Congestive Heart Failure Detection Based on Electrocardiomatrix Method with ECG Signal

  • B. Mohan Rao Department of Electronics and Communication Engineering, NIT Hamirpur, India
  • Aman Kumar Department of Electronics and Communication Engineering, NIT Hamirpur, India


Congestive heart failure (CHF) is a prevalent, expensive to treat, and dangerous disease in which the pumping capacity of the heart muscle is reduced due to injury or stress. It causes major medical problems in humans and contribute to many diseases, thus increasing the mortality rate. In a world with a growing population, there is a need for more precise and simpler approaches to detect such conditions, which can prevent many diseases and lead to a lower mortality rate. The main goal here is to use electrocardiomatrix (ECM) approach to perform the task of detecting CHF. It is detected quickly and accurately with this approach, as ECM converts 2D electrocardiogram (ECG) data into a 3D-colored matrix. The approach is tested using ECG readings from the Beth Israel Deaconess Medical Center (BIDMC) CHF Database on the Internet ( The ECM outcomes of are then compared to manual readings of ECG data. The ECM results achieved the accuracy of 96.89%, the sensitivity of 97.53%, the precision of 99.1%, the F1-score of 97.76%, and the specificity of 96.02% for CHF. This research shows that the ECM approach is a good way for machines and practitioners to interpret long-term ECG readings while maintaining accuracy.


electrocardiomatrix, ECG signal, congestive heart failure,


1. Premature Ventricular Contractions (PVCs) and Premature Atrial Contractions (PACs), Frankel Cardiovascular Center, University of Michigan Health,
2. What Are Premature Atrial Contractions?, WebMD,
3. Supraventricular premature beats, Knowledge for medical students and physicians, AMBOSS, 2022,
4. B. Surawicz, R. Childers, B.J. Deal, L.S. Gettes, AHA/ACCF/HRS Recommendations for the standardization and interpretation of the electrocardiogram, Circulation, 119(10): e235–e240, 2009, doi: 10.1161/circulationaha.108.191095.
5. ECG Basics Tutorial – Complete 12-lead ECG Review,, Healio,
6. T.R. Engel, S.G. Meister, W.S. Frankl, The “R-on-T” phenomenon: An update and critical review, Annals of Internal Medicine, 88(2): 221–225, 1978, doi: 10.7326/0003-4819-88-2-221.
7. Y. Isler, M. Kuntalp, Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure, Computers in Biology and Medicine, 37(10): 1502–1510, 2007, doi: 10.1016/j.compbiomed.2007.01.012.
8. I. Awan et al., Studying the dynamics of inter-beat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis, PLoS One, 13: e0196823, 2018, doi: 10.1371/journal.pone.0196823.
9. W. Aziz, M. Rafique, I. Ahmad, M. Arif, N. Habib, M. Nadeem, Classification of heart rate signals of healthy and pathological subjects using threshold-based symbolic entropy, Acta Biologica Hungarica, 65(3): 252–264, 2014, doi: 10.1556/ABiol.65.2014.3.2.
10. A. Hossen, B. Al-Ghunaimi, A wavelet-based soft decision technique for screening of patients with congestive heart failure, Biomedical Signal Processing and Control, 2(2): 135–143, 2007, doi: 10.1016/j.bspc.2007.05.008.
11. S.N. Yu, M.Y. Lee, Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability, Computer Methods and Programs in Biomedicine, 108(1): 299–309, 2012, doi: 10.1016/j.cmpb.2011.12.015.
12. L. Pecchia, P. Melillo, M. Sansone, M. Bracale, Discrimination power of short-term heart rate variability measures for CHF assessment, IEEE Transactions on Information Technology in Biomedicine, 15(1): 40–46, 2011, doi: 10.1109/TITB.2010.2091647.
13. G. Altan, Y. Kutlu, N. Allahverdi, A new approach to early diagnosis of congestive heart failure disease by using Hilbert–Huang transform, Computer Methods and Programs in Biomedicine, 137: 23–34, 2016, doi: 10.1016/j.cmpb.2016.09.003.
14. G.I. Choudhary, W. Aziz, I.R. Khan, S. Rahardja, P. Fränti, Analysing the dynamics of interbeat interval time series using grouped horizontal visibility graph, IEEE Access, 7: 9926–9934, 2019, doi: 10.1109/ACCESS.2018.2890542.
15. Y. Isler, A. Narin, M. Ozer, M. Perc, Multi-stage classification of congestive heart failure based on short-term heart rate variability, Chaos Solitons and Fractals, 118: 145–151, 2019, doi: 10.1016/j.chaos.2018.11.020.
16. A. Narin, Y. Isler, M. Ozer, M. Perc, Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability, Physica A: Statistical Mechanics and its Applications, 509: 56–65, 2018, doi: 10.1016/j.physa.2018.06.022.
17. T. Jagric et al., Irregularity test for very short electrocardiogram (ECG) signals as a method for predicting a successful defibrillation in patients with ventricular fibrillation, Translational Research, 149(3): 145–151, 2007, doi: 10.1016/j.trsl.2006.09.004.
18. K.H. Yoon et al., Analysis of statistical methods for automatic detection of congestive heart failure and atrial fibrillation with short RR interval time series, [in:] 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Santa Catarina, Brazil, pp. 452–457, 2015, doi: 10.1109/imis.2015.88.
19. J.B. O’Connell, The economic burden of heart failure, Clinical Cardiology, 23(Suppl 3): III6–III10, 2000, doi: 10.1002/clc.4960231503.
20. M.W. Rich, Epidemiology, pathophysiology, and etiology of congestive heart failure in older adults, Journal of the American Geriatrics Society, 45(8): 968–974, 1997, doi: 10.1111/j.1532-5415.1997.tb02968.x.
21. Z. Masetic, A. Subasi, Congestive heart failure detection using random forest classifier, Computer Methods and Programs in Biomedicine, 130: 54–64, 2016, doi: 10.1016/j.cmpb.2016.03.020.
22. R.K. Tripathy, M.R.A. Paternina, J.G. Arrieta, A. Zamora-Méndez, G.R. Naik, Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme, Computer Methods and Programs in Biomedicine, 173: 53–65, 2019, doi: 10.1016/j.cmpb.2019.03.008.
23. M. Kumar, R.B. Pachori, U.R. Acharya, Use of accumulated entropies for automated detection of congestive heart failure in flexible analytic wavelet transform framework based on short-term HRV signals, Entropy, 19(3): 92, 2017, doi: 10.3390/e19030092.
24. R. Mahajan, T. Viangteeravat, O. Akbilgic, Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics, International Journal of Medical Informatics, 108: 55–63, 2017, doi: 10.1016/j.ijmedinf.2017.09.006.
25. L. Wang, W. Zhou, Q. Chang, J. Chen, X. Zhou, Deep ensemble detection of congestive heart failure using short-term RR intervals, IEEE Access, 7: 69559–69574, 2019, doi: 10.1109/access.2019.2912226.
26. U.R. Acharya et al., Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals, Applied Intelligence, 49(1): 16–27, 2019, doi: 10.1007/s10489-018-1179-1.
27. D. Li, F. Tian, S. Rengifo, G. Xu, M.M. Wang, J. Borjigin, Electrocardiomatrix: A new method for beat-by-beat visualization and inspection of cardiac signals, Journal of Integrative Cardiology, 1(5): 124–128, 2015, doi: 10.15761/jic.1000133.
28. V. Lee, G. Xu, P. Farrehi, J. Borjigin, Accurate detection of atrial fibrillation and atrial flutter using the electrocardiomatrix technique, Journal of Electrocardiology, 51(6): S121–S125, 2019, doi: 10.1016/j.jelectrocard.2018.08.011.
29. D.L. Brown et al., Electrocardiomatrix facilitates accurate detection of atrial fibrillation in stroke patients, Stroke, 50(7): 1676–1681, 2019, doi: 10.1161/strokeaha.119.025361.
30. H.F. Jelinek, D.J. Cornforth, A.H. Khandoker [Eds.], ECG Time Series Variability Analysis: Engineering and Medicine, CRC Press, Boca Raton, 2017, doi: 10.4324/9781315372921.
31. The BIDMC Congestive Heart Failure Database, PhysioBank ATM, PhysioNet,, doi: 10.13026/C29G60.
32. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, IEEE Transactions on Biomedical Engineering, 32(3): 230–236, 1985, doi: 10.1109/TBME.1985.325532.
33. L. Hussain, W. Aziz, I.R. Khan, M.H. Alkinani, J.S. Alowibdi, Machine learning based congestive heart failure detection using feature importance ranking of multimodal features, Mathematical Biosciences and Engineering, 18(1): 69–91, 2021, doi: 10.3934/mbe.2021004.
34. K. Sharma, B.M. Rao, P. Marwaha, A. Kumar, Accurate detection of congestive heart failure using electrocardiomatrix technique, Multimedia Tools and Applications, 81(21): 30007–30023, 2022, doi: 10.1007/s11042-022-12773-8.
May 30, 2023
How to Cite
RAO, B. Mohan; KUMAR, Aman. Congestive Heart Failure Detection Based on Electrocardiomatrix Method with ECG Signal. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 3, p. 291–304, may 2023. ISSN 2956-5839. Available at: <>. Date accessed: 21 june 2024. doi: