Using Renyi entropy to detect early cardiac autonomic neuropathy

Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated arrhythmias and heart attack. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). An even more desirable option is to detect CAN in its early, preclinical stage, to improve treatment and outcomes. In previous work we have shown a difference in the Renyi spectrum between participants identified with well-defined CAN and controls. In this work we applied the multi-scale Renyi entropy for identification of early CAN in diabetes patients. Results suggest that Renyi entropy derived from a 20 minute, Lead-II ECG recording, forms a useful contribution to the detection of CAN even in the early stages of the disease. The positive α parameters (1 ≤ α ≤ 5) associated with the Renyi distribution indicated a significant difference (p < 0.00004) between controls and early CAN as well as definite CAN. This is a significant achievement given the simple nature of the information collected, and raises prospects of a simple screening test and improved outcomes of patients.