## 1 Introduction
EMD is an adaptive and data-driven technique, which makes it well-suited to analyzing signals that have complex, nonlinear, and non-stationary characteristics. Compared to other methods of time-frequency analysis of signals, EMD addresses some limitations. For example, the Fourier transform is a linear technique that assumes the signal is stationary over time [24], which is not the case for HRV signals that exhibit nonlinear interactions between different frequency components [15]. On the other hand, the Wavelet transform is a frequency-based method that selects an appropriate wavelet basis function and scale, while EMD is a time-based method that does not require a priori knowledge of the signal frequency content [24]. As a result of its different approach and the advantages over the analysis of complex and nonlinear time-series signals, the use of the EMD technique has gained increasing attention and has been adopted for a variety of biomedical signals, such as the electroencephalogram for epileptic seizure classification [25; 26], emotion recognition [27; 28], and the identification of autism severity level [29], electromyography for the analysis of amyotrophic lateral sclerosis [30; 31] or for the classification of neuromuscular disorders [32; 33], and ECG signals for the classification of cardiovascular diseases [34], ECG denoising [35; 36], the classification of ventricular arrhythmias [37], the prediction of sudden cardiac death [38], and the detection of hypertension [39] or the extraction of fetal ECG [40]. The study by [PERSON] et al. [41] was one of the first to propose the application of EMD to HRV analysis, concluding that EMD and the associated Hilbert spectral representation are powerful techniques for HRV time-frequency analysis due to their capabilities of independently isolating the main frequency components and dealing with non-stationary and nonlinear features of the ECG signal. Subsequently, a study by [PERSON] et al. [42] also showed that EMD is a flexible processing method that enhances the assessment of cardiovascular autonomic control, overcoming the limitations posed by the linearity and stationarity assumptions inherent in traditional spectral techniques. [PERSON] et al. [43] proposed a set of EMD-based features applied to the RR interval signals that allowed differentiation between subjects with and without diabetes. In the same context, [PERSON] et al. [44] presented a methodology for screening patients with DM by applying the EMD method to decompose HRV signals, achieving a classification accuracy of 95.63%. To identify and classify normal and congestive heart failure, [PERSON] et al. [45] subjected HRV signals to the EMD technique, obtaining an accuracy of 97.01%. [PERSON] et al. [46] proposed a methodology for discriminating between normal and coronary artery disease subjects using heart rate signals, showing statistically significant EMD-based features. Similarly, [PERSON] et al. [47] investigated EMD-based features applied to HRV signals on sudden cardiac death (SCD), predicting subjects at risk earlier with an accuracy of 96.1% and outperforming the classical linear estimators of SCD. These studies highlighted that analyzing HRV signals using the EMD technique provides relevant information about overall cardiac abnormalities. Furthermore, within the context of DM, the scope of these studies is limited to characterizing DM and distinguishing between normal and diabetic patients, with no studies analyzing the progression of CAN.