Skip to main content
SpringerLink
Log in

Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone–Entropy

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone–Entropy (T–E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN−) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50–900) and lags (m: 1–8). The CAN− group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN− than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T–E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T–E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T–E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Alam I, Lewis MJ, Morgan J, Baxter J (2009) Linear and nonlinear characteristics of heart rate time series in obesity and during weight-reduction surgery. Physiol Meas 30:541–557

    Article  PubMed  CAS  Google Scholar 

  2. Amano M, Oida E, Moritani T (2005) Age-associated alteration of sympatho-vagal balance in a female population assessed through the tone–entropy analysis. Eur J Appl Physiol 94:602–610

    Article  PubMed  Google Scholar 

  3. Bootsma M, Swenne CA, Van Bolhuis HH, Chang PC, Cats VM, Bruschke AV (1994) Heart rate and heart rate variability as indexes of sympathovagal balance. Am J Physiol 266:H1565–H1571

    PubMed  CAS  Google Scholar 

  4. Cavalcanti S, Belardinelli E (1996) Modeling of cardiovascular variability using a differential delay equation. IEEE Trans Biomed Eng 43(10):982–989

    Article  PubMed  CAS  Google Scholar 

  5. Chan K, Lee TW, Sample PA, Goldbaum MH, Weinreb RN, Sejnowski TJ (2002) Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans Biomed Eng 49:963–974

    Article  PubMed  Google Scholar 

  6. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Earlbaum Associates, Hillsdale

    Google Scholar 

  7. Costa M, Goldberger AL, Peng CK (2005) Multiscale entropy analysis of biological signals. Physical Review E 71

  8. Ewing DJ, Clarke BF (1982) Diagnosis and management of diabetic autonomic neuropathy. Br Med J 285:916–918

    Article  CAS  Google Scholar 

  9. Ewing DJ, Martyn CM, Young RJ, Clarke BF (1985) The value of cardiovascular autonomic function tests: 10 years experience in diabetes. Diabetes Care 8:491–493

    Article  PubMed  CAS  Google Scholar 

  10. Flynn AC, Jelinek HF, Smith MC (2005) Heart rate variability analysis: a useful assessment tool for diabetes associated cardiac dysfunction in rural and remote areas. Aust J Rural Health 13:77–82

    Article  PubMed  Google Scholar 

  11. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    PubMed  CAS  Google Scholar 

  12. Huikuri HV, Perkiomaki JS, Maestri R, Pinna GD (2009) Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics. Philos Transact A Math Phys Eng Sci 367:1223–1238

    Article  PubMed  Google Scholar 

  13. Javorka M, Trunkvalterova Z, Tonhajzerova I, Javorkova J, Javorka K, Baumert M (2008) Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus. Clin Neurophysiol 119:1071–1081

    Article  PubMed  Google Scholar 

  14. Jelinek HF, Pham P, Struzik ZR, Spence I (2007) Short term ECG recording for the identification of cardiac autonomic neuropathy in people with diabetes mellitus. In: Proceedings of the 19th International Conference on Noise and Fluctuations, Tokyo, Japan, 683–686

  15. Khandoker AH, Jelinek HF, Palaniswami M (2009) Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. BioMed Eng Online 8:3

    Article  PubMed  Google Scholar 

  16. Khandoker AH, Jelinek H, Moritani T, Palaniswami M (2010) Association of cardiac autonomic neuropathy with alteration of sympatho-vagal balance through heart rate variability analysis. Med Eng Phys 32:161–167

    Article  PubMed  Google Scholar 

  17. Krolewski AS, Czyzyk A, Janeczko D, Kopezynski J (1977) Mortality from cardiovascular diseases among diabetics. Diabetologia 13:345–350

    Article  PubMed  CAS  Google Scholar 

  18. Krstacic G, Krstacic A, Smalcelj A, Milicic D, Jembrek-Gostovic M (2007) The “Chaos Theory” and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease? Ann Noninvasive Electrocardiol 12:130–136

    Article  PubMed  Google Scholar 

  19. Lerma C, Infante O, Perez-Grovas H, Jose MV (2003) Poincarè plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. Clin Physiol Funct Imaging 23(2):72–80

    Article  PubMed  Google Scholar 

  20. Lombardi F, Makikallio TH, Myerburg RJ, Huikuri HV (2001) Sudden cardiac death: role of heart rate variability to identify patients at risk. Cardiovasc Res 50:210–217

    Article  PubMed  CAS  Google Scholar 

  21. Martinez-Garcia P, Lerma C, Infante O (2012) Baroreflex sensitivity estimation by the sequence method with delayed signals. Clinical autonomic research (Epub ahead of print)

  22. Oida E, Moritani T, Yamori Y (1997) Tone–entropy analysis on cardiac recovery after dynamic exercise. J Appl Physiol 82:1794–1801

    PubMed  CAS  Google Scholar 

  23. Oida E, Kannagi T, Moritani T, Yamori Y (1999) Aging alteration of cardiac vagosympathetic balance assessed through the tone–entropy analysis. J Gerontol 54A:M219–M224

    Google Scholar 

  24. Ottesen JT (1996) Modelling of the baroreflex-feedback mechanism with time-delay. J Math Biol 36(1):41–63

    Article  Google Scholar 

  25. Pagani M (2000) Heart rate variability and autonomic diabetic neuropathy. Diabetes Nutr Metab 13(6):341–346

    PubMed  CAS  Google Scholar 

  26. Pan J, Tompkins WJ (1985) Real time QRS detector algorithm. IEEE Trans Biomed Eng 32:230–323

    Article  PubMed  CAS  Google Scholar 

  27. Pang CCC, Upton ARM, Shine G, Kamath MV (2003) A comparison of algorithms for detection of spikes in the electroencephalogram. IEEE Trans Biomed Eng 50:521–526

    Article  PubMed  Google Scholar 

  28. Ripley BD (1996) Pattern recognition and neural networks. Cambridge Univ Press, Cambridge

    Google Scholar 

  29. Rollins MD, Jenkins JG, Carson DJ, McClure BG, Mitchell RH, Imam SZ (1992) Power spectral analysis of the electrocardiogram in diabetic children. Diabetologia 35:452–455

    Article  PubMed  CAS  Google Scholar 

  30. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Google Scholar 

  31. Shi P, Zhu Y, Allen J, Hu S (2009) Analysis of pulse rate variability derived from photoplethysmography with the combination of lagged Poincaré plots and spectral characteristics. Med Eng Phys 31(7):866–871

    Article  PubMed  Google Scholar 

  32. Spallone V, Menzinger G (1997) Diagnosis of cardiovascular autonomic neuropathy in diabetes. Diabetes 46:S67

    PubMed  CAS  Google Scholar 

  33. The diabetes control and complications trial/epidemiology of diabetes interventions and complications research group (2005) Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med 353:2643–2653

    Article  Google Scholar 

  34. Tomiyama O, Shiigai T, Ideura T, Tomita K, Mito Y, Shinohara S, Takeuchi J (1980) Baroreflex sensitivity in renal-failure. Clin Sci 58(1):21–27

    PubMed  CAS  Google Scholar 

  35. Tulppo MP, Hughson RL, Makikallio TH, Airaksinen KE, Seppanen T, Huikuri HV (2001) Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics. Am J Physiol Heart Circ Physiol 280(3):H1081–H1087

    PubMed  CAS  Google Scholar 

  36. Vinik AI, Ziegler D (2007) Diabetic cardiovascular autonomic neuropathy. Circulation 115:387–397

    Article  PubMed  Google Scholar 

  37. Ziegler D (1994) Diabetic cardiovascular autonomic neuropathy: prognosis, diagnosis and treatment. Diabetes Metab Rev 10:339–383

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Melbourne, VIC, 3010, Australia

    C. K. Karmakar, A. H. Khandoker & M. Palaniswami

  2. Department of Biomedical Engineering, Khalifa University of Science, Technology and Research, Abu Dhabi, UAE

    A. H. Khandoker

  3. Centre for Research in Complex Systems and School of Community Health, Charles Sturt University, Albury, NSW, 2640, Australia

    H. F. Jelinek

Authors
  1. C. K. Karmakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. H. Khandoker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. F. Jelinek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Palaniswami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. K. Karmakar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Karmakar, C.K., Khandoker, A.H., Jelinek, H.F. et al. Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone–Entropy. Med Biol Eng Comput 51, 537–546 (2013). https://doi.org/10.1007/s11517-012-1022-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-012-1022-5

Keywords

Search

Navigation