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Comput Biol Med. 2013 Nov;43(11):1661-72. doi: 10.1016/j.compbiomed.2013.08.004. Epub 2013 Aug 21.

Multi-Gaussian fitting for pulse waveform using Weighted Least Squares and multi-criteria decision making method.

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  • 1College of Information Science and Engineering, Northeastern University, Shenyang City, Liaoning Province, 110819, China.

Abstract

Analysis of pulse waveform is a low cost, non-invasive method for obtaining vital information related to the conditions of the cardiovascular system. In recent years, different Pulse Decomposition Analysis (PDA) methods have been applied to disclose the pathological mechanisms of the pulse waveform. All these methods decompose single-period pulse waveform into a constant number (such as 3, 4 or 5) of individual waves. Furthermore, those methods do not pay much attention to the estimation error of the key points in the pulse waveform. The estimation of human vascular conditions depends on the key points' positions of pulse wave. In this paper, we propose a Multi-Gaussian (MG) model to fit real pulse waveforms using an adaptive number (4 or 5 in our study) of Gaussian waves. The unknown parameters in the MG model are estimated by the Weighted Least Squares (WLS) method and the optimized weight values corresponding to different sampling points are selected by using the Multi-Criteria Decision Making (MCDM) method. Performance of the MG model and the WLS method has been evaluated by fitting 150 real pulse waveforms of five different types. The resulting Normalized Root Mean Square Error (NRMSE) was less than 2.0% and the estimation accuracy for the key points was satisfactory, demonstrating that our proposed method is effective in compressing, synthesizing and analyzing pulse waveforms.

Copyright © 2013 Elsevier Ltd. All rights reserved.

KEYWORDS:

Multi-Gaussian model; Multi-criteria decision making; Photoplethysmography; Pulse decomposition analysis; Pulse waveform

PMID:
24209911
[PubMed - indexed for MEDLINE]
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