Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy

  • Claudia Carricarte-Naranjo
  • David J. Cornforth
  • Lazaro M. Sanchez-Rodriguez
  • Marta Brown
  • Mario Estévez
  • Andres Machado
  • Herbert F. Jelinek
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 65)

Abstract

Cardiac autonomic neuropathy (CAN) is a complication of diabetes with a long asymptomatic phase that is associated with high morbidity and mortality. Early identification of CAN in Type 1 diabetes mellitus (T1DM) may be possible using heart rate variability (HRV). However, the power of HRV analysis to identify CAN depends on the selection of suitable features that provide reliable information regarding cardiac autonomic regulation. Our aim was to compare the performance of Rényi entropy (RE) and permutation entropy (PE) for identification of T1DM patients with CAN. RE and PE measures from 235 data points and 5 min of cardiac interbeat interval (RR) sequences were analysed in 18 T1DM patients without CAN, 14 T1DM patients with CAN, and healthy controls matched for age and sex. RE was calculated for different orders α (-5, 5), pattern lengths λ (2, 4, 8), and tolerance σ. For PE analysis λ was set to (3-4) and time delays τ to (1-10). A forward stepwise discriminant analysis was carried out for estimating the classification functions. Accuracy was estimated following a K-fold cross-validation (k = 14). RE calculated for RR sequences of λ = 2, α > 0 showed the best performance for differentiating T1DM patients with CAN (p < 0.0001). PE measures showed better performance with ordinal patterns and τ = 4, 5 and 7 for differentiating patients with CAN. RE and PE provide complementary information achieving 100% classification accuracy (p < 0.0001 and p < 0.001, respectively). This approach might be promising as a sensitive and specific tool for CAN diagnosis in T1DM.

Keywords

Rényi entropy permutation entropy ordinal patterns heart rate variability cardiac autonomic neuropathy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1. Boulton AJM, Vinik AI, Arezzo JC et al. (2005) Diabetic Neuropathies. A statement by the American Diabetes Association. Diabetes Care 28(4):956-962Google Scholar
  2. 2. Maser RE, Lenhard JM and DeCherney SG (2000) Cardiovascular autonomic neuropathy: the clinical significance of its determination. Endocrinologist 10:27-33Google Scholar
  3. 3. Vinik AI and Erbas T (2001) Recognizing and treating diabetic autonomic neuropathy. Cleve Clin J Med 68(11):928-944Google Scholar
  4. 4. Bandt C and Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88(17):174102Google Scholar
  5. 5. Jelinek HF, Tarvainen MP and Cornforth DJ (2012) Renyi entropy in identification of cardiac autonomic neuropathy in diabetes. Computing in Cardiology 39:909-911Google Scholar
  6. 6. Sanchez‐Rodriguez L, Carricarte C, Machado A et al. (2016) Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy, Canadian Medical and Biological Engineering Conference (CMBEC), Alberta, Canada, 2016Google Scholar
  7. 7. Cornforth D, Jelinek H and Tarvainen M (2015) A comparison of nonlinear measures for the detection of cardiac autonomic neuropathy from heart rate variability. Entropy 17(3):1425-1440Google Scholar
  8. 8. Machado A, Migliaro ER, Contreras P et al. (2000) Automatic filtering of RR intervals for heart rate variability analysis. A N E. 5(3):255-261Google Scholar
  9. 9. Cornforth DJ, Tarvainen MP and Jelinek HF (2013) Using Renyi entropy to detect early cardiac autonomic neuropathy. Conf Proc IEEE Eng Med Biol Soc 2013:5562-5Google Scholar
  10. 10. Cornforth DJ, Tarvainen MP and Jelinek HF (2014) How to calculate Renyi entropy from heart rate variability, and why it matters for detecting cardiac autonomic neuropathy. Front Bioeng Biotechnol 2(34):1-8Google Scholar
  11. 11. Parlitz U, Berg S, Luther S et al. (2012) Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics. Comput Biol Med 42(3):319-327Google Scholar
  12. 12. Jelinek HJ, Alothman D, Cornforth DJ et al. (2014) Effect of biosignal preprocessing and recording length on clinical decision making for cardiac autonomic neuropathy, 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGO 2014), 2014Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Claudia Carricarte-Naranjo
    • 1
  • David J. Cornforth
    • 2
  • Lazaro M. Sanchez-Rodriguez
    • 3
  • Marta Brown
    • 4
  • Mario Estévez
    • 5
  • Andres Machado
    • 1
  • Herbert F. Jelinek
    • 6
  1. 1.Faculty of BiologyUniversity of HavanaHavanaCuba
  2. 2.Applied Informatics Research GroupUniversity of NewcastleNewcastleAustralia
  3. 3.Biomedical Engineering Graduate ProgramUniversity of CalgaryCalgaryCanada
  4. 4.Cuban Neuroscience CenterHavanaCuba
  5. 5.Institute of Neurology and NeurosurgeryHavanaCuba
  6. 6.School of Community HealthCharles Sturt UniversityAlburyAustralia

Personalised recommendations