Skip to main content
SpringerLink
Log in

Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Cardiac diseases are one of the foremost reasons of mortality. Hence, the early detection of cardiac diseases based on electrocardiogram (ECG) is important for delivering appropriate and timely treatment to the heart patients and it is increasing the heart patient’s survival. Recent trends in clinical decision making systems appeal automation in ECG signal processing and beat classification. Automatic beat classification is a significant method to support clinical specialists to categorize arrhythmia signals in ECG recording. The main objective of this paper is to construct novel automatic classification system for analysis of ECG signal and decision making purposes. The proposed method involves three main parts: De-noising, feature extraction and classification. Initially, discrete wavelet transform (DWT) is applied before classification for signal De-noising and feature extraction. In this work, neighborhood rough set is applied to classify the ECG signals into normal and four abnormal heart beats. The presence of neighborhood rough set classification algorithm (NRSC) produces very exciting recognition and classification abilities through a wide range of biomedical signal processing. The experimental analysis of the proposed NRSC algorithm is compared with the multi-layered perceptron, decision table, Naïve Bayes and J48 classification algorithms. Here, the performance of classification algorithms has been evaluated in terms of sensitivity, specificity, Positive predictive value, negative predictive value, false predictive value, Matthews’s correlation coefficients, F-measure, Folke–Mallows Index and Kulcznski Index. The acquired results showed that the proposed algorithm attained 99.32 % of the classification accuracy using NRSC and DWT. Results indicated that the performance of this proposed NRSC classification method was remarkably superior to that of other classification techniques.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Acampora G, Lee CS, Vitiello A, Wang MH (2012) Evaluating cardiac health through semantic soft computing techniques. Soft Comput 16(7):1165–1181

    Article  Google Scholar 

  • Arif M, Akram MU, Afsar FA (2009) Arrhythmia beat classification using pruned fuzzy k-nearest neighbor classifier soft computing and pattern recognition, SOCPAR ’09. In: International conference, pp 37–42

  • Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24(5):1163–1177

    Article  Google Scholar 

  • Benali R, Reguig FB, Slimane ZH (2012) Automatic classification of heartbeats using wavelet neural network. J Med Syst 36(2):883–892

    Article  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  • Daubechies I (1990) The wavelet transform, time–frequency localization and signal analysis. IEEE Trans Inf Theory 36(5):961–1005

    Article  MathSciNet  MATH  Google Scholar 

  • Desgraupes B (2013) Clustering indices. University of Paris Ouest-Lab Modal’X, pp 1–34

  • Dingyin H, Wei L, Xi C (2011) Feature extraction of motor imagery EEG signals based on wavelet packet decomposition. In: Proceedings of the 2011 IEEE international conference on complex medical engineering, pp 694–697

  • Greco S, Matarazzo B, Słowin’ski B (1999) Rough approximation of a preference relation by dominance relations. Eur J Oper Res 117(1):63–83

    Article  MATH  Google Scholar 

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11:10–18

    Article  Google Scholar 

  • Hari MR, Anuragm T, Shailja S (2013) ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier. Measurement 46(9):3238–3246

    Article  Google Scholar 

  • 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. Int J Expert Syst Appl 39(2):2047–2058

    Article  Google Scholar 

  • Hu Q, Yu D, Xie Z, Liu J (2006) Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans Fuzzy Syst 14(2):191–201

    Article  Google Scholar 

  • Hu Q, Yu D, Liu J, Wu C (2008a) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594

  • Hu Q, Yu D, Xie Z (2008b) Neighborhood classifiers. Expert Syst Appl 34(2):866–876

  • Inan OT, Giovangrandi T, Kovacs GTA (2006) Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans Biomed Eng 53(12):2507–2515

    Article  Google Scholar 

  • Inbarani HH, Banu PKN, Azar AT (2014) Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 25(3–4):793–806

    Article  Google Scholar 

  • Karpagachelvi S, Arthanari M, Sivakumar M (2012) Classification of electrocardiogram signals with support vector machines and extreme learning machine. Neural Comput Appl 21(6):1331–1339

    Article  Google Scholar 

  • Kumar SU, Inbarani HH (2015a) Classification of ECG cardiac arrhythmias using bijective soft set. In: Hassanien AE, Azar AT, Snasael V, Kacprzyk J, Abawajy JH (eds) Big data in complex systems. Springer International Publishing, pp 323–350

  • Kumar SU, Inbarani HH (2015b) A novel neighborhood rough set based classification approach for medical diagnosis. Procedia Comput Sci 47:351–359

  • Kumar SU, Inbarani HH, Kumar SS (2013) Bijective soft set based classification of medical data. International conference on pattern recognition, informatics and medical engineering (PRIME), pp 517–521

  • Kumar SU, Inbarani HH, Azar AT, Hassanien AE (2014) Identification of heart valve disease using bijective soft sets theory. Int J Rough Sets Data Anal 1(2):1–14

    Article  Google Scholar 

  • Kutlu Y, Kuntalp D (2011) A multi-stage automatic arrhythmia recognition and classification system. Comput Biol Med 41(1):37–45

    Article  Google Scholar 

  • Maharaj EA, Alonso AM (2013) Discriminant analysis of multivariate time series: application to diagnosis based on ECG signals. Comput Stat Data Anal 70(2013):67–87

    MathSciNet  Google Scholar 

  • Mathews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Struct 405(2):442–451

    Article  Google Scholar 

  • Minami K, Nakajima H, Toyoshima T (2011) Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans Biomed Eng 46:179–185

    Article  Google Scholar 

  • Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50

    Article  Google Scholar 

  • Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036

    Article  Google Scholar 

  • Osowski S, Linh TH (2001) ECG beat recognition using fuzzy hybrid neural network. IEEE Trans Biomed Eng 48(11):1265–1271

    Article  Google Scholar 

  • Özbay Y (2009) A new approach to detection of ECG arrhythmias: complex discrete wavelet transform based complex valued artificial neural network. J Med Syst 33(6):435–445

    Article  Google Scholar 

  • Pan J, Tompkins W (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32(3):230–236

    Article  Google Scholar 

  • Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  MATH  Google Scholar 

  • Rioul O, Vetterli M (1991) Wavelets and signal processing. IEEE Signal Process Mag 8(4):14–38

    Article  Google Scholar 

  • Shi SP, Qiu J, Sun XY, Suo SB, Huang SY, Liang RP (2012) PMeS: prediction of methylation sites based on enhanced feature encoding scheme. PLoS One 7(6):1–11

    Google Scholar 

  • Skowron A, Stepaniuk J (1996) Tolerance approximation spaces. Fundamenta Informaticae 27(2–3):245–253

    MathSciNet  MATH  Google Scholar 

  • Slezak D, Ziarko W (2005) The investigation of the Bayesian rough set model. Int J Approx Reason 40(1–2):81–91

    Article  MathSciNet  MATH  Google Scholar 

  • Slowinski R, Vanderpooten D (2000) A generalized definition of rough approximations based on similarity. IEEE Trans Knowl Data Eng 12(2):331–336

    Article  Google Scholar 

  • Sumathi S, Beaulah HL, Vanithamani R (2014) A wavelet transform based feature extraction and classification of cardiac disorder. J Med Syst 38(9):1–9

    Article  Google Scholar 

  • Yao Y (2005) Probabilistic rough set approximations. Int J Approx Reason 49(2):255–271

    Article  MATH  Google Scholar 

  • Yao Y, Yao B (2012) Covering based rough set approximations. Inf Sci 200(1):91–107

    Article  MathSciNet  MATH  Google Scholar 

  • Yao Y, Zhao Y (2008) Attribute reduction in decision-theoretic rough set models. Inf Sci 178(17):3356–3373

    Article  MathSciNet  MATH  Google Scholar 

  • Yong L, Wenliang H, Yunliang J, Zhiyong Z (2014) Quick attribute reduct algorithm for neighborhood rough set model. Inf Sci 271(1):65–81

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

The first author immensely acknowledges the partial financial assistance under University Research Fellowship, Periyar University, Salem. The Second author would like to thank UGC, New Delhi for the financial support received under UGC Major Research Project No. F-41-650/2012 (SR).

Author information

Authors and Affiliations

  1. Department of computer science, Periyar University, Salem, TN, India

    S. Udhaya Kumar & H. Hannah Inbarani

Authors
  1. S. Udhaya Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Hannah Inbarani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Udhaya Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S.U., Inbarani, H.H. Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases. Soft Comput 21, 4721–4733 (2017). https://doi.org/10.1007/s00500-016-2080-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2080-7

Keywords

Search

Navigation