NDLI: Do nonlinearities play a significant role in short term, beat-to-beat variability?

Content Provider	IEEE Xplore Digital Library
Author	Choi, H.G. Mukkamala, R. Moody, G.B. Mark, R.G.
Copyright Year	2001
Description	Author affiliation: Kumoh Nat. Univ. of Tech., Kumi, South Korea (Choi, H.G.)
Abstract	Numerous studies of short-term beat-to-beat variability in cardiovascular signals have not resolved the debate about the completeness of linear analysis techniques. This paper further evaluates the role of nonlinearities in short-term beat-to-beat variability. We compared linear autoregressive moving average (ARMA) and nonlinear neural network (NN) models for predicting instantaneous heart rate (HR) and mean arterial blood pressure (BP) from past HR and BP. To evaluate these models, we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that ARMA linear analysis techniques provide adequate characterization of the system dynamics responsible for generating short-term beat-to-beat variability.
Starting Page	53
Ending Page	56
File Size	404187
Page Count	4
File Format	PDF
ISBN	0780372662
ISSN	02766547
DOI	10.1109/CIC.2001.977589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-09-23
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart rate Neural networks Autoregressive processes Predictive models Cardiology Signal resolution Signal analysis Arterial blood pressure Databases Nonlinear dynamical systems
Content Type	Text
Resource Type	Article

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