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

C. K. Karmakar, A. H. Khandoker, H. F. Jelinek, M. Palaniswami

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageBritish English
Pages (from-to)537-546
Number of pages10
JournalMedical and Biological Engineering and Computing
Issue number5
StatePublished - May 2013


  • Cardiac autonomic neuropathy
  • Heart rate variability
  • Multi-lag
  • Tone-entropy


Dive into the research topics of 'Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone-Entropy'. Together they form a unique fingerprint.

Cite this