## 1 Introduction  
Cannabis is a dioecious annual plant, which is used to develop various therapeutic agents for the treatment of pain, nausea, and insomnia in cancer patients and anorexia in acquired immune deficiency syndrome (AIDS) patients [1]. However, the cannabis plant is also used to derive various recreational products like bhang, ganja, and charas. Cannabis-based recreational products are believed to have low toxicity, and their consumption is increasing day by day [2]. As per the reported literature, cannabis has become the most highly used psychotropic recreational compound across the globe, following alcohol and tobacco [1]. Many states of the USA have legalized the non-medical use of cannabis and have allowed companies to sell cannabis to adults who are older than 21 years old [3]. However, many recent reports have revealed the occurrence of a variety of unfavorable health effects of cannabis, including cardiovascular diseases [4]. Hence, it is important to identify the effect of cannabis intake on the cardiovascular activities of cannabis users. It is a well-known fact that the modulation in cardiac autonomic regulation (CAR) provides prognostic information about cardiovascular diseases [5]. This is attributed to the innervations of the sympathetic and vagal nerves of the ANS to the heart [6]. The functioning of the ANS can be understood non-invasively by analyzing the RR interval/HRV signals, derived from the electrocardiogram (ECG) signals [7]. Researchers have proposed both linear and nonlinear techniques for the processing of the HRV signals [8]. However, decomposition-based methods (e.g., EMD, DWT, and WPD) are getting more attention from researchers nowadays because of the nonlinear and non-stationary behavior of the HRV signals [8; 9; 10]. The EMD technique is a popular nonlinear signal decomposition technique, which was first proposed by [PERSON] et al. (1998) [11]. It divides the input signal into mono-component functions called intrinsic mode functions (IMFs) and simplifies the further processing of the signals [8]. Several researchers have proposed that subtle variations within the HRV signals can be identified easily using EMD [8]. [PERSON] et al. (2015) performed the EMD analysis of the HRV signals for the automated detection of diabetes [12]. The EMD-based decomposition of the HRV signals produced six IMFs. The IMFs were used to extract two time-domain parameters and three frequency-domain parameters [12]. The clinically significant variations were obtained between the diabetic and control groups, suggesting the applicability of the EMD method in the automated identification of diabetic patients. [PERSON] et al. (2016) used EMD analysis of HRV signals and calculated the power spectral density (PSD) of the IMFs for the detection of cardiac arrhythmia [13]. [PERSON] et al. (2017) reported EMD-based processing of HRV signals to facilitate the automated detection of congestive heart failure (CHF) disease [8]. Each of the HRV signals was decomposed into six IMFs, and 13 types of entropies were extracted from them. The extracted parameters were ranked using five ranking methods, and the highly-rated variables were classified using probabilistic neural networks and support vector machines. The normal and the CHF classes could be classified with the accuracy, sensitivity, and specificity of 97.64%, 97.01%, and 98.24%, respectively. Thus, the authors recommended that the proposed automated method could be employed to recognize the persons suffering from CHF, which will help the clinicians to plan their treatment. In the last few decades, studies have also been carried out on wavelet (DWT and WPD)-based decomposition of the HRV signals [14; 15]. The DWT represents a widely used wavelet-based decomposition method that simultaneously enables the time and frequency domain analyses of the signals [16]. [PERSON] et al. (2015) employed DWT-based processing of HRV signals for automated diagnosis of diabetes [10]. The HRV signals were extracted from 30 volunteers (15 controls and 15 diabetics).