Multimedia Tools and Applications

, Volume 78, Issue 24, pp 35351–35372 | Cite as

GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification

  • K. MuthuvelEmail author
  • S. Anto
  • T. Jerry Alexander


The main objective of the proposed methodology is to classify an ECG signal as normal or abnormal using the optimal neuro-fuzzy classifier. The proposed work consists of two phases namely, feature extraction and neuro-fuzzy classifier based classification. The beat signals are initially taken from the physio-bank ATM. Then, three types of features are extracted from each signal namely, Morphological-based features, Haar wavelet-based features, and Tri-spectrum based features. After feature extraction, the optimal neuro-fuzzy classifier is classifying the beat signal as normal or abnormal. Here, Artificial Bee Colony (ABC) algorithm is combined with Genetic Algorithm (GA) for training the neuro-fuzzy classifier. For experimental evaluation, the MIT-BIH Arrhythmia Database is utilized and the performances are analyzed in terms of accuracy, sensitivity, and specificity. The experimental results clearly demonstrated that the proposed technique outperformed by having better accuracy of 93% when compared existing technique achieved 84% only.


ECG Neuro-fuzzy classifier Artificial bee colony 



  1. 1.
    Aramendi E, Irusta U, Pastor E, Bodegas A, Benito F (2010) ECG spectral and morphological parameters reviewed and updated to detect adult and paediatric life-threatening arrhythmia. Physiol Meas 31(6):749CrossRefGoogle Scholar
  2. 2.
    Bianchi D, Michele PD, Marchetti C, Tirozzi B, Cuomo S, Marie H, Migliore M (2014) Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit. Hippocampus 24(2):165–177CrossRefGoogle Scholar
  3. 3.
    Carnevale L, Celesti A, Fazio M, Bramanti P, Villari M (2017) Heart disorder detection with menard algorithm on apache spark, in: European Conference on Service-Oriented and Cloud Computing, Springer, pp. 229–237Google Scholar
  4. 4.
    Chen S, Hua W, Li Z, Li J, Gao X (2017) Heartbeat classification using projected and dynamic features of ECG signal. Biomed Signal Process Control 31:165–173. CrossRefGoogle Scholar
  5. 5.
    Cuomo S, De Pietro G, Farina R, Galletti A, Sannino G (2016) A revised scheme for real time ecg signal denoising based on recursive filtering. Biomed Signal Process Control 27:134–144CrossRefGoogle Scholar
  6. 6.
    Das M, Ari S (2014) ECG beats classification using mixture of features. Int Sch Res Not: 1–12, 2014Google Scholar
  7. 7.
    Dong X, Wang C, Si W (2017) ECG beat classification via deterministic learning. Neurocomputing 240:1–12CrossRefGoogle Scholar
  8. 8.
    Fang R, Pouyanfar S, Yang Y, Chen S-C, Iyengar S (2016) Computational health informatics in the big data age: A survey. ACM Comput Surv 49:1) 12CrossRefGoogle Scholar
  9. 9.
    Feng N, Xu S, Liang Y, Liu K (2019) A Probabilistic Process Neural Network and Its Application in ECG Classification. IEEE Access 7:50431–50439CrossRefGoogle Scholar
  10. 10.
    Géron A (2017) Hands-on Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. OReilly Media, SebastopolGoogle Scholar
  11. 11.
    Hanbay K (2019) Deep neural network based approach for ECG classification using hybrid differential features and active learning. IET Signal Processing 13(2):165–175CrossRefGoogle Scholar
  12. 12.
    Huang H, Liu J, Zhu Q, Wang R, Hu G (2014) A new hierarchical method for interpatient heartbeat classification using random projections and RR intervals. Biomed Eng Online 13:1–26. CrossRefGoogle Scholar
  13. 13.
    Isafiade OE, Isafiade OE, Bagula AB (2016) Data Mining Trends and Applications in Criminal Science and Investigations, first edn. IGI Global, Hershey, pp 1–386Google Scholar
  14. 14.
    Kanaujia M, Srivastava G (2015) ECG signal decomposition using PCA and ICA. In: National Conference on Recent Advances in Electronics & Computer Engineering (RAECE)Google Scholar
  15. 15.
    Lee J, Kwak K (2019) Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals. IEEE Access 7:48392–48404CrossRefGoogle Scholar
  16. 16.
    Liamedo M, Martinez J (2012) P.: ‘An automatic patient-adapted ECG heartbeat classifier allowing expert assistance. IEEE Trans Biomed Eng 59(8):2312–2320CrossRefGoogle Scholar
  17. 17.
    Llamedo M, Martínez JP (2011) Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng 58:616–625. CrossRefGoogle Scholar
  18. 18.
    Mar T, Zaunseder S, Martnez JP, Llamedo M, Poll R (2011) Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 58:2168–2177. CrossRefGoogle Scholar
  19. 19.
    Martis RJ, Acharya UR, Mandana KM, Ray AK, Chakraborty C (2012) Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 39:11792–11800CrossRefGoogle Scholar
  20. 20.
    Melillo P, Castaldo R, Sannino G, Orrico A, De Pietro G, Pecchia L (2015) Wearable technology and ECG processing for fall risk assessment, prevention and detection. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 7740–7743Google Scholar
  21. 21.
    Sannino G, De Pietro G (2011) An evolved ehealth monitoring system for a nuclear medicine department, In: Developments in E-systems Engineering (DeSE), IEEE, pp. 3–6Google Scholar
  22. 22.
    Sannino G, De Pietro G (2018) A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Futur Gener Comput Syst 86:446–455CrossRefGoogle Scholar
  23. 23.
    Sarfraz M, Khan A, Li F (2014) Using independent component analysis to obtain feature space for reliable ECG Arrhythmia classification. In: IEEE international conference on bioinformatics and biomedicine (BIBM)Google Scholar
  24. 24.
    Shi HT, Wang HR, Huang YX, Zhang YF, Liu CL (2017) A Mobile Intelligent ECG Monitoring System Based on IOS. International Conference on Sensing, Diagnostics, Prognostics, and Control:149–153Google Scholar
  25. 25.
    Shi H, Wang H, Huang Y, Zhao L, Qin C, Liu C (2019) A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Comput Methods Prog Biomed 171:1–10CrossRefGoogle Scholar
  26. 26.
    Silva I, Moody GB (2014) An open-source toolbox for analysing and processing physionet databases in matlab and octave. J Open Res Softw 2:1) e27CrossRefGoogle Scholar
  27. 27.
    J. Sun, C.K. Reddy (2013) Big data analytics for healthcare. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 1525–1525Google Scholar
  28. 28.
    Tripathy R, Bhattacharyya A, Pachori R (2019) A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes. IEEE Sensors J 19(12):4509–4517CrossRefGoogle Scholar
  29. 29.
    Ye C, Kumar BV, Coimbra MT (2012) Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng 59(10):2930–2941CrossRefGoogle Scholar
  30. 30.
    Zhu W, Chen X, Wang Y, Wang L (2019) Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 16(1):131–138CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringNoorul Islam UniversityKumaracoilIndia
  2. 2.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  3. 3.Department of Electronics and Communication EngineeringSathyabama UniversityChennaiIndia

Personalised recommendations