## 1 Introduction  
Heart rate variability (HRV) signals are extracted from an electrocardiogram (ECG), a technique utilized in many disciplines including the study of cardiac and non-cardiac diseases, such as myocardial infarction (_MI_) [1], sudden cardiac death (SCD) and ventricular arrhythmias [2], diabetes mellitus (DM) [3], and hypertension [4]. Patients suffering from congestive heart failure (CHF) present with a depressed or low HRV. Moreover, minute variations in HRV signals are difficult to identify, as these signals contain baseline shifts in ECG signals, along with noise. It is difficult and challenging to analyze these signals with traditional methods. The HRV signal parameters are affected by instantaneous variation [5], respiration [6], and motion artifacts [7]. To minimize the obstacles of manual and visual interpretation, computer aided diagnostic (CAD) techniques are used to analyze the HRV signals. Around the world, there are about 26 million people suffering from CHF [8]. This pathophysiological condition prevents the heart from circulating enough blood around the body. These different types of health pathologies reduce the ventricle's ability to pump blood [9]. The common indications of CHF include edema, dyspnea, and fatigue [8,9], myocardial infarction (_MI_), heart valve disease, etc. [10]. Patients suffering from CHF are prone to cardiac death [11]. Thus, we need to develop automated tools which can help us to investigate the underlying hidden dynamics at the earliest stages, so that concerned cardiologists can take the relevant measures to treat these diseases. [PERSON] et al. (2011) [12] employed methods from data mining to detect heart diseases. The methods from data mining, including Bayesian networks (BNs) and decision trees (DTs) yielded the higher performance than other predictive models such as neural networks and KNN. Moreover, the genetic algorithms applied to DT and BN further enhanced the detection performance for heart disease prediction [13]. Probabilistic propagation networks using Bayesian networks (BNs) have recently been used to investigate the parametric information from the data using associations and the degree of uncertainty of the variables, and to make the expert opinions, etc. [14]. Recently, BNs have successfully been used in many applications, as detailed in [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18]. When other variables in the models are known, the Bayes approach helps to determine causal relationships. BNs have also been utilized in other applications, including the prediction of coffee rust disease using Bayesian networks [19], predicting energy crop yields [20], sustainable planning and management decisions, [21] etc. Moreover, BNs are used to visualize and determine the complex interrelationships between interdisciplinary variables resulting from the impacts of climate change in agricultural scenarios [22]. Most recently, BNs have been used to determine the complex causal interaction between the environment (i.e., climate, weather, and their causes and impact severity) and plant disease in Canada. In the present era, the most common and sudden deaths occur due to cardiac and congestive heart failure (CHF). The proper and timely treatment of CHF is highly desired and demanded; researchers are therefore devising automated tools for automatic diagnosis. The dynamics produced from these signals are of a highly complex, nonlinear, and nonstationary nature. Recently, researchers have mostly utilized classification methods to distinguish the CHF from NSR. However, this study was specifically designed to first extract the GLCM features from CHF and NSR subjects, in order to capture these dynamics. We then ranked the features to determine the features' importance, based on an empirical receiver operating characteristics (EROC) curve and random classifier slope.