The Use of Empirical Mode Decomposition on Heart Rate Variability Signals to Assess Autonomic Neuropathy Progression in Type 2 Diabetes

Abstract

In this study, we investigated the use of empirical mode decomposition (EMD)-based features extracted from electrocardiogram (ECG) RR interval signals to differentiate between different levels of cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM). This study involved 60 participants divided into three groups: no CAN, subclinical CAN, and established CAN. Six EMD features (area of analytic signal representation—ASRarea; area of the ellipse evaluated from the second-order difference plot—SODParea; central tendency measure of SODP—SODPCTM; power spectral density (PSD) peak amplitude—PSDpkamp; PSD band power—PSDbpow; and PSD mean frequency—PSDmfreq) were extracted from the RR interval signals and compared between groups. The results revealed significant differences between the noCAN and estCAN individuals for all EMD features and their components, except for the PSDmfreq. However, only some EMD components of each feature showed significant differences between individuals with noCAN or estCAN and those with subCAN. This study found a pattern of decreasing ASRarea and SODParea values, an increasing SODPCTM value, and a reduction in PSDbpow and PSDpkamp values as the CAN progressed. These findings suggest that the EMD outcome measures could contribute to characterizing changes associated with CAN manifestation in individuals with T2DM.

Keywords

• electrocardiogram (ECG)
• heart rate variability
• diabetes
• cardiovascular autonomic neuropathy
• empirical mode decomposition