Next Article in Journal
Searching for a Numerical Model for Prediction of Pressure-Swirl Atomizer Internal Flow
Previous Article in Journal
Performance Evaluation of Planetary Boundary Layer Schemes in Simulating Structures of Wintertime Lower Troposphere in Seoul Using One-Hour Interval Radiosonde Observation
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure

Department of Information Systems, College of Science and Arts, King Khalid University, Abha 62529, Saudi Arabia
Department of Computer Science and Information Technology, King Abdullah Campus, Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam 13230, Pakistan
Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
Faculty of Arts and Science, Najran University, Sharourah 51730, Saudi Arabia
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(13), 6350;
Submission received: 8 April 2022 / Revised: 12 June 2022 / Accepted: 18 June 2022 / Published: 22 June 2022


The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson’s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes “dissimilarity” and “cluster performance” (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.

1. Introduction

Heart rate variability (HRV) signals are extricated 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.
Soni 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 Kocian et al. [15], Amaral et al. [16], Laurila-Pant et al. [17], and Zhang 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. Finally, we utilized the robust Bayesian inference approach to determine the associations between the extracted GLCM features by computing arc analysis using mutual information, the significance of a cluster’s prominence, and overall analysis of highly ranked dissimilarity features among other extracted features. The proposed approach will further elucidate the underlying dynamics of highly complex heart variability signals, and can be used for better diagnosis and prognosis by health professionals and cardiologists for timely treatment. The associations and strengths of these relationships will be useful for further micro-level analysis and determining the significance of these features among all computed features.
The first section of the paper describes the background of the problem, the existing methods utilized and their limitations, and the proposed innovative methods. The second section explains the dataset used, feature extraction and ranking methods, the Bayesian inference methods used, along with exploratory analysis of unsupervised network analysis. The next section describes and interprets the results, and the last section discusses the main findings and limitations of this study, and future research directions.

2. Materials and Methods

2.1. Dataset

This dataset is taken from publicly available Physionet database [23]. We identified the congestive heart failure (CHF) and normal sinus rhythm (NSR) of our subjects [23]. A total of 72 subjects, including 37 women and 35 men (54 from the RR-interval NSR database and 18 from the MIT BIT normal NSR database), underwent 24 h Holter monitor recordings. The average age of the groups was 54.6 ± 16.2 (mean ± SD), ranging between 20 and 78 years. A value of 128 Hz was used to sample the ECG data. The data of CHF subjects comprised 44 subjects, including 15 women and 29 men, aged 55.5 ± 11.4, with a range of 22–78 years. We used 20,000 samples for all subjects, including both NSR and CHF subjects.

2.2. Features Extraction

In machine learning, the most important step is to extract the most relevant features. In the past, researchers [24] and [25] computed various hybrid and geometric features for colon cancer detection. The researchers [26] computed texture features to predict breast cancer. The researchers [27,28,29,30,31,32] computed various features based on texture, morphology, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) to predict brain tumors, lung cancer, breast cancer, and prostate cancer. In this study, we extracted Gray-level co-occurrence matrix (GLCM) features and then ranked the features based on empirical receiver operating characteristic (EROC) and a random classifier slope, as utilized in [33,34,35] to rank the features’ importance.

2.3. Gray-Level Co-Occurrence Matrix (GLCM)

The GLCM features are based on textural features identified by performing transitions on two pixels with a gray level technique. GLCM features were originally proposed by [36] in 1973, in a study that characterizes texture using different quantities obtained from second order image statistics. Two steps are used in obtaining GLCM features. In the first step, the pair-wise spatial co-occurrences of image pixels separated by distance d in a particular direction and angle θ are computed. The spatial relationship between the two pixels is created, i.e., the neighboring pixel and the reference pixel. In the second step, the GLCM matrix is used for computing scalar quantities which are utilized in the characterization of several aspects of an image [36]. The detailed description and mathematical formulation are described in [32,37,38,39].

2.4. Feature Ranking Algorithms

After extracting the features, all features are not equally important. The feature importance was computed using supervised feature ranking algorithms [40]. The feature importance ranking (FIR) algorithm was developed in MATLAB [41]. The importance of the computed features was measured using an empirical receiver operating characteristic (EROC) curve and a random classifier slope. The greater the ROC value indicate the more important feature.

2.5. Bayesian Inference Approach

The Bayesian approach is used to determine the association among the nodes, and is based on Bayes theorem. The Bayesian inference approach provides many comprehensive tools for in-depth analysis of the extracted nodes (features). This approach uses mutual information methods to provide the arc analysis, i.e., the strength of relationship among the nodes and association among the nodes. The optimization tree based on the selected target ‘dissimilarity’ yielded the tree with a parent–child nodes relationship, by determining importance based on the probability and joint probability scores based on the parent–child relationship. Moreover, ingoing, outgoing, and total force was computed, which helped to determine the force effect of the computed nodes.
The causal effects and their relationships are determined using a Bayesian inference approach with a directed acyclic graph (DAG) [42]. Considering X = {X1, X2, X3, …, Xn} a set of n dimensional variables, the Bayesian network is defined with couplet X = G , P represented in angular brackets, where G denotes the DAG and P denotes the set of parameters that quantify the network that contains the probabilities of each possible value of xi for each variable Xi. Mathematically:
P ( X ) = P ( X 1 ,     X 2 ,     X 3 , ,   X n ) = i = 1 n P ( X i X j ( i ) )
Here, X j ( i ) represents the set of parent variables of X i for direct acyclic graph G. This algorithm thus consequently computes the posterior probability through inference of variables of interest. BayesiaLab V10 was used for this analysis [43], by applying a set of supervised learning algorithms to search the optimal model. The Shannon entropy [44] was computed using:
H ( X ) = x ϵ X p ( X ) l o g 2 p ( X )

2.6. Mutual Information (MI)

In this study, we utilized mutual information (MI) to compute the correlation of and strength of relationship between the extracted GLCM features from congestive heart failure signals. The mutual information algorithm computed the difference between the marginal entropy of the target variable and the conditional entropy of predicted variable, [44] denoted by MI, mathematically:
M I ( X , Y ) = H ( X ) H ( X Y )
MI is thus the reduction in uncertainty about the variable X, or can be the reduction in the number of X (Yes) or Y (No) questions required to guess X after observing Y. By combining H(X) and H ( X Y ) ,   we acquire:
M I ( X , Y ) = x ϵ X y ϵ X p ( X , Y )   log 2 p ( X , Y ) p ( X ) p ( Y )
Moreover, conditional mutual information (CMI) is defined as:
C M I ( X , Y | Z ) = x ϵ X y ϵ X y | z ϵ X p ( X , Y | Z )   log 2 p ( X , Y | Z ) p ( X | Z ) p ( Y | Z )
p (X,Y) shows the joint probability distribution of X and Y, whereas p(X) and p(Y) indicate the marginal distribution of X and Y, respectively. The relevant Gaussian distribution of co-variance matrix variables X1, X2, X3, …. Xn [45] can be computed as:
H ( X ) = log ( 2 π e ) n 2 | C | 1 2
By applying the mathematical transformation function, the MI can be calculated using following formulae:
M I ( X , Y ) = 1 2 log | C ( X ) | × | C ( X ) | | C ( X , Y ) |

2.7. Exploratory Analysis of the Unsupervised Network

By constructing an unsupervised Bayesian network, the potential relationships between variables can be computed and explored in reality by transferring them to the model [46]. Thus, we can investigate the global analysis of the problem by computing the mutual influence of the nodes and understanding the individual influence of the variables under consideration. We built the network model using the EQ algorithm in BayesiaLab V.12 [47]. This learning method explores the space of equivalence classes of Bayesian network structures. Moreover, this method is also highly efficient, as it reduces the size of the search space to partially directed acyclic graphs (PD AGs), which are smaller than the space of Bayesian networks (DAGs), by representing equivalence classes, evaluated during each search, by computing their scores directly. A maximum weighted spanning tree (MWST) was tested. A lowest minimum description length (MDL) value was obtained with EQ, indicating the best trade-off between complexity and data representation, and validating its adoption in this study.
The Schematic flow of our model is reflected in Figure 1. We computed the GLCM texture features proposed by Avinash Uppuluri publicly available at (, accessed on 20 June 2022). The description of features is mentioned in the available code i.e. 1 features taken by author from MATLAB and 2 features taken by author from the paper. The Bayesian approach has recently gained popularity and is utilized in many medical, signal, and image-processing problems. Classifying congestive heart failure is a complex problem which requires lots of effort in developing automated tools. To consider the diverse dynamics, we first computed the gray-level co-occurrence matrix (GLCM) features from the CHF and NSR subjects. We then ranked these features based on ROC values. The dissimilarity feature yielded the highest entropy values, indicating highly important features. We then set the dissimilarity feature as the target variable and applied a Bayesian approach in our further deeper analysis, which was based on four different clusters states.
Afterwards, we computed the posterior probabilities, likelihood, optimization tree, prior and posterior means, the association of the target variable with other nodes, and the detailed analysis of target node. This analysis provides a comprehensive overview of the extracted features, and their contribution and association among the nodes.

3. Results and Discussion

In this study, we first computed the GLCM features from heart failure signals. We determined these features based on ROC values. The highly important dissimilarity value was selected as our target node, and its association with other extracted target variables was computed. The detailed analysis will help to elucidate further dynamics of our complex analysis for a deeper understanding of the extracted features.
We first extracted the GLCM features from pituitary and meningioma brain MRI images. We then ranked the features based on entropy values. The ranked values, based on entropy, are shown in Figure 2. The importance of the features based on entropy value are energy (3.069), hoomogenity1 (2.7317), homogenity2 (2.6927), maximum probability (2.6818), and so on. We then chose energy as the target variable to compute its association with other computed GLCM features using a Bayesian inference approach.
Figure 3 shows the arc analysis using the MI method. The arcs between the nodes show the strength of the relationship between the nodes. The bolder arc with greater values shows the highest strength between the nodes; the arc line decreases accordingly as the value and strength of the relationship decreases. There are also other methods for computing arc analysis, including Pearson’s correlation (PC), etc. However, in the present study, we only utilized the MI for our arc analysis. Moreover, node size can be computed using different methods, including mean, normalized mean value, node force, etc., to reflect the node size. However, in this study, the node size was computed using the normalized mean value.
In this study, we computed the GLCM features and computed the associations among them using mutual information, as reflected in Figure 3. The arcs show the strengths of relation, and the nodes show the normalized mean values. The relationship of the highest strength was yielded by correlation correlation 1 , 1.4640 , followed by dissimilarity homogenity 1 , 1.2164 , energy entropy , 1.0845 , and so on.
Table 1 shows the outgoing, incoming, and total force of extracted GLCM features from congestive heart failure signals. The highest incoming force was yielded at node dissimilarity (1.0146), outgoing force (0.9330), and total force (1.9477), followed by cluster shade with incoming force (0.9330), outgoing force (0.000), and total force (0.9330); this contrasts with incoming force (0.6281), outgoing force (0.5446), and total force (1.1727), and so on.
The target node of dissimilarity had a probability of 58.62% and a joint probability of 100%. The first level of the tree contains child nodes with probabilities such as cluster prominence (100%), contrast (100%), cluster shade (91.78%), energy (73.53%), entropy (68.23%), homogenity1 (68.23%), autocorrelation (67.79%), correlation (63.95%), and correlation1 (62.51%). The red colors show the 1/3rd cluster, green shows the 3/3rd cluster ranges. Navy blue color denote the probabilities at each node. Light purple denotes the joint probability and white denote the scores as reflected in Figure 4.
At the second level, cluster shades produce child nodes with probabilities such as energy (95.38%), entropy (94.21%), homogenity1 (94.21%), autocorrelation (93.56%), correlation (92.91%), and correlation1 (92.64%). Likewise, energy has child nodes of autocorrelation, correlation, and correlation2. Moreover, homogeneity1 has child nodes of autocorrelation, correlation, and correlation2. At the third level, energy, entropy, and homogenity1 have the child nodes autocorrelation, correlation, and correlation2.
Figure 5 shows the unsupervised clustering when dissimilarity was the selected target node. The arrows indicate the direction of relationship. Further dynamics are computed based on the target node’s associations with other nodes. The right part of the figure indicates the different cluster states with their occurrence out of the total subjects.
Figure 6 shows the significance of the selected top ranked dissimilarity node with other nodes such as cluster prominence, cluster shade, contrast, autocorrelation, energy, entropy, correlation, correlation2, and homogeinity1. The highest association was yielded in the cluster ≤349,738 (58.62%) with the red lines showing cluster prominence, contrast, and cluster shade with probability in the range of 0.0 to 1.0, and with other nodes in the range 0.3 to 0.75. The second highest occurrence of dissimilarity was yielded in the cluster ≤882,604 (34.48%), indicated in the green color, with cluster prominence and cluster shades occurring in the highest probability range of 0.0 to 0.90.
Table 2 shows the overall analysis of the high-ranked feature of dissimilarity as the target node, alongside other nodes. The highest performance was yielded using cluster prominence with mutual information (MI) as 1.0146, normalized MI (64.01%), relative MI (81.33%), and relative significance (1.000), followed by cluster shade, contrast, autocorrelation, energy, entropy, correlation, correlation2, and homogeneity.
Table 3 shows the local analysis for cluster 1 of 3. The highest significance was yielded with cluster prominence with binary MI (0.7847), relative binary significance, (80.19%), binary relative significance (1.000), and max. Bayes factor (92.64%), followed by cluster shade, contrast, and so on.
Table 4 shows the local analysis for cluster 2 of 3. The highest significance was yielded with cluster prominence with binary MI (0.6966), relative binary significance (74.95%), binary relative significance (1.000), and max. Bayes factor (97.50%), followed by cluster shade, contrast, and so on.
Table 5 shows the local analysis for cluster 3 of 3. The highest significance was yielded with cluster prominence with binary MI (0.3621), relative binary significance (100%), binary relative significance (1.000), and max. Bayes factor (100%), followed by cluster shade, contrast, and so on.
Recently, the need has arisen for a comprehensive analysis to compute the associations among the computed features, in order to understand the strength of relationships, associations, the incoming and outgoing forces between parent and child nodes, and the significance of target nodes and target nodes trees; this analysis can be carried out using the robust Bayesian inference approach. This approach will further help us to determine the underlying dynamics and relationships among the extracted nodes, which will help the relevant healthcare professionals to further improve their decision-making capabilities and diagnostic procedures. Hussain et al. [28] extracted the morphological features from prostate cancer data, in order to compute the associations between these features for deeper analysis.

4. Conclusions

The dynamics of heart signals are highly complex and nonlinear in nature. To explicate these nonlinear dynamics, we first computed the gray level co-occurrence matrix (GLCM) features to capture these dynamics. We ranked the extracted features based on EROC to determine their importance. The dissimilarity feature yielded the highest EROC value (0.3589), followed by inverse difference (0.3564), cluster prominence (0.3441), and so on. We computed the association and strength of relationships among these features using mutual information (MI). The nodes   correlation correlation 1   ( 1.4640 ) yielded the relationship with the highest strength. We then selected dissimilarity as the target node and computed the significance with other selected states. Finally, we computed the local analysis of dissimilarity at selected states by computing binary mutual information, relative binary significance, and the maximum Bayes factor to further clarify the underlying nonlinear dynamics. The results reveal that the proposed method is more robust as a means of determining the nonlinear dynamics of heart failure signals and will lead to further improvement in prognosis and diagnosis. Currently, we have tested the results on a small dataset with no clinical profiles available. In future, we will test on larger datasets with more clinical details, which present congestive heart failure data and other demographic information. We will also apply more in-depth Bayesian inference analysis to further explain the nonlinear dynamics present in these datasets, to further improve healthcare professionals’ decision making and diagnostic capabilities.

Author Contributions

Data curation, L.H.; formal analysis, M.M.E., L.H., A.A.M., M.K.N., M.O., H.M., A.Y., and M.A.H.; investigation, L.H.; methodology, L.H. and M.K.N.; project administration, L.H.; resources, L.H. and A.Y.; software, L.H.; supervision, L.H.; visualization, A.A.M.; writing—original draft, L.H.; writing—review and editing, M.M.E., M.O., H.M., and M.A.H. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

This dataset is taken from publicly available Physionet database [23].


The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Project, under grant number 158/43. Princess Nourah bint Abdulrahman University Researchers Supporting Project number PNURSP2022R151, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22UQU4310373DSR24.

Conflicts of Interest

The authors declare no conflict of interest.


GLCMGray level co-occurrence
HRVHeart rate variability
PCPearson’s correlation
CHFCongestive heart failure
NSRNormal sinus rhythm
MIMyocardial infarction
SCDSudden cardiac death
CADComputed aided diagnostic
EMDEmpirical mode decomposition
LFLow frequency
VLFVery low frequency
HFHigh frequency
SDNNStandard deviation of normal-to-normal beat interval
LRPLow risk patients
AFAtrial fibrillation
RMSSDRoot mean square of successive RR differences
FAWTFlexible analytic wavelet transforms
APEntAccumulated permutation entropy
MIMutual information
PCPearson’s correlation
EROCEmpirical receiver operating characteristic curve
NYHANew York Heart Association
MFCCMel frequency cepstral Coefficients
SIFTScale invariant Feature transform
EFDsElliptic Fourier descriptors
WPCWavelet phase coherence
DAGDirected acyclic graph


  1. Stein, P.K.; Kleiger, R.E.; Domitrovich, P.P.; Schechtman, K.B.; Rottman, J.N. Clinical and demographic determinants of heart rate variability in patients post myocardial infarction: Insights from the cardiac arrhythmia suppression trial (CAST). Clin. Cardiol. 2000, 23, 187–194. [Google Scholar] [CrossRef] [PubMed]
  2. Malliani, A.; Lombardi, F.; Pagani, M.; Cerutti, S. Power spectral analysis of cardiovascular variability in patients at risk for sudden cardiac death. J. Cardiovasc. Electrophysiol. 1994, 5, 274–286. [Google Scholar] [CrossRef] [PubMed]
  3. Pagani, M. Heart rate variability and autonomic diabetic neuropathy. Diabetes. Nutr. Metab. 2000, 13, 341–346. [Google Scholar] [PubMed]
  4. Sajadieh, A. Increased heart rate and reduced heart-rate variability are associated with subclinical inflammation in middle-aged and elderly subjects with no apparent heart disease. Eur. Heart J. 2004, 25, 363–370. [Google Scholar] [CrossRef]
  5. Hu, J.; Gao, J.; Tung, W.; Cao, Y. Multiscale analysis of heart rate variability: A comparison of different complexity measures. Ann. Biomed. Eng. 2010, 38, 854–864. [Google Scholar] [CrossRef]
  6. Liu, G.-Z.; Huang, B.-Y.; Wang, L. A wearable respiratory biofeedback system based on generalized body sensor network. Telemed. e-Health 2011, 17, 348–357. [Google Scholar] [CrossRef] [Green Version]
  7. Liu, G.-Z.; Guo, Y.-W.; Zhu, Q.-S.; Huang, B.-Y.; Wang, L. Estimation of respiration rate from three-dimensional acceleration data based on body sensor network. Telemed. e-Health 2011, 17, 705–711. [Google Scholar] [CrossRef]
  8. Budi Siswanto, B.; Shimokawa, H.; Samal, U.C.; Ponikowski, P.; Cowie, M.R.; Hu, S.; Rastogi, V.; AlHabib, K.F.; Anker, S.D.; Krum, H.; et al. Heart failure: Preventing disease and death worldwide. ESC Heart Fail. 2014, 1, 4–25. [Google Scholar] [CrossRef]
  9. Peteiro, J.; Peteiro-Vázquez, J.; Gacía-Campos, A.; García-Bueno, L.; Abugattás-de-Torres, J.P.; Castro-Beiras, A. The causes, consequences, and treatment of left or right heart failure. Vasc. Health Risk Manag. 2011, 7, 237. [Google Scholar] [CrossRef] [Green Version]
  10. Jong, T.-L.; Chang, B.; Kuo, C.-D. Optimal timing in screening patients with congestive heart failure and healthy subjects during circadian observation. Ann. Biomed. Eng. 2011, 39, 835–849. [Google Scholar] [CrossRef]
  11. Khaled, A.; Owis, M.I.; Mohamed, A.S.A. Detection of congestive heart failure using time-domain methods and poincar.e plot of heart rate variability signals. In Proceedings of the 3rd Cairo International Biomedical Engineering Conference, CIBEC 2006, Cairo, Egypt, 21–24 December 2006. [Google Scholar]
  12. Soni, J.; Ansari, U.; Sharma, D.; Soni, S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int. J. Comput. Appl. 2011, 17, 43–48. [Google Scholar] [CrossRef]
  13. Falsey, A.R.; Walsh, E.E.; Esser, M.T.; Shoemaker, K.; Yu, L.; Griffin, M.P. Respiratory syncytial virus-associated illness in adults with advanced chronic obstructive pulmonary disease and/or congestive heart failure. J. Med. Virol. 2019, 91, 65–71. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Kaikkonen, L.; Parviainen, T.; Rahikainen, M.; Uusitalo, L.; Lehikoinen, A. Bayesian networks in environmental risk assessment: A review. Integr. Environ. Assess. Manag. 2021, 17, 62–78. [Google Scholar] [CrossRef] [PubMed]
  15. Kocian, A.; Massa, D.; Cannazzaro, S.; Incrocci, L.; Di Lonardo, S.; Milazzo, P.; Chessa, S. Dynamic Bayesian network for crop growth prediction in greenhouses. Comput. Electron. Agric. 2020, 169, 105167. [Google Scholar] [CrossRef]
  16. Amaral, C.B.D.; Oliveira, G.H.F.D.; Eghrari, K.; Buzinaro, R.; Môro, G.V. Bayesian network: A simplified approach for environmental similarity studies on maize. Crop Breed. Appl. Biotechnol. 2019, 19, 70–76. [Google Scholar] [CrossRef] [Green Version]
  17. Laurila-Pant, M.; Mäntyniemi, S.; Venesjärvi, R.; Lehikoinen, A. Incorporating stakeholders’ values into environmental decision support: A Bayesian Belief Network approach. Sci. Total Environ. 2019, 697, 134026. [Google Scholar] [CrossRef]
  18. Zhang, L.; Pan, Q.; Wang, Y.; Wu, X.; Shi, X. Bayesian network construction and genotype-phenotype inference using GWAS Statistics. IEEE/ACM Trans. Comput. Biol. Bioinforma. 2019, 16, 475–489. [Google Scholar] [CrossRef] [PubMed]
  19. Corrales, D.C.; Corrales, J.C.; Figueroa-Casas, A. Toward detecting crop diseases and pest by supervised learning. Ing. Univ. 2015, 19, 207–228. [Google Scholar] [CrossRef] [Green Version]
  20. Gandhi, N.; Armstrong, L.J.; Petkar, O. PredictingRice crop yield using Bayesian networks. In Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 21–24 September 2016; pp. 795–799. [Google Scholar]
  21. Musango, J.K.; Peter, C. A Bayesian approach towards facilitating climate change adaptation research on the South African agricultural sector. Agrekon 2007, 46, 245–259. [Google Scholar] [CrossRef]
  22. Ershadi, M.M.; Seifi, A. An efficient Bayesian network for differential diagnosis using experts’ knowledge. Int. J. Intell. Comput. Cybern. 2020, 13, 103–126. [Google Scholar] [CrossRef]
  23. Goldberger, A.L.; Amaral, L.A.N.; Glass, L.; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.-K.; Stanley, H.E. PhysioBank, PhysioToolkit, and PhysioNet. Circulation 2000, 101, e215–e220. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Rathore, S.; Hussain, M.; Aksam Iftikhar, M.; Jalil, A. Ensemble classification of colon biopsy images based on information rich hybrid features. Comput. Biol. Med. 2014, 47, 76–92. [Google Scholar] [CrossRef]
  25. Ferland-McCollough, D.; Slater, S.; Richard, J.; Reni, C.; Mangialardi, G. Pericytes, an overlooked player in vascular pathobiology. Pharmacol. Ther. 2017, 171, 30–42. [Google Scholar] [CrossRef] [PubMed]
  26. Dheeba, J.; Albert Singh, N.; Tamil Selvi, S. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 2014, 49, 45–52. [Google Scholar] [CrossRef]
  27. Hussain, L.; Saeed, S.; Awan, I.A.; Idris, A.; Nadeem, M.S.A.; Chaudhary, Q.-A. Detecting brain tumor using machine learning techniques based on different features extracting strategies. Curr. Med. Imaging Rev. 2019, 15, 595–606. [Google Scholar] [CrossRef] [PubMed]
  28. Hussain, L.; Ali, A.; Rathore, S.; Saeed, S.; Idris, A.; Usman, M.U.; Iftikhar, M.A.; Suh, D.Y. Applying Bayesian Network approach to determine the association between morphological features extracted from prostate cancer images. IEEE Access 2019, 7, 1586–1601. [Google Scholar] [CrossRef]
  29. Hussain, L.; Huang, P.; Nguyen, T.; Lone, K.J.; Ali, A.; Khan, M.S.; Li, H.; Suh, D.Y.; Duong, T.Q. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response. Biomed. Eng. Online 2021, 20, 1–23. [Google Scholar] [CrossRef]
  30. Hussain, L.; Ahmed, A.; Saeed, S.; Rathore, S.; Awan, I.A.; Shah, S.A.; Majid, A.; Idris, A.; Awan, A.A. Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies. Cancer Biomarkers 2018, 21, 393–413. [Google Scholar] [CrossRef]
  31. Anjum, S.; Hussain, L.; Ali, M.; Alkinani, M.H.; Aziz, W.; Gheller, S.; Duong, T.Q. Detecting brain tumors using deep learning convolutional neural network with transfer learning approach. Int. J. Imag. Sys. Tech. 2022, 32, 307–323. [Google Scholar] [CrossRef]
  32. Rathore, S.; Hussain, M.; Khan, A. Automated colon cancer detection using hybrid of novel geometric features and some traditional features. Comput. Biol. Med. 2015, 65, 279–296. [Google Scholar] [CrossRef]
  33. Baim, D.S.; Colucci, W.S.; Monrad, E.S.; Smith, H.S.; Wright, R.F.; Lanoue, A.; Gauthier, D.F.; Ransil, B.J.; Grossman, W.; Braunwald, E. Survival of patients with severe congestive heart failure treated with oral milrinone. J. Am. Coll. Cardiol. 1986, 7, 661–670. [Google Scholar] [CrossRef] [Green Version]
  34. Wilcoxon, F. Individual comparisons by ranking methods. In Biometrics Bulletin; Springer: New York, NY, USA, 1992; Volume 1, p. 80. [Google Scholar]
  35. Acharya, U.R.; Fujita, H.; Sudarshan, V.K.; Bhat, S.; Koh, J.E. Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowl. -Based Syst. 2015, 88, 85–96. [Google Scholar] [CrossRef]
  36. Haralick, R.M.; Shanmugam, K.; Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, SMC-3, 610–621. [Google Scholar] [CrossRef] [Green Version]
  37. Parvez, A.; Phadke, A.C. Efficient implementation of GLCM based texture feature computation using CUDA platform. In Proceedings of the 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, India, 11–12 May 2017; pp. 296–300. [Google Scholar]
  38. Singh, G.A.P.; Gupta, P.K. Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Comput. Appl. 2019, 31, 6863–6877. [Google Scholar] [CrossRef]
  39. Nithya, R. Comparative Study on Feature Extraction. J. Theor. Appl. Infrormation Technol. 2011, 33, 7. [Google Scholar]
  40. Wang, H.; Khoshgoftaar, T.M.; Gao, K. A comparative study of filter-based feature ranking techniques. In Proceedings of the 2010 IEEE International Conference on Information Reuse & Integration, Las Vegas, NV, USA, 4–6 August 2010; Volume 1, pp. 43–48. [Google Scholar]
  41. Yu, S.; Zhang, Z.; Liang, X.; Wu, J.; Zhang, E.; Qin, W.; Xie, Y. A matlab toolbox for feature importance ranking. In Proceedings of the 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE), Shenzhen, China, 22–24 November 2019; 2019; pp. 1–6. [Google Scholar]
  42. Pearl, J. Fusion, propagation, and structuring in belief networks. Artif. Intell. 1986, 29, 241–288. [Google Scholar] [CrossRef] [Green Version]
  43. Bayesia, S.C. BayesiaLab7. Bayesia USA: Franklin, TN, USA, 2017. [Google Scholar]
  44. Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef] [Green Version]
  45. Xiao, F.; Gao, L.; Ye, Y.; Hu, Y.; He, R. Inferring gene regulatory networks using conditional regulation pattern to guide candidate genes. PLoS ONE 2016, 11, e0154953. [Google Scholar] [CrossRef] [Green Version]
  46. Moreno-Jiménez, E.; García-Gómez, C.; Oropesa, A.L.; Esteban, E.; Haro, A.; Carpena-Ruiz, R.; Tarazona, J.V.; Peñalosa, J.M.; Fernández, M.D. Screening risk assessment tools for assessing the environmental impact in an abandoned pyritic mine in Spain. Sci. Total Environ. 2011, 409, 692–703. [Google Scholar] [CrossRef]
  47. Wilhere, G.F. Using Bayesian networks to incorporate uncertainty in habitat suitability index models. J. Wildl. Manag. 2012, 76, 1298–1309. [Google Scholar] [CrossRef]
Figure 1. Schematic diagram, based on Bayesian inference analysis, of target nodes based on extracted fuzzy entropy features.
Figure 1. Schematic diagram, based on Bayesian inference analysis, of target nodes based on extracted fuzzy entropy features.
Applsci 12 06350 g001
Figure 2. Feature ranking based on entropy values.
Figure 2. Feature ranking based on entropy values.
Applsci 12 06350 g002
Figure 3. Arc analysis mutual information (MI) with node size of normalized mean.
Figure 3. Arc analysis mutual information (MI) with node size of normalized mean.
Applsci 12 06350 g003
Figure 4. Dynamic tree analysis, based on the selected target node of dissimilarity.
Figure 4. Dynamic tree analysis, based on the selected target node of dissimilarity.
Applsci 12 06350 g004
Figure 5. Unsupervised clustering when dissimilarity was selected as the target node.
Figure 5. Unsupervised clustering when dissimilarity was selected as the target node.
Applsci 12 06350 g005
Figure 6. Significance of the dissimilarity node with all nodes at selected cluster states (≤349,738 (58.62%), ≤882,604 (34.48%), and >882,604 (5.89%)).
Figure 6. Significance of the dissimilarity node with all nodes at selected cluster states (≤349,738 (58.62%), ≤882,604 (34.48%), and >882,604 (5.89%)).
Applsci 12 06350 g006
Table 1. Node force of extracted GLCM features from congestive heart failure signals.
Table 1. Node force of extracted GLCM features from congestive heart failure signals.
NodeOutgoing ForceIncoming ForceTotal Force
Cluster prominance1.64280.00001.6428
Cluster shade0.00000.93300.9330
Table 2. Overall analysis of the highly ranked feature dissimilarity   (target feature) with other extracted features.
Table 2. Overall analysis of the highly ranked feature dissimilarity   (target feature) with other extracted features.
NodeMutual Information (MI)Normalized MIRelative MIRelative Significancep-Value
Cluster prominence1.014664.01%81.33%1.00000.0000
Cluster shade0.933058.86%74.79%0.91960.0000
Table 3. Local analysis with target states dissimilarity     349 , 738.09 ( 1 3 ) 58.62 % .
Table 3. Local analysis with target states dissimilarity     349 , 738.09 ( 1 3 ) 58.62 % .
NodeBinary MIRelative Binary SignificanceBinary Relative SignificanceMaximum Bayes Factor
Cluster prominence0.784780.19%1.000≤571,475 (1/3)92.64%1.7059
Cluster shade0.664067.86%0.8462≤163,215 (1/3)98.52%1.5657
Contrast0.395740.44%0.5043≤0.068 (1/3)60.29%1.7059
Autocorrelation0.08398.57%0.1069≤0.021 (1/3)88.73%1.1566
Energy0.08028.19%0.1021≤45,768 (1/3)65.96%1.2544
Entropy0.04814.92%0.0614≤57,145 (1/3)73.25%1.1640
Correlation0.02792.85%0.0356≤0.041 (1/3)80.88%1.0910
Correlation20.02152.19%0.0274≤0.058 (1/3)87.33%1.0664
Homogenity10.01121.14%0.0143>2.201 (1/3)9.03%1.1640
Table 4. Local analysis with target states dissimilarity     882 , 604.41 ( 2 3 ) 34.48 % .
Table 4. Local analysis with target states dissimilarity     882 , 604.41 ( 2 3 ) 34.48 % .
NodeBinary MIRelative Binary SignificanceBinary Relative SignificanceMaximum Bayes Factor
Cluster prominence0.696674.95%1.000≤1,433,166.15 (2/3)97.50%2.5705
Cluster shade0.615066.17%0.84828≤411,098.14 (2/3)85.00%2.8171
Contrast0.299332.20%0.4297≤0.105 (2/3)84.20%1.6841
Energy0.04414.74%0.0633>129,593.07 (2/3)11.90%1.3809
Entropy0.02592.78%0.0372>156,446.10 (3/3)5.92%1.3751
Autocorrelation0.01601.71%0.0229>0.041 (3/3)6.73%1.5628
Correlation0.00690.74%0.0100>0.089 (2/3)13.01%1.3722
Homogenity10.00630.67%0.0090≤1.241 (1/3)56.321.1265
Correlation20.00550.58%0.0078>0.124 (3/3)8.28%1.3722
Table 5. Local analysis with target states dissimilarity     882 , 604.41 ( 3 3 ) 6.89 % .
Table 5. Local analysis with target states dissimilarity     882 , 604.41 ( 3 3 ) 6.89 % .
NodeBinary MIRelative Binary SignificanceBinary Relative SignificanceMaximum Bayes Factor
Cluster shade0.3621100%1.000>411,098 (3/3)100%14.500
Cluster prominence0.323089.21%0.8922>1,433,166 (3/3)100%12.888
Contrast0.215959.62%0.5962>0.105 (3/3)100%6.8235
Autocorrelation0.090725.06%0.2506≤0.041 (2/3)76.47%4.032
Energy0.02877.94%0.0794>129,593 (3/3)29.41%3.411
Correlation0.02416.65%0.0665>0.089 (3/3)25.75%2.716
Correlation20.01714.73%0.0473>0.124 (3/3)16.39%2.716
Entropy0.01544.25%0.0426>156,446 (3/3)12.81%2.972
Homogenity10.00330.90%0.0090≤1.241 (1/3)62.03%1.240
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Eltahir, M.M.; Hussain, L.; Malibari, A.A.; K. Nour, M.; Obayya, M.; Mohsen, H.; Yousif, A.; Ahmed Hamza, M. A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure. Appl. Sci. 2022, 12, 6350.

AMA Style

Eltahir MM, Hussain L, Malibari AA, K. Nour M, Obayya M, Mohsen H, Yousif A, Ahmed Hamza M. A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure. Applied Sciences. 2022; 12(13):6350.

Chicago/Turabian Style

Eltahir, Majdy M., Lal Hussain, Areej A. Malibari, Mohamed K. Nour, Marwa Obayya, Heba Mohsen, Adil Yousif, and Manar Ahmed Hamza. 2022. "A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure" Applied Sciences 12, no. 13: 6350.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop