fneur.2021.711378Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fneur.2021.711378Direct OA link when available
- Concepts
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Semiology, Epilepsy, Cohort, Medicine, Limiting, Audiology, Pediatrics, Psychology, Psychiatry, Internal medicine, Mechanical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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