## 1 Introduction  
According to the International Diabetes Federation (IDF) [1], it is estimated that 537 million people are living with diabetes mellitus (DM), which represents 10.5% of the world's population. DM is a chronic disorder that occurs when the body cannot produce enough insulin or cannot effectively use the insulin it produces, resulting in raised blood glucose levels [2]. The metabolic disorders of DM lead to diffuse and widespread damage to the peripheral and autonomic nerves and small vessels. Among these, damage to the autonomic nerve fibers that innervate the heart and blood vessels is known as cardiovascular autonomic neuropathy (CAN), resulting in abnormalities in heart rate and vascular dynamics [3]. CAN prevalence is estimated at 17-66% among patients with type 1 DM and 31-73% in patients with type 2 DM [4]. The gold standard for CAN diagnosis is the series of cardiac autonomic reflex tests (CARTs), which measure the heart rate and blood pressure responses to simple interventions such as deep breathing, the Valsalva maneuver, and lying-to-standing [5]. These responses are compared to normal and age-adjusted cut-off values [6]. Nevertheless, CAN has a silent presentation and may exhibit no symptoms or have subtle symptoms, making it challenging to identify and diagnose in the initial stages with only the CARTs [7]. Inaddition, CARTs must follow consistent and standardized protocols, which require active patient collaboration [8]. Early or subclinical CAN is limited to baroreceptor abnormalities and changes in heart rate variability (HRV) [4]. Hence, HRV indices are a different approach to CARTs for assessing autonomic function, as they are easier and quicker than CARTs, patient-independent, and sensitive to early dysfunction [9]. HRV refers to the time elapsed between two successive R-waves of the QRS complex on the electrocardiogram (ECG) (i.e., the RR interval) [10]. HRV indices are amongst the simplest and most reliable ways to assess CAN and are obtained by time- and frequency-domain methods, which measure, respectively, the overall magnitude of the fluctuations of the RR interval between each heartbeat around the average values and the magnitude of fluctuations in a predetermined range of frequency [11]. Despite having a wide basis of evidence for the supportive value of HRV analysis in CAN diagnosis and risk stratification in diabetic individuals [12; 13; 14], HRV methods and indices for decision-making and, most importantly, CAN severity quantification remain an active and expanding research topic [15]. HRV reflects the dynamic changes of the autonomic nervous system's regulation and is determined by the combined inputs of the sympathetic and parasympathetic systems [5]. HRV signals involve nonlinear contributions and are essentially non-stationary [10]. Thus, measures from information and invariant domains that can accurately describe the nonlinear properties of HRV signals are strongly recommended to be used in conjunction with traditional techniques because they may provide supplementary information about the underlying mechanisms involved in cardiovascular regulation [16; 17]. Several studies have demonstrated the usefulness of the nonlinear analysis of HRV for assessing cardiac abnormalities and have explored techniques such as the correlation dimension, Poincare plots, entropy parameters, recurrence plots, and detrended fluctuation analysis [18; 19; 20; 21; 22]. Among these techniques, a nonlinear method that has been explored for HRV analysis is empirical mode decomposition (EMD). EMD is a data analysis method proposed by [PERSON] et al., (1998) [23] that decomposes a time series into a set of simpler and more interpretable oscillatory modes called intrinsic mode functions (IMFs). The Hilbert transform can be applied to IMFs to obtain their analytical signal, from which additional information can be calculated, such as the instantaneous frequency and the amplitude and phase modulation of the IMF [24].