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  Linking acute stress and heart rate variability in daily life while accounting for physical activity: A machine learning approach

Jordan, B., Fourcade, A., Gaebler, M., Scherf, N., Loheit, A.-C., & Villringer, A. (2021). Linking acute stress and heart rate variability in daily life while accounting for physical activity: A machine learning approach. Journal of Computational Neuroscience, 49(Suppl. 1), S193-S194.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-E982-C Version Permalink: https://hdl.handle.net/21.11116/0000-000C-3785-0
Genre: Meeting Abstract

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 Creators:
Jordan, Benedikt, Author
Fourcade, Antonin1, Author           
Gaebler, Michael1, Author           
Scherf, Nico2, Author           
Loheit, Anne-Christin, Author
Villringer, Arno1, Author           
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3282987              

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Language(s): eng - English
 Dates: 2021-12
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: WOS:000736099800223
 Degree: -

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Title: Journal of Computational Neuroscience
Source Genre: Journal
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Affiliations:
Publ. Info: Boston : Kluwer Academic Publishers
Pages: - Volume / Issue: 49 (Suppl. 1) Sequence Number: - Start / End Page: S193 - S194 Identifier: ISSN: 0929-5313
CoNE: https://pure.mpg.de/cone/journals/resource/954925568787