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Complex correlation measure: a novel descriptor for Poincaré plot

Karmakar, Chandan K., Khandoker, Ahsan H., Gubbi, Jayavardhana and Palaniswami, Marimuthu 2009, Complex correlation measure: a novel descriptor for Poincaré plot, Biomedical engineering online, vol. 8, no. 1, pp. 1-12, doi: 10.1186/1475-925X-8-17.

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Title Complex correlation measure: a novel descriptor for Poincaré plot
Author(s) Karmakar, Chandan K.ORCID iD for Karmakar, Chandan K. orcid.org/0000-0003-1814-0856
Khandoker, Ahsan H.
Gubbi, Jayavardhana
Palaniswami, Marimuthu
Journal name Biomedical engineering online
Volume number 8
Issue number 1
Article ID 17
Start page 1
End page 12
Total pages 12
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2009-08
ISSN 1475-925X
Keyword(s) Aged
Algorithms
Arrhythmias, Cardiac
Data Interpretation, Statistical
Diagnosis, Computer-Assisted
Heart Rate
Humans
In Vitro Techniques
Middle Aged
Reproducibility of Results
Sensitivity and Specificity
Statistics as Topic
Summary Background
Poincaré plot is one of the important techniques used for visually representing the heart rate variability. It is valuable due to its ability to display nonlinear aspects of the data sequence. However, the problem lies in capturing temporal information of the plot quantitatively. The standard descriptors used in quantifying the Poincaré plot (SD 1, SD 2) measure the gross variability of the time series data. Determination of advanced methods for capturing temporal properties pose a significant challenge. In this paper, we propose a novel descriptor "Complex Correlation Measure (CCM)" to quantify the temporal aspect of the Poincaré plot. In contrast to SD 1 and SD 2, the CCM incorporates point-to-point variation of the signal.

Methods

First, we have derived expressions for CCM. Then the sensitivity of descriptors has been shown by measuring all descriptors before and after surrogation of the signal. For each case study, lag-1 Poincaré plots were constructed for three groups of subjects (Arrhythmia, Congestive Heart Failure (CHF) and those with Normal Sinus Rhythm (NSR)), and the new measure CCM was computed along with SD 1 and SD 2. ANOVA analysis distribution was used to define the level of significance of mean and variance of SD 1, SD 2 and CCM for different groups of subjects.

Results

CCM is defined based on the autocorrelation at different lags of the time series, hence giving an in depth measurement of the correlation structure of the Poincaré plot. A surrogate analysis was performed, and the sensitivity of the proposed descriptor was found to be higher as compared to the standard descriptors. Two case studies were conducted for recognizing arrhythmia and congestive heart failure (CHF) subjects from those with NSR, using the Physionet database and demonstrated the usefulness of the proposed descriptors in biomedical applications. CCM was found to be a more significant (p = 6.28E-18) parameter than SD 1 and SD 2 in discriminating arrhythmia from NSR subjects. In case of assessing CHF subjects also against NSR, CCM was again found to be the most significant (p = 9.07E-14).

Conclusion

Hence, CCM can be used as an additional Poincaré plot descriptor to detect pathology.
Language eng
DOI 10.1186/1475-925X-8-17
Field of Research 0903 Biomedical Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2009, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074270

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.