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Detection Performance and Risk Stratification Using a Model-Based Shape Index Characterizing Heart Rate Turbulence

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Abstract

A detection–theoretic approach to quantify heart rate turbulence (HRT) following a ventricular premature beat is proposed and validated using an extended integral pulse frequency modulation (IPFM) model which accounts for HRT. The modulating signal of the extended IPFM model is projected into a three-dimensional subspace spanned by the Karhunen–Loève basis functions, characterizing HRT shape. The presence or absence of HRT is decided by means of a likelihood ratio test, the Neyman–Pearson detector, resulting in a quadratic detection statistic. Using a labeled dataset built from different interbeat interval series, detection performance is assessed and found to outperform the two widely used indices: turbulence onset (TO) and turbulence slope (TS). The ability of the proposed method to predict the risk of cardiac death is evaluated in a population of patients (n = 90) with ischemic cardiomyopathy and mild-to-moderate congestive heart failure. While both TS and the novel HRT index differ significantly in survivors and cardiac death patients, mortality analysis shows that the latter index exhibits much stronger association with risk of cardiac death (hazard ratio = 2.8, CI = 1.32–5.97, p = 0.008). It is also shown that the model-based shape indices, but not TO and TS, remain predictive of cardiac death in our population when computed from 4-h instead of 24-h ambulatory ECGs.

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Acknowledgments

This study was supported by Projects TEC-2007-68076-C02-02 from CICYT, GTC T-30 from DGA (Spain), CIBER de Bioingeniería, Biomateriales y Nanomedicina, an initiative of the Instituto de Salud Carlos III, and the Swedish Research Council.

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Authors and Affiliations

  1. Communications Technology Group (GTC), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Aragon, Spain

    Juan Pablo Martínez & Pablo Laguna

  2. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Aragon, Spain

    Juan Pablo Martínez & Pablo Laguna

  3. Department of Electrocardiology, Medical University of Lodz, Lodz, Poland

    Iwona Cygankiewicz

  4. Institut Català de Ciències Cardiovasculars, Barcelona, Catalonia, Spain

    Iwona Cygankiewicz & Antonio Bayés de Luna

  5. Signal Processing Group, Department of Electrical and Information Technology, Lund University, Lund, Sweden

    Danny Smith & Leif Sörnmo

  6. Center of Integrative Electrocardiology at Lund University (CIEL), Lund, Sweden

    Danny Smith & Leif Sörnmo

Authors
  1. Juan Pablo Martínez
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  2. Iwona Cygankiewicz
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  3. Danny Smith
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  4. Antonio Bayés de Luna
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  5. Pablo Laguna
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  6. Leif Sörnmo
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Corresponding author

Correspondence to Juan Pablo Martínez.

Additional information

Associate Editor Ioannis A. Kakadiaris oversaw the review of this article.

Appendix

Appendix

The model parameters \({\varvec{\mu}}, {\varvec{\Sigma}}_0,\) and \({\varvec{\Sigma}}_1,\) estimated from datasets \({{\mathcal{S}}}_{0{\text{tr}}}\) and \({{\mathcal{S}}}_{1{\text{tr}}},\) and used for computing \(T_{\varvec{\mu}}({{\mathbf{x}}})\) and \(T_{\varvec{\Sigma}}({{\mathbf{x}}})\) have the following values:

$$ {\varvec{\mu}} = 10^{-3} \left[ \begin{array}{lll} -34.699 & 107.246 & 0.112 \end{array}\right]^{\text{T}} $$
(8)
$$ {\varvec{\Sigma}}_0 = 10^{-3} \left[ \begin{array}{rrr} 8.061& 0.932&-0.368 \\ 0.932 & 1.442& 0.330\\ -0.368 & 0.330& 1.103 \end{array}\right] $$
(9)
$$ {\varvec{\Sigma}}_1 = 10^{-3} \left[ \begin{array}{rrr} 32.262 & 3.726&0.004\\ 3.726 & 6.603 & -0.012 \\ 0.004 & -0.012 & 5.454 \end{array}\right] $$
(10)

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Martínez, J.P., Cygankiewicz, I., Smith, D. et al. Detection Performance and Risk Stratification Using a Model-Based Shape Index Characterizing Heart Rate Turbulence. Ann Biomed Eng 38, 3173–3184 (2010). https://doi.org/10.1007/s10439-010-0081-8

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  • DOI: https://doi.org/10.1007/s10439-010-0081-8

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