Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-10

AUTHORS

Hung-Chih Chiu, Yen-Hung Lin, Men-Tzung Lo, Sung-Chun Tang, Tzung-Dau Wang, Hung-Chun Lu, Yi-Lwun Ho, Hsi-Pin Ma, Chung-Kang Peng

ABSTRACT

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress. More... »

PAGES

13315

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep13315

DOI

http://dx.doi.org/10.1038/srep13315

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043221717

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26286628


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HOW TO GET THIS DATA PROGRAMMATICALLY:

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Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/srep13315'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/srep13315'


 

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