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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.

This article demonstrates the power and flexibility of linear mixed-effects models (LMEM) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabetic peripheral neuropathy (n = 12). We started by presenting the limitations of traditional approaches by building a linear model with only fixedeffects. Then, we showed how the model adequacy could be increased by including random-effects, as well as by adding alternative correlation structures. The models were compared with the Akaike information criterion and the Bayesian information criterion, as well as the likelihood ratio test. The results showed that the inclusion of the random-effects of intercept and slope, along with an autoregressive moving average correlation structure is the one that best describes the data (p < .01). Furthermore, we demonstrate how the inclusion of additional variance structures can accommodate heterogeneity in the residual analysis and therefore increase model adequacy (p < .01). Thus, in conclusion, we suggest that adopting LMEM to repeated measures such as electromyography can provide additional information from the data (e.g., test for alternative correlation structures of the RMS value), and hence provide new insights into HD-sEMG related work.