Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer
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. Howe...
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Series: | BioMed Research International |
Online Access: | http://dx.doi.org/10.1155/2014/923260 |
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doaj-2f8a99e12de745d385c794dc3e52a14d2020-11-24T23:28:47ZengHindawi LimitedBioMed Research International2314-61332314-61412014-01-01201410.1155/2014/923260923260Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm OptimizerChengyu Liu0Tao Zhuang1Lina Zhao2Faliang Chang3Changchun Liu4Shoushui Wei5Qiqiang Li6Dingchang Zheng7School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaInstitute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE2 4HH, UKChanges 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.http://dx.doi.org/10.1155/2014/923260 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chengyu Liu Tao Zhuang Lina Zhao Faliang Chang Changchun Liu Shoushui Wei Qiqiang Li Dingchang Zheng |
spellingShingle |
Chengyu Liu Tao Zhuang Lina Zhao Faliang Chang Changchun Liu Shoushui Wei Qiqiang Li Dingchang Zheng Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer BioMed Research International |
author_facet |
Chengyu Liu Tao Zhuang Lina Zhao Faliang Chang Changchun Liu Shoushui Wei Qiqiang Li Dingchang Zheng |
author_sort |
Chengyu Liu |
title |
Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer |
title_short |
Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer |
title_full |
Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer |
title_fullStr |
Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer |
title_full_unstemmed |
Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer |
title_sort |
modelling arterial pressure waveforms using gaussian functions and two-stage particle swarm optimizer |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2014-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2014/923260 |
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