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Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer

Lookup NU author(s): Dr Ding Chang Zheng


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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 9TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms 9CAPW 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 9MPSO), and the dynamic multiswarm particle swarm optimizer 9DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error 9MAE) 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.

Publication metadata

Author(s): Liu CY, Zhuang T, Zhao LN, Chang FL, Liu CC, Wei SS, Li QQ, Zheng DC

Publication type: Article

Publication status: Published

Journal: BioMed Research International

Year: 2014

Online publication date: 20/05/2014

Acceptance date: 27/04/2014

ISSN (print): 2314-6133

ISSN (electronic): 2314-6141

Publisher: Hindawi Publishing Corporation


DOI: 10.1155/2014/923260


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Funder referenceFunder name
51075243National Natural Science Foundation of China
61201049National Natural Science Foundation of China
201303102Postdoctoral Innovation Foundation of Shandong Province in China
2013M530323China Postdoctoral Science Foundation
61273277National Natural Science Foundation of China
BS2013DX029Excellent Young Scientist Awarded Foundation of Shandong Province in China