## 4 Discussion  
In summary, it was observed that all features (except the \(PSD_{mfrq}\)) calculated from the four EMD-extracted IMF components differed significantly between the extreme groups (i.e., noCAN and estCAN). More importantly, the specific IMF components of each feature could effectively distinguish individuals without a CAN diagnosis or with an established CAN from those with subclinical CAN (refer to Figure 6). This research explores a new approach for analyzing the RR interval signals acquired from type 2 DM individuals at different stages of cardiovascular autonomic neuropathy. We used the EMD method, which decomposes non-stationary and nonlinear signals into IMFs. The findings of this study suggest that employing the nonlinear EMD method enables the extraction of several parameters from heart rate variability that hold promise in identifying changes associated with the manifestation and progression of CAN. Specifically, the complex plane plots from the analytical signal representation and the second-order difference plot of the first four IMFs were used to extract the ASRarea, SODParea and SODPCTM features, while the power spectral density of the IMFs was used to obtain the \(PSD_{bpow}\), PSDpkamp and \(PSD_{mfrq}\) features. These parameters were compared across subjects at distinct stages of CAN (noCAN, subCAN, and estCAN). The main results were as follows: (a) decreased IMF variability and (b) decreased IMF power distribution, according to CAN progression. The autonomic nervous system (ANS) maintains homeostasis by regulating arterial pressure and all significant cardiovascular variables through the sympathetic and parasympathetic divisions [63]. The ANS imbalance manifesting as CAN in the diabetic population is an important predictor of cardiovascular events [7], as reflected in the abnormal HRV indices. The nonlinear analysis methods of HRV allow a more subtle characterization of autonomic balance and are reliable markers of morbidity and mortality in patients with cardiovascular disease [7]. Furthermore, nonlinear HRV indices in diabetic populations may have diagnostic and prognostic potential for identifying asymptomatic CAN and cardiovascular events [21]. A recent study found that prediabetes and T2 DM patients were independently associated with lower HRV (both in the time and frequency domains), strongly suggesting that the HRV indices could help identify subclinical CAN [64]. In another study, T2 DM patients presented an overall decrease in HRV and decreased sympathetic and parasympathetic activity [15]. Correspondingly, BRS is also a sensitive indicator of CAN in DM patients [65] and can be associated with cardiovascular events [9]. Previous studies [66; 67] have demonstrated a correlation between the first four IMFs extracted by EMD and the HRV frequency components (VHF, HF, LF, and VLF, respectively). The HRV oscillatory components are usually divided into the following spectral profiles: (a) very-high-frequency (VHF, 0.4 to 1 Hz); (b) high-frequency (HF, 0.15 to 0.40 Hz); (c) low-frequency (LF, 0.04 to 0.15 Hz); (d) very-low-frequency (VLF, \(<\)0.04 Hz) bands [68]. However, in short-term recordings (5-10 min), the spectral analysis of HRV holds three main frequency bands: HF, LF, and VLF [68]. By examining the power spectrum density of the HRV signal's EMD components (Figure 5), we could observe the energy distribution according to the different frequency components and associate IMF\({}_{1}\) with the HF component, IMF\({}_{3}\) with the LF component, and IMF\({}_{4}\) with the VLF band. IMF\({}_{2}\) is not clearly defined between the LF and HF components. Importantly, each frequency component can be related to the different activities of the autonomic nervous system. The HF band reflects parasympathetic activity and is a marker of cardiac vagal modulation. In contrast, the LF band modifies the parasympathetic and sympathetic nervous systems and indicatesbaroreceptor activity during resting conditions [68].