Andreis, Felipe Rettore

Cossul, Sandra

Favretto, Mateus Andre

Marques, Jefferson Luiz Brum

English

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— (Formula presented.) ; area of the ellipse evaluated from the second-order difference plot— (Formula presented.) ; central tendency measure of SODP— (Formula presented.) ; power spectral density (PSD) peak amplitude— (Formula presented.) ; PSD band power— (Formula presented.) ; and PSD mean frequency— (Formula presented.)) 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 (Formula presented.). 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 (Formula presented.) and (Formula presented.) values, an increasing (Formula presented.) value, and a reduction in (Formula presented.) and (Formula presented.) 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.</p

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

https://vbn.aau.dk/files/545450396/applsci-13-07824-v2.pdf

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

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