NDLI: Quantifying cardiac parasympathetic and sympathetic function based on a weighted-principal component regression method

Content Provider	IEEE Xplore Digital Library
Author	Xiao, X. Mukkamala, R. Cohen, R.J.
Copyright Year	2004
Description	Author affiliation: Div. of Health Sci. & Technol., MIT, Cambridge, MA, USA (Xiao, X.)
Abstract	A quantitative evaluation of autonomic cardiovascular control is important in understanding basic pathophysiological mechanisms or for patient monitoring, treatment design and follow-up. Noninvasive techniques for this purpose have been the focus of many research endeavors. We previously proposed a method to extract pure parasympathetic and pure sympathetic indices based on the impulse response between instantaneous lung volume and heart rate. Identification of this impulse response involves a dual-input, single-output system in which one input interacts with the output in closed-loop. To identify this relatively complicated system, we propose here a new system identification technique based on a weighted-principal component regression method. Asymptotically, this technique implements model selection in the frequency domain. Therefore, in contrast to the conventional methods, it allows the data to play a significant role in determining candidate models. Moreover, the estimated model parameters reflect a trade-off between bias and variance to reach a relatively small mean squared prediction error. We employ experimental data to demonstrate that this technique is superior to a more traditional technique in terms of measuring cardiac autonomic indices.
Starting Page	3945
Ending Page	3948
File Size	880300
Page Count	4
File Format	PDF
ISBN	0780384393
DOI	10.1109/IEMBS.2004.1404102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cardiology Patient monitoring Medical treatment Noninvasive treatment Lungs Heart rate System identification Frequency domain analysis Parameter estimation Predictive models weighting Autonomic function SVD PCA
Content Type	Text
Resource Type	Article

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