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

Diabetes mellitus (DM) is a serious and increasing health problem worldwide. Compared to non-diabetics, patients experience an increased risk of all cardiovascular diseases, including dysfunctional neural control of the heart. Poor diagnoses of cardiac autonomic neuropathy (CAN) may result in increased incidence of silent myocardial infarction and ischaemia, which can lead to sudden death. Traditionally the Ewing battery of tests is used to identify CAN. The purpose of this study is to examine the usefulness of heart rate variability (HRV) analyses of short-term ECG recordings as a method for detecting CAN. HRV may be able to identify asymptomatic individuals, which the Ewing battery is not able to do. Several HRV parameters are assessed, including time and frequency domain, as well as nonlinear parameters. Eighteen out of thirty-eight 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 demonstrate a significant difference at p < 0.05 between CAN⁺ and CAN^-. This indicates that nonlinear scaling parameters are able to identify people with cardiac autonomic neuropathy in short ECG recordings. Our study paves the way to assess the utility of nonlinear parameters in identifying asymptomatic CAN.