## 3 Discussion
An understanding of the CAR provides valuable diagnostic information about the cardiovascular activity. This is because the rhythmic contraction of the heart is primarily regulated by the ANS through its nerve innervations (both vagal and sympathetic) into the heart. The ANS activity results in the alteration of the time duration between the consecutive RR intervals (also regarded as the HRV signals) [29]. Hence, the HRV signals have been widely used in the last few decades for diagnosing any imbalance in the CARactivity due to any external stimuli or diseases [8]. Usually, the processing of the HRV signals is performed using the time domain (statistical, geometric), frequency domain (e.g., FFT, AR), or the nonlinear methods (e.g., Poincare plot, recurrence quantification analysis (RQA), correlation dimension, and detrended fluctuation analysis (DFA)) [30]. However, several recent studies have proposed the use of signal decomposition-based techniques like EMD, DWT, and WPD for the analysis of the HRV signals [8; 9; 10]. This may be attributed to their ability to evaluate the original signatures that are present within the nonlinear and non-stationary HRV signals [8; 31]. Hence, EMD, DWT, and WPD-based processing of the HRV signals were performed in the current study to examine the existence of any alteration in the CAR activity due to cannabis consumption. The EMD method resulted in the generation of at least seven IMFs from each HRV signal. Hence, only the first seven IMFs of each HRV signal were chosen as the input signals for the feature extraction process [12]. The selection of the appropriate mother wavelet plays a vital role in the wavelet analysis of the signals. [PERSON] et al. (2006) have advocated the use of the Daubechies series of the mother wavelet for the processing of the HRV signals due to its ability to clearly reveal the properties of the transient and other components of the HRV signals [32]. Hence, Daubechies (db) wavelet was used as the mother wavelet for the processing of the HRV signals using DWT and WPD. The level of decomposition of the HRV signals was varied from 2 to 8 for identifying the most suitable level of decomposition. The entropy-based parameters were extracted from the IMFs and the wavelet coefficients to obtain useful information related to the corresponding HRV signals. The reason behind the extraction of the entropy-based parameters may be attributed to the fact that entropy reveals the rate of generation of the information in a dynamical system and is extensively used as a complexity measure in the biomedical signal analysis [8; 33]. Apart from this, the entropy-based parameters also help to quantify the degree of regularity in the signal by examining the repetitive patterns [34]. It is customary to use hypothesis testing as a tool for establishing whether there is a significant difference between the data of two populations using samples of a relatively lower size [35]. The \(t\)-test method was used for hypothesis testing when the parameter followed the normal distribution. However, most of the IMF and wavelet coefficient-derived entropy parameters did not exhibit the normal distribution (evident from the Shapiro--Wilk test). Hence, the Mann--Whitney U test (with a critical \(p\)-value = 0.05) was used to identify the significantly varying parameters for such parameters, which is a nonparametric statistical method. This method does not require the normal distribution of the parameters [36]. The results of the \(t\)-test and Mann--Whitney U test revealed many statistically significant parameters. This corresponds to the existence of variation in the characteristics of the HRV signals obtained from Category-C and Category-B. The variation in the HRV signal behavior might be due to the alteration in the CAR activity induced by cannabis intake. This is because HRV signals are regarded as a non-invasive indicator of CAR [37].