Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer

Chengyu Liu, Tao Zhuang, Lina Zhao, Faliang Chang, Changchun Liu, Shoushui Wei, Qiqiang Li, Dingchang Zheng

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    9 Citations (Scopus)
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    Abstract

    Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform
    modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and
    hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively.
    Original languageEnglish
    Article number923260
    Number of pages10
    JournalBioMed Research International
    Volume2014
    DOIs
    Publication statusPublished - 20 May 2014

    Bibliographical note

    Copyright © 2014 Chengyu Liu et al. This is an open access article distributed under the Creative Commons Attribution License,
    which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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