Foundations of Time Series Analysis.
Deep learning
EEG
Intracranial pressure
Machine learning
Time series
Journal
Acta neurochirurgica. Supplement
ISSN: 0065-1419
Titre abrégé: Acta Neurochir Suppl
Pays: Austria
ID NLM: 100962752
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
4
12
2021
pubmed:
5
12
2021
medline:
15
12
2021
Statut:
ppublish
Résumé
For almost a century, classical statistical methods including exponential smoothing and autoregression integrated moving averages (ARIMA) have been predominant in the analysis of time series (TS) and in the pursuit of forecasting future events from historical data. TS are chronological sequences of observations, and TS data are therefore prevalent in many aspects of clinical medicine and academic neuroscience. With the rise of highly complex and nonlinear datasets, machine learning (ML) methods have become increasingly popular for prediction or pattern detection and within neurosciences, including neurosurgery. ML methods regularly outperform classical methods and have been successfully applied to, inter alia, predict physiological responses in intracranial pressure monitoring or to identify seizures in EEGs. Implementing nonparametric methods for TS analysis in clinical practice can benefit clinical decision making and sharpen our diagnostic armory.
Identifiants
pubmed: 34862545
doi: 10.1007/978-3-030-85292-4_25
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
215-220Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.
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