Optimization of Euclidean distance threshold in the application of recurrence quantification analysis to heart rate variability studies

Ding, H., Crozier, S. and Wilson, S.J. (2008) Optimization of Euclidean distance threshold in the application of recurrence quantification analysis to heart rate variability studies. Chaos Solitons & Fractals, 38 5: 1457-1467. doi:10.1016/j.chaos.2006.07.059


Author Ding, H.
Crozier, S.
Wilson, S.J.
Title Optimization of Euclidean distance threshold in the application of recurrence quantification analysis to heart rate variability studies
Journal name Chaos Solitons & Fractals   Check publisher's open access policy
ISSN 0960-0779
Publication date 2008-12-01
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.chaos.2006.07.059
Open Access Status DOI
Volume 38
Issue 5
Start page 1457
End page 1467
Total pages 11
Editor El Naschie, M.S.
Place of publication United Kingdom
Publisher Pergmon-Elsevier Science Ltd
Language eng
Subject C1
0903 Biomedical Engineering
970109 Expanding Knowledge in Engineering
920103 Cardiovascular System and Diseases
Abstract An integrated approach is proposed to solve the optimization problem of the Euclidean distance threshold ε in recurrence quantification analysis (RQA), which is increasingly applied in the study of heart rate variability (HRV). In this paper, ε is inversely computed from a given recurrence rate (REC), the percentage of recurrence points. From the inversely computed ε, two other RQA output variables: determinism (DET), the percentage of recurrence points forming diagonal line structures, and laminarity (LAM), the percentage of recurrence points forming vertical and horizontal structures, are computed out as well. The trend of DET, LAM values at different REC levels (DLR trend) is introduced to comprehensively represent the dynamic properties of a time series. Based on the DLR trend, the variation of discrimination power, represented by the average loss (or Bayes risk), of DET and LAM, at different REC values is analyzed. Surrogate techniques are used to generate reliable test data sets for the discrimination evaluation. In particular, the results show that (1) the optimal REC can be much higher than the widely used 1% REC, and (2) after the optimization, the average loss can be reduced compared to 1% REC. It is also demonstrated that the optimal ε depends on the dynamic source and RQA variables, and the DLR trend based ε optimization method can improve RQA discrimination analysis especially for the short term HRV analysis.
Keyword Euclidean Distance
Recurrence Quantification Analysis
Heart Rate Variability
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 8 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 14 Apr 2009, 02:31:45 EST by Ms Kimberley Nunes on behalf of School of Information Technol and Elec Engineering