Advertisement

Medical & Biological Engineering & Computing

, Volume 56, Issue 10, pp 1887–1898 | Cite as

Classification of ECG beats using deep belief network and active learning

  • Sayantan G.
  • Kien P. T.
  • Kadambari K. V.
Original Article
  • 298 Downloads

Abstract

A new semi-supervised approach based on deep learning and active learning for classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed work is to model a scientific method for classification of cardiac irregularities using electrocardiogram beats. The model follows the Association for the Advancement of medical instrumentation (AAMI) standards and consists of three phases. In phase I, feature representation of ECG is learnt using Gaussian-Bernoulli deep belief network followed by a linear support vector machine (SVM) training in the consecutive phase. It yields three deep models which are based on AAMI-defined classes, namely N, V, S, and F. In the last phase, a query generator is introduced to interact with the expert to label few beats to improve accuracy and sensitivity. The proposed approach depicts significant improvement in accuracy with minimal queries posed to the expert and fast online training as tested on the MIT-BIH Arrhythmia Database and the MIT-BIH Supra-ventricular Arrhythmia Database (SVDB). With 100 queries labeled by the expert in phase III, the method achieves an accuracy of 99.5% in “S” versus all classifications (SVEB) and 99.4% accuracy in “V ” versus all classifications (VEB) on MIT-BIH Arrhythmia Database. In a similar manner, it is attributed that an accuracy of 97.5% for SVEB and 98.6% for VEB on SVDB database is achieved respectively.

Graphical Abstract

Reply- Deep belief network augmented by active learning for efficient prediction of arrhythmia.

Keywords

ECG Classification Linear support vector machine Active learning Gaussian-Bernoulli deep belief network 

References

  1. 1.
    Acharya R, Kumar A, Bhat P et al (2004) Classification of cardiac abnormalities using heart rate signals. Med Biol Eng Comput 42:288–293CrossRefGoogle Scholar
  2. 2.
    Ahn CW, Ramakrishna RS (2003) Elitism-based compact genetic algorithms. IEEE Trans Evol Comput 7:367–385CrossRefGoogle Scholar
  3. 3.
    Al Rahhal MM, Bazi Y, AlHichri H, Alajlan N, Melgani G, Yager RR (2016) Deep learning approach for active classification of electrocardiogram signals. Inform Sci 345:340–354CrossRefGoogle Scholar
  4. 4.
    Alonso-Atienza F, Morgado E, Fernandez-Martinez L, Garcia-Alberola A, Rojo-Alvarez JL (2014) Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 61:832–840CrossRefGoogle Scholar
  5. 5.
    Alvarado AS, Lakshminarayan C, Principe JC (2012) Time-based compression and classification of heartbeats. IEEE Trans Biomed Eng 59:1641–1648CrossRefGoogle Scholar
  6. 6.
    Bono V, Mazomenos EB, Chen T, Rosengarten JA, Acharyya A, Maharatna K et al (2015) Development of an automated updated Selvester QRS scoring system using SWT-based QRS fractionation detection and classification. IEEE J Biomed Health Inf 18:193–204CrossRefGoogle Scholar
  7. 7.
    Castro RM, Nowak RD (2008) Minimax bounds for active learning. IEEE Trans Inf Theory 54:2339–2353CrossRefGoogle Scholar
  8. 8.
    Chang PC, Lin JJ, Hsieh JC, Weng J (2012) Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl Soft Comput 12:3165–3175CrossRefGoogle Scholar
  9. 9.
    De Chazal P, O’Dwyer M, Reilly RB (2004) Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 51:1196–1206CrossRefGoogle Scholar
  10. 10.
    De Chazal P, Reilly RB (2006) A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 53:2535–2543CrossRefGoogle Scholar
  11. 11.
    Dima S M, Panagiotou C, Mazomenos EB, Rosengarten JA, Maharatna K, Gialelis JV et al (2013) On the detection of myocadial scar based on ECG/VCG analysis. IEEE Trans Biomed Eng 60:3399–3409CrossRefGoogle Scholar
  12. 12.
    Gosselin PH, Cord M (2008) Active learning methods for interactive image retrieval. IEEE Trans Image Process 17:1200–1211CrossRefGoogle Scholar
  13. 13.
    Huanhuan M, Yue Z (2014) Classification of electrocardiogram signals with deep belief networks. In: Proceedings of the 2014 IEEE seventeenth international conference on computer science engineering CSE, pp 7–12Google Scholar
  14. 14.
    Homaeinezhad MR, Atyabi SA, Tavakkoli E, Toosi HN, Ghaffari A, Ebrahimpour R (2012) ECG arrhythmia recognition via a neuro-SVM–KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Syst Appl 39:2047–2058CrossRefGoogle Scholar
  15. 15.
    Hu YH, Palreddy S, Tompkins WJ (1997) A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans Biomed Eng 44:891–900CrossRefGoogle Scholar
  16. 16.
    Ince T, Kiranyaz S, Gabbouj M (2009) A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng 56:1415–1426CrossRefGoogle Scholar
  17. 17.
    Javadi M, Arani SAAA, Sajedin A, Ebrahimpour R (2013) Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed Signal Process Control 8:289–296CrossRefGoogle Scholar
  18. 18.
    Jiang W, Kong SG (2007) Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Netw 18:1750–1761CrossRefGoogle Scholar
  19. 19.
    Kutlu Y, Kuntalp D (2012) Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput Methods Progr Biomed 105:257–267CrossRefGoogle Scholar
  20. 20.
    Langkvist M, Karlsson L, Loutfi A (2012) Sleep stage classification using unsupervised feature learning. Adv Artif Neural Syst, e107046Google Scholar
  21. 21.
    Luo T, Kramer K, Goldgof DB (2005) Active learning to recognize multiple types of plankton. J Mach Learn Res 6:589–613Google Scholar
  22. 22.
    Maršánová L, Ronzhina M, Smíšek R, Vítek M Němcová A, Smital L, Nováková M (2017) ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: a comprehensive experimental study.  https://doi.org/10.1038/s41598-017-10942-6
  23. 23.
    Martis RJ, Acharya UR, Min LC (2013) ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Process Control 8:437–448CrossRefGoogle Scholar
  24. 24.
    Melgani F, Bazi Y (2008) Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans Inf Technol Biomed 12:667–677CrossRefGoogle Scholar
  25. 25.
    Ning X, Selesnick IW (2013) ECG enhancement and QRS detection based on sparse derivatives. Biomed Signal Process Control 8:713–723CrossRefGoogle Scholar
  26. 26.
    Pasolli E, Melgani F, Bazi Y (2011) Support vector machine active learning through significance space construction. IEEE Geosci Remote Sens Lett 8:431–435CrossRefGoogle Scholar
  27. 27.
    Phukpattaranont P (2015) QRS detection algorithm based on the quadratic filter. Expert Syst Appl 42:867–877CrossRefGoogle Scholar
  28. 28.
    Sameni R, Shamsollahi MB, Jutten C, Clifford GD (2007) A nonlinear Bayesian filtering framework for ECG Denoising. IEEE Trans Biomed Eng 54:2172–2185CrossRefGoogle Scholar
  29. 29.
    Schwartzman A, Wolf T, Gepstein L, Hayam G, Lessick J, Reisfeld D, Schwartz Y, Uretzky G, Ben-Haim S (2001) Characterisation of acute myocardial ischaemia in a canine model based on principal component analysis of unipolar endocardial electrograms. Med Biol Eng Comput 39:571–578CrossRefGoogle Scholar
  30. 30.
    Thaler MS (1999) The only EKG book you’ll ever need, 3rd edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  31. 31.
    Tracey BH, Miller EL (2012) Nonlocal means denoising of ECG signals. IEEE Trans Biomed Eng 59:2383–2386CrossRefGoogle Scholar
  32. 32.
    Wang J, Ye Y, Pan X, Gao X (2015) Parallel-type fractional zero-phase filtering for ECG signal denoising. Biomed Signal Process Control 18:36–41CrossRefGoogle Scholar
  33. 33.
    Yadav SK, Sinha R, Bora PK (2015) Electrocardiogram signal denoising using non-local wavelet transform domain filtering. IET Signal Process 9:88–96CrossRefGoogle Scholar
  34. 34.
    Yang H, Kan C, Liu G, Chen Y (2013) Spatiotemporal differentiation of myocardial infarctions. IEEE Trans Autom Sci Eng 10:938–947CrossRefGoogle Scholar
  35. 35.
    Ye C, Kumar BVKV, Coimbra MT (2012) Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng 59:2930–2941CrossRefGoogle Scholar
  36. 36.
    Yu SN, Chou KT (2008) Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst Appl 34:2841–2846CrossRefGoogle Scholar
  37. 37.
    Zhang Z, Dong J, Luo X, Choi KS, Wu X (2014) Heartbeat classification using disease-specific feature selection. Comput Biol Med 46:79–89CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Department of Computer Science EngineeringNational Institute of Technology WarangalHanamkondaIndia

Personalised recommendations