A matching pursuit algorithm for inferring tonic sympathetic arousal from spontaneous skin conductance fluctuations
Tonic sympathetic arousal is often inferred from spontaneous fluctuations in skin conductance, and this relies on assumptions about the shape of these fluctuations and how they are generated. We have previously furnished a psychophysiological model for this relation, and an efficient and reliable in...
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Published in | Psychophysiology Vol. 52; no. 8; pp. 1106 - 1112 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
United States
Blackwell Publishing Ltd
01.08.2015
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Tonic sympathetic arousal is often inferred from spontaneous fluctuations in skin conductance, and this relies on assumptions about the shape of these fluctuations and how they are generated. We have previously furnished a psychophysiological model for this relation, and an efficient and reliable inversion method to estimate tonic arousal from given data in the framework of dynamic causal modeling (DCM). Here, we provide a fast alternative inversion method in the form of a matching pursuit (MP) algorithm. Analyzing simulated data, this algorithm approximates the true underlying arousal up to about 10 spontaneous fluctuations per minute of data. For empirical data, we assess predictive validity as the ability to differentiate two known psychological arousal states. Predictive validity is comparable between the methods for three datasets, and also comparable to visual peak scoring. Computation time of the MP algorithm is 2–3 orders of magnitude faster for the MP than the DCM algorithm. In summary, the new MP algorithm provides a fast and reliable alternative to DCM inversion for SF data, in particular when the expected number of fluctuations is lower than 10 per minute, as in typical experimental situations. |
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Bibliography: | Wellcome Trust - No. 091593/Z/10/Z ark:/67375/WNG-DHKL6BM5-0 Swiss National Science Foundation - No. 320030_149586/1 ArticleID:PSYP12434 The Wellcome Trust Centre for Neuroimaging University of Zurich istex:6EC474326D2679DF2BAB35A1A312C0172208EC37 This work was funded by the Swiss National Science Foundation (320030_149586/1) and the University of Zurich. The Wellcome Trust Centre for Neuroimaging is supported by a strategic grant from the Wellcome Trust (091593/Z/10/Z). The authors thank Athina Tzovara for inspiring comments on a first draft of this manuscript. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0048-5772 1540-5958 1469-8986 |
DOI: | 10.1111/psyp.12434 |