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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Complex correlation measure: a novel descriptor for Poincaré plot.

Chandan K Karmakar1, Ahsan H Khandoker, Jayavardhana Gubbi

  • 1Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia. c.karmakar@ee.unimelb.edu.au

Biomedical Engineering Online
|August 14, 2009
PubMed
Summary

A new Complex Correlation Measure (CCM) quantifies temporal aspects of Poincaré plots, outperforming standard descriptors like SD1 and SD2 in detecting heart conditions such as arrhythmia and congestive heart failure (CHF). This advanced method offers improved sensitivity for analyzing heart rate variability.

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Area of Science:

  • Cardiology and Biomedical Engineering
  • Signal Processing and Time Series Analysis

Background:

  • Poincaré plots are crucial for visualizing heart rate variability (HRV) and its nonlinear dynamics.
  • Existing quantitative measures (SD1, SD2) capture gross variability but lack temporal detail.
  • Quantifying temporal properties of Poincaré plots presents a significant challenge in HRV analysis.

Purpose of the Study:

  • To introduce a novel descriptor, the Complex Correlation Measure (CCM), for quantifying the temporal information in Poincaré plots.
  • To evaluate the sensitivity and efficacy of CCM compared to traditional HRV descriptors.

Main Methods:

  • Derived mathematical expressions for the Complex Correlation Measure (CCM).
  • Assessed descriptor sensitivity using signal surrogation techniques.
  • Constructed lag-1 Poincaré plots for subjects with Arrhythmia, Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR).
  • Computed CCM, SD1, and SD2, followed by ANOVA analysis to determine statistical significance.

Main Results:

  • CCM quantifies the correlation structure of Poincaré plots by analyzing time series autocorrelation at various lags.
  • Surrogate analysis demonstrated higher sensitivity for CCM compared to SD1 and SD2.
  • CCM significantly discriminated between arrhythmia and NSR subjects (p = 6.28E-18) and CHF and NSR subjects (p = 9.07E-14).

Conclusions:

  • The Complex Correlation Measure (CCM) serves as a valuable addition to Poincaré plot analysis.
  • CCM enhances the detection of cardiac pathologies by providing a more sensitive measure of temporal dynamics.