Circadian Profile and Seizure Forecasting: Still Cloudy but with Chance for Sunshine

Lee, Jong Woo

doi:10.5698/1535-7597.18.1.37

Cited by 1 publication

(1 citation statement)

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“…Studying periodicities in seizure patterns has revealed that more than 90% of individuals experience circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles (Foundation, 2016 ; Stirling et al, 2021 ). Such results provide insight into new approaches to improving seizure prediction algorithms and serve as proofs-of-concept for implementing a circadian forecasting framework (Lee, 2018 ). Over the past decades, seizure forecasting has seen a lot of progress, including improvements in algorithms using machine learning and exploration of other seizure susceptibility measures besides EEG, such as physiological biomarkers, behavioral changes, environmental factors, and cyclic seizure patterns (Stirling et al, 2021 ).…”

Section: Approachmentioning

confidence: 99%

A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy

Tao

Lhatoo

et al. 2022

Front. Big Data

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Epilepsy affects ~2–3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.

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Section: Approachmentioning

confidence: 99%