Skip to main content
SpringerLink
Log in

Label consistent non-negative representation of ECG signals for automated recognition of cardiac arrhythmias

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. This paper aims to propose a novel robust ECG biometric method, named the Label Consistent Non-negative Representation (LCNR), for ECG classification. We propose an objective function consists of the reconstruction error, classification error and discriminative sparse-code error with the non-negative regularization term on the coding coefficients. The coding vector was restricted to be non-negative using a non-negative constrained least squares model, and a blockwise coordinate descent algorithm was used to simultaneously learn a compact discriminative dictionary and a multiclass linear classifier. The experiments are carried out for the proposed methods using benchmark MIT-BIH data and evaluated under standard scheme and category-based scheme. The evaluation and experimental results show that our proposed LCNR algorithm achieves state-of-the-art performance, specifically surpassing the label consistent KSVD algorithm in terms of classification accuracy. By means of the dictionary learning algorithm, we can improve the efficiency for a large-size training database with a significantly faster execution time (more than 5 times) than NRC.

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. Acharya UR, Oh SL, Hagiwara Y (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396

    Article  Google Scholar 

  2. Boyd S, Parikh N, Chu E, Peleato B (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122

    Article  Google Scholar 

  3. Cui G, Li X, Dong Y (2018) Subspace clustering guided convex nonnegative matrix factorization. Neurocomputing 292:38–48

    Article  Google Scholar 

  4. Desai U, Martis RJ, Nayak CG, Sarika K (2015) Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. In: 12 IEEE Int C Elect Energy Env Communications Computer Control

    Google Scholar 

  5. Elhaj FA, Salim N, Harris AR, Swee TT (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Meth Prog Bio 127:52–63

    Article  Google Scholar 

  6. Hua J, Zhang H, Liu J, Xu Y (2018) Direct arrhythmia classification from compressive ECG signals in wearable health monitoring system. J Circuit Syst Comp 27(6):1850088

    Article  Google Scholar 

  7. Hua J, Xu Y, Tang J, Liu J (2020) ECG heartbeat classification in compressive domain for wearable devices. J Syst Architect 104:101687

    Article  Google Scholar 

  8. Huang HF, Hu GS, Zhu L (2012) Sparse representation-based heartbeat classification using independent component analysis. J Med Syst 36(3):1235–1247

    Article  Google Scholar 

  9. Jiang ZL, Lin Z, Davis LS (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE T Pattern Anal 35(11):2651–2664

    Article  Google Scholar 

  10. Lee D, Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Article  Google Scholar 

  11. Li N, Si Y, Deng D, Yuan C (2017) ECG beats classification via online sparse dictionary and time pyramid matching. In: IEEE 17th international conference on communication technology (ICCT), pp 1537–1543

    Google Scholar 

  12. Li R, Yang GP, Wang KK, Huang YW (2020) Robust ECG biometrics using GNMF and sparse representation. Pattern Recogn Lett 129:70–76

    Article  Google Scholar 

  13. Liu HW, Li XL, Zheng XY (2013) Solving non-negative matrix factorization by alternating least squares with a modified strategy. Data Min Knowl Disc 26(3):435–451

    Article  MathSciNet  Google Scholar 

  14. Mar T, Zaunseder S, Pablo Martinez J (2011) Optimization of ECG classification by means of feature selection. IEEE T Bio-Med Eng 58(8):2168–2177

    Article  Google Scholar 

  15. Marinho LB, Nascimento NDMM, Souza JWM, Gurgel MV (2019) A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Gener Comp Sy 97:564–577

    Article  Google Scholar 

  16. Martis RJ, Chakraborty C, Ray AK (2010) An integrated ECG feature extraction scheme using PCA and wavelet transform2009. In: Annual IEEE India conference, p 422

    Google Scholar 

  17. Martis RJ, Acharya UR, Mandana KM, Ray AK (2012) Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 39(14):11792–11800

    Article  Google Scholar 

  18. Martis RJ, Acharya UR, Min LC (2013) ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Proces 8(5):437–448

    Article  Google Scholar 

  19. Martis RJ, Acharya UR, Adeli H (2014) Current methods in electrocardiogram characterization. Comput Biol Med 48:133–149

    Article  Google Scholar 

  20. Mathews SM, Polania LF, Barner KE (2015) Leveraging a discriminative dictionary learning algorithm for single-lead ECG classification 41st Annual Northeast Biomedical Engineering Conference, pp 1–2

  21. Mondéjar-Guerra V, Novo J, Rouco J, Penedo MG (2019) Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed Signal Proces 47:41–48

    Article  Google Scholar 

  22. Plawiak P (2018) Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 92:334–349

    Article  Google Scholar 

  23. Raj S, Ray KC (2018) Sparse representation of ECG signals for automated recognition of cardiac arrhythmias. Expert Syst Appl 49-64(105):49–64

    Article  Google Scholar 

  24. Raj S, Ray KC, Shankar O (2016) Cardiac arrhythmia beat classification using DOST and PSO tuned SVM. Comput Meth Prog Bio 136:163–177

    Article  Google Scholar 

  25. Romdhane TF, Alhichri H, Ouni R, Atri M (2020) Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss - ScienceDirect. Comput Biol Med 123:103866

    Article  Google Scholar 

  26. Rubinstein R, Bruckstein AM, Elad M (2010) Dictionaries for sparse representation modeling. P IEEE 98(6):1045–1057

    Article  Google Scholar 

  27. Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process 16(3):275–287

    Article  Google Scholar 

  28. Standard AE (1998) Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms, ANSI/AAMI EC57:1998 standard. Association for the Advancement of Medical Instrumentation

    Google Scholar 

  29. Wan M, Lai Z, Yang G, Yang Z (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120–131

    Article  MathSciNet  Google Scholar 

  30. Wan M, Lai Z, Ming Z, Yang G (2019) An improve face representation and recognition method based on graph regularized non-negative matrix factorization. Multimed Tools Appl 78(15):22109–22126

    Article  Google Scholar 

  31. Wan M, Chen X, Zhan T, Xu C (2021) Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction. Inf Sci 563:1–15

    Article  MathSciNet  Google Scholar 

  32. Wright J, Yang AY, Ganesh A, Sastry SS (2009) Robust face recognition via sparse representation. IEEE T Pattern Anal 31(2):210–227

    Article  Google Scholar 

  33. Xu J, An WP, Zhang L, Zhang D (2019) Sparse, collaborative, or nonnegative representation: which helps pattern classification? Pattern Recogn 88:679–688

    Article  Google Scholar 

  34. Xu JX, Yang GP, Wang KK, Huang YW (2020) Structural sparse representation with class-specific dictionary for ECG biometric recognition. Pattern Recogn Lett 135:44–49

    Article  Google Scholar 

  35. Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. IEEE International Conference on Image Processing 2010:1601–1604

  36. Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vis 109(3):209–232

    Article  MathSciNet  Google Scholar 

  37. Zhang L, Yang M, Feng XC (2012) Sparse representation or collaborative representation: which helps face recognition? In: International Conference on Computer Vision, pp 471–478

    Google Scholar 

  38. Zhao W, Hu J, Jia D (2019) Deep learning based patient-specific classification of arrhythmia on ECG signal in 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 1500–1503

  39. Zhou JH, Zhang B (2019) Collaborative representation using non-negative samples for image classification. Sensors-Basel 19(11):2609

    Article  Google Scholar 

  40. Zhu W, Chen X, Wang Y, Wang L (2019) Arrhythmia recognition and classification using ECG morphology and segment feature analysis. IEEE Acm T Comput Bi 16(1):131–138

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant no. 61863027), the Key Research and Development Plan of Jiangxi Province (Grant no. 20202BBGL73057), the Natural Science Foundation of Jiangxi Province (Grant no. 20171BAB201013) and the Project of Nanchang Key Laboratory of Medical and Technology Research (Grant no. 2018-NCZDSY-002).

Author information

Authors and Affiliations

  1. Nanchang key Laboratory of Medical and Technology Research, School of Mechatronic Engineering, Nanchang University, Nanchang, 330031, China

    Bing Zhang & Jizhong Liu

  2. School of Information Engineering, Nanchang University, Nanchang, 330031, China

    Jianhua Wu

Authors
  1. Bing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jizhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianhua Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jizhong Liu or Jianhua Wu.

Ethics declarations

Conflict of interests

The authors declare that there are no conflicts of interest regarding the publication of this paper.

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

Zhang, B., Liu, J. & Wu, J. Label consistent non-negative representation of ECG signals for automated recognition of cardiac arrhythmias. Multimed Tools Appl 81, 16047–16065 (2022). https://doi.org/10.1007/s11042-022-12614-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12614-8

Keywords

Search

Navigation