Skip to main content
SpringerLink
Log in

Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This work proposes a novel method for the detection of Left Ventricular Hypertrophy (LVH) from a multi-lead ECG signal. Left Ventricle walls become thick due to prolonged hypertension which may fail to pump heart effectively. The imaging techniques can be used as an alternative diagnose LVH; however, they are more expensive and time-consuming than proposed LVH. To overcome this issue, an algorithm to the diagnosis of LVH using ECG signal based on machine learning techniques were designed. In LVH detection, the pathological attributes such as R wave, S wave, inversion of QRS complex, changes in ST segment noticed in the ECG signal. This clinical information extracted as a feature by applying continuous wavelet transform. The signals were reconstructed with the frequency between 10 and 50 Hz from the wavelet. This followed by the detection of R wave and S wave peaks to obtain the relevant LVH diagnostic features. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Ensemble of Bagged Tree, AdaBoost classifiers were employed and the results are compared with four neural network classifiers including Multilayer Perceptron (MLP), Scaled Conjugate Gradient Backpropagation Neural Network (SCG NN), Levenberg–Marquardt Neural Network (LMNN) and Resilient Backpropagation Neural network (RPROP). The data source includes Left Ventricular Hypertrophy and healthy ECG signal from PTB diagnostic ECG database and St Petersburg INCART 12-Lead Arrhythmia Database. The results revealed that the proposed work can diagnose LVH successfully using neural network classifiers. The accuracy in detecting LVH is 86.6%, 84.4%, 93.3%,75.6%, 95.6%, 97.8%, 97.8%, 88.9% using SVM, KNN, Ensemble of Bagged Tree, AdaBoost, MLP, SCG NN, LMNN and RPROP classifiers, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bacharova L, Harvey Estes E (2017) Left ventricular hypertrophy by the surface ECG. J Electrocardiol 50(6):906–908

    Article  Google Scholar 

  2. Linhart A, Cecchi F (2018) Common presentation of rare diseases: Left ventricular hypertrophy and diastolic dysfunction. Int J Cardiol 257:344–350

    Article  Google Scholar 

  3. Pinto J, George P, Hegde N (2014) Study in Southern India among hypertensive patients using ecg to screen left ventricular hypertrophy-can we do it in rural health centres? J Clin Diagn Res JCDR 8(3):59

    Google Scholar 

  4. Schillaci G, Verdecchia P, Borgioni C, Ciucci A, Guerrieri M, Zampi I, Battistelli M, Bartoccini C, Porcellati C (1994) Improved electrocardiographic diagnosis of left ventricular hypertrophy. Am J Cardiol 74(7):714–719

    Article  Google Scholar 

  5. Yang X, Fan D, Ren A, Zhao N, Shah SA, Alomainy A, Ur-Rehman M, Abbasi Qammer H (2020) Diagnosis of the Hypopnea syndrome in the early stage. Neural Comput Appl 32(3):855–866

    Article  Google Scholar 

  6. Sharma LN, Tripathy RK, Samarendra D (2015) Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 62(7):1827–1837

    Article  Google Scholar 

  7. Kumar KS, Babak Y, Rajesh Kumar P (2015) Removal of noise from electrocardiogram using digital FIR and IIR filters with various methods. In: 2015 International conference on communications and signal processing (ICCSP), pp 0157–0162. IEEE

  8. Amin W, Davis MR, Thomas GA, Holloway DS (2013) Analysis of wave slam induced hull vibrations using continuous wavelet transforms. Ocean Eng 58:154–166

    Article  Google Scholar 

  9. Stepanov AB (2017) Wavelet analysis of compressed biomedical signals. In: Open innovations association (FRUCT), 20th Conference of 2017, pp 434–440. IEEE

  10. Provaznik I (2002) Wavelet analysis for signal detection—application to experimental cardiology research. Ph.D. dissertation, Dept. Biomed. Eng., Brno University of technology

  11. Nayak SK, Banerjee I, Pal K (2019) Electrocardiogram signal processing-based diagnostics: applications of wavelet transform. In: Pal K, Kraatz H-B, Khasnobish A, Bag S, Banerjee I, Kuruganti U (eds) Bioelectronics and Medical Devices, pp 591–614. Woodhead Publishing, Cambridge

    Chapter  Google Scholar 

  12. Addison PS (2018) Introduction to redundancy rules: the continuous wavelet transform comes of age. Philos Trans R Soc A Math Phys Eng Sci 376(2126):1–15

    MathSciNet  MATH  Google Scholar 

  13. Veroy KPL (2000) Time-frequency analysis of Lamb waves using the Morlet wavelet transform.” Ph.D. diss., Massachusetts Institute of Technology

  14. Baloglu UB, Talo M, Yildirim O, San Tan R, Rajendra Acharya U (2019) Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn Lett 122:23–30

    Article  Google Scholar 

  15. Turhan M, Şengur D, Karabatak S, Guo Y, Smarandache F (2018) Neutrosophic weighted support vector machines for the determination of school administrators who attended an action learning course based on their conflict-handling styles. Symmetry 10(5):176

    Article  Google Scholar 

  16. Hofmann M (2006) Support vector machines—Kernels and the kernel trick. Notes 26:1–16

    Google Scholar 

  17. Parikh KS, Shah TP (2016) Support vector machine–a large margin classifier to diagnose skin illnesses. Proc Technol 23:369–375

    Article  Google Scholar 

  18. He R, Wang K, Li Q, Yuan Y, Zhao N, Liu Y, Zhang H (2017) A novel method for the detection of R-peaks in ECG based on K-nearest neighbors and particle swarm optimization. EURASIP J Adv Signal Process 2017(1):82

    Article  Google Scholar 

  19. Malini Suvarna V (2015) Performance measure and efficiency of chemical skin burn classification using KNN Method. In: International conference on eco-friendly computing and communication systems, ICECCS2015, no. 70, pp 48–54

  20. Saini I, Singh D, Khosla A (2013) QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases. J Adv Res 4(4):331–344

    Article  Google Scholar 

  21. Acharya UR, Fujita H, Sudarshan VK, Sree VS, Eugene LWJ, Ghista DN, SanTan R (2015) An integrated index for detection of sudden cardiac death using discrete wavelet transform and nonlinear features. Knowledge-Based Syst 83(2015):149–158

    Article  Google Scholar 

  22. Li Y, Cui W (2019) Identifying the mislabeled training samples of ECG signals using machine learning. Biomed Signal Process Control 47:168–176

    Article  Google Scholar 

  23. Zareapoor M, Shamsolmoali P (2015) Application of credit card fraud detection: based on bagging ensemble classifier. Proc Comp Sci 48(2015):679–685

    Article  Google Scholar 

  24. Al-Barazanchi KK, Al-Neami AQ, Al-Timemy AH (2017) Ensemble of bagged tree classifier for the diagnosis of neuromuscular disorders. In: Advances in Biomedical Engineering (ICABME), 2017 Fourth International Conference on, pp 1–4. IEEE

  25. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen CG, Barman SA (2012) Retinal vessel segmentation using ensemble classifier of bagged decision trees. In: Image processing (IPR 2012), IET conference on, pp 1–6

  26. Acharya UR, Faust O, Kadri NA, Suri JS, Yu W (2013) Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput Biol Med 43(10):1523–1529

    Article  Google Scholar 

  27. Wang R (2012) AdaBoost for feature selection, classification and its relation with SVM, a review. Phys Proc 25:800–807

    Article  Google Scholar 

  28. Tharwat A (2016) Linear vs. quadratic discriminant analysis classifier: a tutorial. Int J Appl Pattern Recogn 3(2):145–180

    Article  Google Scholar 

  29. Ramchoun H, Idrissi MAJ, Ghanou Y, Ettaouil M (2016) multilayer perceptron: architecture optimization and training. IJIMAI 4(1):26–30

    Article  Google Scholar 

  30. Kora P, Kalva SRK (2017) Detection of bundle branch block using adaptive bacterial foraging optimization and neural network. Egypt Inf J 18(1):67–74

    Google Scholar 

  31. Damodara VD, Arokiaraj A, Chen DH, Lou HH, Martin Christopher, Li Xianchang (2020) Flare performance modeling and set point determination using artificial neural networks. Int J Energy Environ Eng 11(1):91–109

    Article  Google Scholar 

  32. Nayak S, Kumar N, Choudhury BB (2017) Scaled conjugate gradient backpropagation algorithm for selection of industrial robots. Int J Comput Appl 7(6):2250–1797

    Google Scholar 

  33. Prasad N, Rajeshni S, Sunil PL (2013) Comparison of back propagation and resilient propagation algorithm for spam classification. In: 2013 Fifth international conference on computational intelligence, modelling and simulation, pp. 29–34. IEEE

  34. Govindarajan P, Soundarapandian R, Gandomi AH, Patan R, Jayaraman P, Manikandan R (2020) Classification of stroke disease using machine learning algorithms. Neural Comput Appl 32(3):817–828

    Article  Google Scholar 

  35. Imam MH, Karmakar CK, Jelinek HF, Palaniswami M, Khandoker AH (2016) Detecting subclinical diabetic cardiac autonomic neuropathy by analyzing ventricular repolarization dynamics. IEEE J Biomed Health Inf 20(1):64–72

    Article  Google Scholar 

  36. Chang P-C, Lin J-J, Hsieh J-C, Weng J (2012) Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl Soft Comput 12(10):3165–3175

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Instrumentation Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India

    Revathi Jothiramalingam

  2. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    Anitha Jude & Jude Hemanth Duraisamy

  3. Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India

    Rizwan Patan

  4. School of Computing, SASTRA Deemed University, Thanjavur, India

    Manikandan Ramachandran

  5. Faculty of Engineering and Information Systems, University of Technology Sydney, Ultimo, 2007, NSW, Australia

    Amir H. Gandomi

Authors
  1. Revathi Jothiramalingam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anitha Jude
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rizwan Patan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manikandan Ramachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jude Hemanth Duraisamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amir H. Gandomi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir H. Gandomi.

Ethics declarations

Conflict of interest

This manuscript is not submitted in any journal and none of the co-authors having any conflict to submit and process our manuscript in this Journal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jothiramalingam, R., Jude, A., Patan, R. et al. Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal. Neural Comput & Applic 33, 4445–4455 (2021). https://doi.org/10.1007/s00521-020-05238-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05238-2

Keywords

Search

Navigation