Short-term ECG recording for the Identification of cardiac autonomic neuropathy in people with diabetes mellitus

Diabetes mellitus is a serious and increasing health problem world-wide. An increased risk for all cardiovascular disease compared to non-diabetic patients including dysfunctional neural control of the heart. The clinical manifestations of cardiac autonomic neuropathy (CAN) include heart rate variability. Poor diagnoses of CAN may result in increased incidence of silent myocardial infarction and ischemia, which can lead to sudden death. This study examined the usefulness of HRV analyses of short ECG recordings as a method for detecting CAN utilizing the traditional Ewing battery as a standard for identification of CAN. Several HRV parameters were assessed including time and frequency domain as well as nonlinear parameters. The advantage of the newer nonlinear HRV measures such as approximate entropy (ApEn) is that they are model independent, suitable for nonlinear processes, and measure aspects of HRV different from the traditional methods such as standard deviation or spectral analysis. Eighteen of 38 individuals with diabetes were positive for two or more of the Ewing battery of tests indicating CAN. Approximate Entropy (ApEn), log normalized total power (LnTP) and log normalized high frequency (LnHF) power were different in CAN+ to CAN- individuals (p < 0.05). This indicates that nonlinear scaling parameters are able to identify people with cardiac autonomic neuropathy in short ECG recordings.