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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

Sensitivity of descriptors with changed temporal structure. Sensitivity of all descriptors with change in temporal structure is shown. ΔSD1, ΔSD2 and ΔCCM are calculated using equations 14–16. Value of ΔCCM is much higher than ΔSD1 and ΔSD2 which indicates that CCM is much more sensitive than SD1 and SD2 to the changes in underlying temporal structure of the data.
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Figure 4: Sensitivity of descriptors with changed temporal structure. Sensitivity of all descriptors with change in temporal structure is shown. ΔSD1, ΔSD2 and ΔCCM are calculated using equations 14–16. Value of ΔCCM is much higher than ΔSD1 and ΔSD2 which indicates that CCM is much more sensitive than SD1 and SD2 to the changes in underlying temporal structure of the data.

Mentions: From the mathematical definition of CCM, we anticipated that CCM would be more sensitive to changes in temporal structure within the signal than the standard descriptors. In this study, the sensitivity is defined as the rate of change of the value due to the change in temporal structure of the signal. As shown in figure 4, value of ΔCCM is much higher than ΔSD1 and ΔSD2 which indicates that CCM is much more sensitive than SD1 and SD2 to changes in underlying temporal structure of the data. This supports the mathematical definition of CCM as a sensitive measure of temporal variation of the signal.


Complex correlation measure: a novel descriptor for Poincaré plot.

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

Sensitivity of descriptors with changed temporal structure. Sensitivity of all descriptors with change in temporal structure is shown. ΔSD1, ΔSD2 and ΔCCM are calculated using equations 14–16. Value of ΔCCM is much higher than ΔSD1 and ΔSD2 which indicates that CCM is much more sensitive than SD1 and SD2 to the changes in underlying temporal structure of the data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Sensitivity of descriptors with changed temporal structure. Sensitivity of all descriptors with change in temporal structure is shown. ΔSD1, ΔSD2 and ΔCCM are calculated using equations 14–16. Value of ΔCCM is much higher than ΔSD1 and ΔSD2 which indicates that CCM is much more sensitive than SD1 and SD2 to the changes in underlying temporal structure of the data.
Mentions: From the mathematical definition of CCM, we anticipated that CCM would be more sensitive to changes in temporal structure within the signal than the standard descriptors. In this study, the sensitivity is defined as the rate of change of the value due to the change in temporal structure of the signal. As shown in figure 4, value of ΔCCM is much higher than ΔSD1 and ΔSD2 which indicates that CCM is much more sensitive than SD1 and SD2 to changes in underlying temporal structure of the data. This supports the mathematical definition of CCM as a sensitive measure of temporal variation of the signal.

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