Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00346889" target="_blank" >RIV/68407700:21730/21:00346889 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1111/epi.16785" target="_blank" >https://doi.org/10.1111/epi.16785</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/epi.16785" target="_blank" >10.1111/epi.16785</a>

Jazyk výsledku
angličtina
Název v původním jazyce
Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast
Popis výsledku v původním jazyce
Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors,such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.
Název v anglickém jazyce
Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast
Popis výsledku anglicky
Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors,such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.

Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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