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Complex correlation measure: a novel descriptor for Poincaré plot.

Karmakar CK, Khandoker AH, Gubbi J, Palaniswami M - Biomed Eng Online (2009)

Bottom Line: A surrogate analysis was performed, and the sensitivity of the proposed descriptor was found to be higher as compared to the standard descriptors.In case of assessing CHF subjects also against NSR, CCM was again found to be the most significant (p = 9.07E-14).Hence, CCM can be used as an additional Poincaré plot descriptor to detect pathology.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia. c.karmakar@ee.unimelb.edu.au

ABSTRACT

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 (SD1, SD2) 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 SD1 and SD2, 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 SD1 and SD2. ANOVA analysis distribution was used to define the level of significance of mean and variance of SD1, SD2 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 SD1 and SD2 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.

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Related in: MedlinePlus

Forming triangles in calculating CCM. Poincaré plot of RR interval time series for three different lags (m = {1, 2, 3}) are shown. It is obvious that for RR intervals with different lag plots give more insight of the signal than a single lag. Figure also shows formation of triangles by dotted lines connecting three consecutive points at different lags (m = {1, 2, 3}) for 20 RR intervals for calculating CCM. Sequence of points (RRn, RRn+m) are plotted, where RR ≡ {u1, u2, ......, uN}.
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Figure 3: Forming triangles in calculating CCM. Poincaré plot of RR interval time series for three different lags (m = {1, 2, 3}) are shown. It is obvious that for RR intervals with different lag plots give more insight of the signal than a single lag. Figure also shows formation of triangles by dotted lines connecting three consecutive points at different lags (m = {1, 2, 3}) for 20 RR intervals for calculating CCM. Sequence of points (RRn, RRn+m) are plotted, where RR ≡ {u1, u2, ......, uN}.

Mentions: As shown in equation sets 3 and 4, for any m, the descriptors SD1 and SD2 only indicate m lag correlation information of the plot. This essentially conveys overall behavior of the system completely neglecting its temporal variation. Figure 3 shows the Poincaré plot of RR interval time series for three different lags. From the figure, it is obvious that for any time series signal different lag plots give more insight of the signal than a single lag plot. Hence, to reflect temporal variation, we developed a descriptor to incorporate multiple lag correlation information, which we call as Complex Correlation Measure (CCM). The proposed descriptor is not only related to the standard descriptors, but it also embeds temporal information, which can be used in quantification of the temporal dynamics of the system.


Complex correlation measure: a novel descriptor for Poincaré plot.

Karmakar CK, Khandoker AH, Gubbi J, Palaniswami M - Biomed Eng Online (2009)

Forming triangles in calculating CCM. Poincaré plot of RR interval time series for three different lags (m = {1, 2, 3}) are shown. It is obvious that for RR intervals with different lag plots give more insight of the signal than a single lag. Figure also shows formation of triangles by dotted lines connecting three consecutive points at different lags (m = {1, 2, 3}) for 20 RR intervals for calculating CCM. Sequence of points (RRn, RRn+m) are plotted, where RR ≡ {u1, u2, ......, uN}.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2743693&req=5

Figure 3: Forming triangles in calculating CCM. Poincaré plot of RR interval time series for three different lags (m = {1, 2, 3}) are shown. It is obvious that for RR intervals with different lag plots give more insight of the signal than a single lag. Figure also shows formation of triangles by dotted lines connecting three consecutive points at different lags (m = {1, 2, 3}) for 20 RR intervals for calculating CCM. Sequence of points (RRn, RRn+m) are plotted, where RR ≡ {u1, u2, ......, uN}.
Mentions: As shown in equation sets 3 and 4, for any m, the descriptors SD1 and SD2 only indicate m lag correlation information of the plot. This essentially conveys overall behavior of the system completely neglecting its temporal variation. Figure 3 shows the Poincaré plot of RR interval time series for three different lags. From the figure, it is obvious that for any time series signal different lag plots give more insight of the signal than a single lag plot. Hence, to reflect temporal variation, we developed a descriptor to incorporate multiple lag correlation information, which we call as Complex Correlation Measure (CCM). The proposed descriptor is not only related to the standard descriptors, but it also embeds temporal information, which can be used in quantification of the temporal dynamics of the system.

Bottom Line: A surrogate analysis was performed, and the sensitivity of the proposed descriptor was found to be higher as compared to the standard descriptors.In case of assessing CHF subjects also against NSR, CCM was again found to be the most significant (p = 9.07E-14).Hence, CCM can be used as an additional Poincaré plot descriptor to detect pathology.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia. c.karmakar@ee.unimelb.edu.au

ABSTRACT

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 (SD1, SD2) 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 SD1 and SD2, 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 SD1 and SD2. ANOVA analysis distribution was used to define the level of significance of mean and variance of SD1, SD2 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 SD1 and SD2 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.

Show MeSH
Related in: MedlinePlus