Detection of congestive heart failure based on Gramian angular field and two-dimensional symbolic phase permutation entropy

• Autorzy: Yang Juanjuan , Xi Caiping

Identyfikatory

DOI

10.1016/j.bbe.2024.06.005

• Języki publikacji: EN

Abstrakty

EN

Congestive heart failure (CHF) is a serious threat to human health. Electrocardiogram (ECG) signals have been proven to be useful in the detection of CHF. However, the low amplitude and short duration of the ECG signals, as well as the superimposed noise during the real-time acquisition of the signal, seriously affect the CHF detection. To improve the detection rate of CHF, this paper proposes a congestive heart failure detection method based on Gramian angular field (GAF) and two-dimensional symbolic phase permutation entropy (SPPE2D). The significant advantage of this method is that it reduces the sensitivity to noise, and good performance can be obtained without denoising using raw ECG signals. We segment the original ECG signals into 2 s non-overlapping segments and convert them into images using the GAF method. Then, the SPPE2D algorithm is proposed to measure the complexity between normal sinus rhythm (NSR) and CHF, and analyze the anti-noise performance of the algorithm. Finally, the SPPE2D features of GAF images are computed and input into a support vector machine (SVM) for CHF detection. Classification accuracy on the Massachusetts Institute of Technology - Beth Israel Hospital Normal Sinus Rhythm Database and Beth Israel Deaconess Medical Center Congestive Heart Failure Database is 99.59%, sensitivity is 99.42%, specificity is 99.80%, and F1-score is 99.62%. The accuracy of detecting CHF reach more than 97.75% in the other five CHF databases. The experimental results show that the method based on GAF and SPPE2D can effectively detect CHF by images of ECG signals and has good robustness. CHF can be detected using the 2 s sample lengths of ECG signals recording with high sensitivity, giving clinicians ample time to treat patients with CHF.

Słowa kluczowe

EN

congestive heart failure; electrocardiogram; Gramian angular field; twodimensional symbolic phase permutation entropy

PL

zastoinowa niewydolność serca; elektrokardiogram; pole kątowe Gramiana

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 3
• Strony: 674--688
• Opis fizyczny: Bibliogr. 69 poz., rys., tab., wykr.

Twórcy

autor

Yang Juanjuan

• Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

autor

Xi Caiping

• College of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Bibliografia

• [1] Groenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. Eur J Heart Fail 2020;22:1342-56. https://doi.org/10.1002/ejhf.1858.
• [2] Dunlay SM, Roger VL. Understanding the Epidemic of Heart Failure: Past, Present, and Future. Curr Heart Fail Rep 2014;11:404-15. https://doi.org/10.1007/s11897-014-0220-x.
• [3] Narin A, Isler Y, Ozer M. Investigating the performance improvement of HRV indices in CHF using feature selection methods based on backward elimination and statistical significance. Comput Biol Med 2014;45:72-9. https://doi.org/10.1016/j.compbiomed.2013.11.016.
• [4] Acharya UR, Fujita H, Sudarshan VK, Lih OS, Muhammad A, Koh JEW, et al. Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals. Neural Comput & Applic 2017; 28:3073-94. https://doi.org/10.1007/s00521-016-2612-1.
• [5] Kumar M, Pachori R, Acharya U. Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals. Entropy 2017;19:92. https://doi.org/10.3390/e19030092.
• [6] Sharma RR, Kumar A, Pachori RB, Acharya UR. Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals. Biocybernetics and Biomedical Engineering 2019;39:312-27. https://doi.org/10.1016/j.bbe.2018.10.001.
• [7] Hu B, Wei S, Wei D, Zhao L, Zhu G, Liu C. Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability. IEEE Access 2019;7:17862-71. https://doi.org/10.1109/ACCESS.2019.2895998.
• [8] Wang Z, Oates T. Imaging Time-Series to Improve Classification and Imputation 2015.
• [9] Narotamo H, Dias M, Santos R, Carreiro AV, Gamboa H, Silveira M. Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches. Biomed Signal Process Control 2024;93:106141. https://doi.org/10.1016/j.bspc.2024.106141.
• [10] Ramla M, Ramesh M. Building a Deep Learning model using Grammian Angular Field Encoding of Time-Series Cardiotocography Images. Journal of Pharmaceutical Negative Results 2022;13:2412-8. https://doi.org/10.47750/pnr.2022.13.S09.286.
• [11] Tang L, Liu G. In: Obstructive Sleep Apnea Heart Rate Variability Analysis using Gramian Angular Field images and Two-dimensional Sample Entropy. Xiamen China: ACM; 2021. p. 89-94. https://doi.org/10.1145/3498731.3498744.
• [12] Yousuf A, Hafiz R, Riaz S, Farooq M, Riaz K, Rahman MMU. Myocardial Infarction Detection from ECG: A Gramian Angular Field-based 2D-CNN Approach 2023. DOI: 10.48550/arXiv.2302.13011.
• [13] Camara C, Peris-Lopez P, Safkhani M, et al. ECG identification based on the gramian angular field and tested with individuals in resting and activity states. Sensors 2023;23:937. https://doi.org/10.3390/s23020937.
• [14] Elmir Y, Himeur Y, Amira A. ECG classification using Deep CNN and Gramian Angular Field. 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), Athens, Greece: IEEE; 2023, p. 137-41. DOI: 10.1109/BigDataService58306.2023.00026.
• [15] Freitas PG, De Lima RG, Lucafo GD, Penatti OAB, Assessing the Quality of Photoplethysmograms via Gramian Angular Fields and Vision Transformer. 31st European Signal Processing Conference (EUSIPCO). Helsinki, Finland: IEEE 2023; 2023:1035-9. https://doi.org/10.23919/EUSIPCO58844.2023.10290014.
• [16] Wu H. Multiscale entropy with electrocardiograph, electromyography, electroencephalography, and photoplethysmography signals in healthcare: A twelve-year systematic review. Biomed Signal Process Control 2024;93:106124. https://doi.org/10.1016/j.bspc.2024.106124.
• [17] Lahoti S. An Upgraded Entropy and Fractal Investigation of HRV Signal for Identification of Heart Dynamics-A Multiscale Methodology. International Journal of Intelligent Systems and Applications in Engineering 2023;11:259-67. https://ijisae.org/index.php/IJISAE/article/view/3048.
• [18] Yan X, Zhang L, Li J, et al. Entropy-based measures of hypnopompic heart rate variability contribute to the automatic prediction of cardiovascular events. Entropy 2020;22:241. https://doi.org/10.3390/e22020241.
• [19] Asgharzadeh-Bonab A, Amirani MC, Mehri A. Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybernetics and Biomedical Engineering 2020;40:691-700. https://doi.org/10.1016/j.bbe.2020.02.004.
• [20] Shi M, He H, Geng W, Wu R, Zhan C, Jin Y, et al. Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals. Front Physiol 2020; 11:118. https://doi.org/10.3389/fphys.2020.00118.
• [21] Shao S, Wang T, Mumtaz A, Song C, Yao C. Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network. IEEE J Biomed Health Inform 2022:1. https://doi.org/10.1109/JBHI.2022.3162894.
• [22] Volpes G, Barà C, Busacca A, Stivala S, Javorka M, Faes L, et al. Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures. Sensors 2022;22: 9149. https://doi.org/10.3390/s22239149.
• [23] Escribano P, Ródenas J, García M, Hornero F, Gracia-Baena JM, Alcaraz R, et al. Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis. Entropy 2023;26:28. https://doi.org/10.3390/e26010028.
• [24] Arsac LM. Entropy-Based Multifractal Testing of Heart Rate Variability during Cognitive-Autonomic Interplay. Entropy 2023;25:1364. https://doi.org/10.3390/e25091364.
• [25] Associate Professor, Cauvery College for Women (Autonomous), [Affiliated to Bharathidasan University], Tiruchirappalli, 620 018, Tamil Nadu, India, Muthulakshmi P, Parveen M. Big Data Analytics for Heart Disease Prediction using Regularized Principal and Quadratic Entropy Boosting. IJST 2024;17:533-47. DOI: 10.17485/IJST/v17i6.2928.
• [26] Soni A, Rawal K. Analyzing the effect of postural change on heart rate variability using multi-distance sample entropy (MDSE). Biomed Signal Process Control 2024; 87:105476. https://doi.org/10.1016/j.bspc.2023.105476.
• [27] Mayor D, Steffert T, Datseris G, Firth A, Panday D, Kandel H, et al. Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy. Entropy 2023;25:301. https://doi.org/10.3390/e25020301.
• [28] Lin Z, Zhang Z, Xiao M, Xiao Z, Wang Q, Jiang Q. Application of permutation entropy and factorization network in the classification of congenital heart disease. Expert Syst 2023;40:e12877.
• [29] Kumar M, Pachori RB, Rajendra AU. Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocybernetics and Biomedical Engineering 2018;38:564-73. https://doi.org/10.1016/j.bbe.2018.04.004.
• [30] Koh JoelEW, Ooi CP, Lim-Ashworth NSj, Vicnesh J, Tor HT, Lih OS, et al. Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals. Computers in Biology and Medicine 2022;140:105120. DOI: 10.1016/j.compbiomed.2021.105120.
• [31] Lahmiri S. A wavelet leaders model with multiscale entropy measures for diagnosing arrhythmia and congestive heart failure. Healthcare Analytics 2023;3: 100171. https://doi.org/10.1016/j.health.2023.100171.
• [32] Sharma K, Sunkaria RK. Novel multiscale E-metric cross-sample entropy-based cardiac arrhythmia detection and its performance investigation in reference to multiscale cross-sample entropy-based analysis. SIViP 2023;17:2845-56. https://doi.org/10.1007/s11760-023-02503-4.
• [33] Jafal NJ, Hariyani YS, Hadiyoso S. Classification of ECG signal-based cardiac abnormalities using fluctuation-based dispersion entropy and first-order statistics. JINFOTEL 2022;14:85-92. https://doi.org/10.20895/infotel.v14i2.768.
• [34] Kim Y, Choi Y-S. Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals. Entropy 2023;25:1562. https://doi.org/10.3390/e25111562.
• [35] Rohila A, Sharma A. Phase entropy: a new complexity measure for heart rate variability. Physiol Meas 2019;40:105006. https://doi.org/10.1088/1361-6579/ab499e.
• [36] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 2000:101. https://doi.org/10.1161/01.CIR.101.23.e215.
• [37] Wu S-D, Wu C-W, Humeau-Heurtier A. Refined scale-dependent permutation entropy to analyze systems complexity. Physica A 2016;450:454-61. https://doi.org/10.1016/j.physa.2016.01.044.
• [38] Kang H, Zhang X, Zhang G. Phase permutation entropy: A complexity measure for nonlinear time series incorporating phase information. Physica A 2021;568: 125686. https://doi.org/10.1016/j.physa.2020.125686.
• [39] Wang W, Zheng H, Liu Y, Li Z, Zhang T, Zhang W. Photoluminescence and ferroelectric properties of sol-gel-grown Eu-doped CaBi4 Ti4O15: Nd films. J Phys D: Appl Phys 2009;42:105411. https://doi.org/10.1088/0022-3727/42/10/105411.
• [40] Zhang N, Lin A, Shang P. Multiscale Symbolic Phase Transfer Entropy in Financial Time Series Classification. Fluct Noise Lett 2017;16:1750019. https://doi.org/10.1142/S0219477517500195.
• [41] Yao W, Wang J. Differential symbolic entropy in nonlinear dynamics complexity analysis 2018.
• [42] Weking PNM, Partha IGNW, Weking AI. Application of Data Mining with Support Vector Machine (SVM) in Selling Prediction Trend of Spiritual Goods (Case Study: PT. X Bali) International Journal of Engineering and Emerging Technology 2019.
• [43] Alkan A, Günay M. Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 2012;39:44-7. https://doi.org/10.1016/j.eswa.2011.06.043.
• [44] Subasi A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 2013;43:576-86. https://doi.org/10.1016/j.compbiomed.2013.01.020.
• [45] Palaniappan R, Sundaraj K, Sundaraj S. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinf 2014;15:223. https://doi.org/10.1186/1471-2105-15-223.
• [46] Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput & Applic 2014;24: 1163-77. https://doi.org/10.1007/s00521-012-1324-4.
• [47] Singh BN, Tiwari AK. Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Process 2006;16:275-87. https://doi.org/10.1016/j.dsp.2005.12.003.
• [48] El Boujnouni I, Harouchi B, Tali A, Rachafi S, Laaziz Y. Automatic diagnosis of cardiovascular diseases using wavelet feature extraction and convolutional capsule network. Biomed Signal Process Control 2023;81:104497. https://doi.org/10.1016/j.bspc.2022.104497.
• [49] Pachori RB, Avinash P, Shashank K, Sharma R, Acharya UR. Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Syst Appl 2015;42:4567-81. https://doi.org/10.1016/j.eswa.2015.01.051.
• [50] Hussain L, Awan IA, Aziz W, Saeed S, Ali A, Zeeshan F, et al. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. Biomed Res Int 2020;2020:1-19. https://doi.org/10.1155/2020/4281243.
• [51] Wang C-C, Chang C-D. SVD and SVM based approach for congestive heart failure detection from ECG signal. In: The 40th International Conference on Computers & Industrial Engineering, Awaji City. Japan: IEEE; 2010. p. 1-5. https://doi.org/10.1109/ICCIE.2010.5668319.
• [52] Isler Y, Ozturk U, Sayilgan E. A new sample reduction method for decreasing the running time of the k-nearest neighbors algorithm to diagnose patients with congestive heart failure: backward iterative elimination. Sādhanā 2023;48:35. https://doi.org/10.1007/s12046-023-02105-3.
• [53] Tripathy RK, Paternina MRA, Arrieta JG, Zamora-Méndez A, Naik GR. Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. Comput Methods Programs Biomed 2019;173:53-65. https://doi.org/10.1016/j.cmpb.2019.03.008.
• [54] Hussain L, Aziz W, Khan IR, Alkinani MH, Alowibdi JS, Department of Computer & AI, University of Jeddah, Jeddah. 23890, Saudi Arabia, et al. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features. MBE 2021;18:69-91. https://doi.org/10.3934/mbe.2021004.
• [55] Selek MB, Yesilkaya B, Egeli SS, Isler Y. The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis. Proc Inst Mech Eng H 2021;235:1479-88. https://doi.org/10.1177/09544119211036806.
• [56] Sarshar NT, Abdossalehi M. Congestive Heart Failure from ECG Prediction Using Empirical wavelets transform Algorithm 2022;36:1-16. https://sanad.iau.ir/journal/spre/Article/685794?jid=685794.
• [57] Josan GA, Bustamam A, Sarwinda D, Hermawan GA, Mangunwardoyo W, et al. Automated Congestive Heart Failure Detection Using XGBoost on Short-term Heart Rate Variability. In: 2023 Eighth International Conference on Informatics and Computing (ICIC). Manado, Indonesia: IEEE; 2023. p. 1-6. https://doi.org/10.1109/ICIC60109.2023.10381940.
• [58] Sharma M, Patel S, Acharya UR. Expert system for detection of congestive heart failure using optimal wavelet and heart rate variability signals for wireless cloud-based environment. Expert Syst 2023;40:e12903.
• [59] Manilo L, Kholmatov D, Nemirko A. Automatic Analysis of Heart Failure by Statistical and Nonlinear Measures of Heart Rate Variability. 2023 Systems and Technologies of the Digital HealthCare (STDH), Tashkent, Uzbekistan: IEEE; 2023, p. 73-6. DOI: 10.1109/STDH59314.2023.10490875.
• [60] Moses JC, Adibi S, Angelova M, Islam SMS. Time-domain heart rate variability features for automatic congestive heart failure prediction. ESC Heart Failure 2024; 11:378-89. https://doi.org/10.1002/ehf2.14593.
• [61] Victor OA, Chen Y, Ding X. Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms. Sensors 2024;24:2248. https://doi.org/10.3390/s24072248.
• [62] Zhang Y, Xia M. Application of Deep Neural Network for Congestive Heart Failure Detection Using ECG Signals. J Phys: Conf Ser 2020;1642:012021. https://doi.org/10.1088/1742-6596/1642/1/012021.
• [63] Lei M, Li J, Li M, Zou L, Yu H. An Improved UNet++ Model for Congestive Heart Failure Diagnosis Using Short-Term RR Intervals. Diagnostics 2021;11:534. https://doi.org/10.3390/diagnostics11030534.
• [64] Kusuma S, Jothi KR. ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture. Biocybernetics and Biomedical Engineering 2022;42:247-57. https://doi.org/10.1016/j.bbe.2022.02.003.
• [65] Sun M, Si Y, Yang W, Fan W, Zhou L. Inter-patient congestive heart failure automatic recognition using attention-based multi-scale convolutional neural network. Measurement 2023;218:113239. https://doi.org/10.1016/j.measurement.2023.113239.
• [66] Padmavathi C, Veenadevi SV. An Automated Embedded Distribution of Deep Learning Heart Disease Identification System Using ECG Signal. International Journal of Intelligent Systems and Applications in Engineering 2024;12:15-26.
• [67] Yang W, Wang D, Zou S, Fan W, Li C, Zhang G, et al. Automatic recognition of coronary artery disease and congestive heart failure using a multi-granularity cascaded hybrid network. Biomed Signal Process Control 2023;86:105332. https://doi.org/10.1016/j.bspc.2023.105332.
• [68] Prusty MR, Pandey TN, Lekha PS, Lellapalli G, Gupta A. Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals. Sci Rep 2024;14:2633.
• [69] Bharath P, Nahak S, Saha G. A ResNet-Attention Approach for Detection of Congestive Heart Failure from ECG Signals. In: 2024 National Conference on Communications (NCC). Chennai, India: IEEE; 2024. p. 1-6. https://doi.org/10.1109/NCC60321.2024.10485847.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).