Linking the Researchers, Developing the Innovations Manuscripts submittal opens till 25 January 2024. Please submit your papers at editor@kwpublisher.com or editorkwpublisher@gmail.com

  • Volume 2018

    A Qualitative Overview of Fuzzy Logic in ECG Arrhythmia Classification
    (International Journal of Engineering Works)

    Vol. 5, Issue 11, PP. 232-239, November 2018
    DOI
    Keywords: Arrhythmias, Electrocardiogram, Fuzzy logic, Fuzzy Classifier, Fuzzy Inference System

    Download PDF

    Abstract

    Achieving elevated efficiency for the classification of the ECG signal is a noteworthy issue in the present world. Electrocardiogram (ECG) is a technique to identify heart diseases. However, the detection of the actual type of heart diseases is indispensable for further treatment. Various techniques have been invented and explored to categorize the heart diseases which are recognized as arrhythmias. This paper aims to investigate the development of various techniques of arrhythmia classification on the basis of fuzzy logic along with an elaborative discussion on accepted techniques. Moreover, a comparative study on their efficiency has been analyzed to emphasize the scope of novel research areas. 

    Author

    1. Ahmed Farhan: College of Information and Communication Engineering, Harbin Engineering University, China
    2. Chen Li Wei: College of Information and Communication Engineering, Harbin Engineering University, China
    3. Md Toukir Ahmed: College of Information and Communication Engineering, Harbin Engineering University, China

    Full Text

    Cite

    Ahmed Farhan Chen Li Wei and Md Toukir Ahmed, "A Qualitative Overview of Fuzzy Logic in ECG Arrhythmia Classification", International Journal of Engineering Works, Vol. 5 Issue 11 PP. 232-239 November 2018.

    References

    1. [1]     Channappa Bhyri, Satish T. Hamde, Laxman M. Waghmare, “ECG Acquisition and Analysis System for Diagnosis of Heart Diseases”, Sensors & Transducers Journal, Vol. 133, Issue 10, October 2011, pp. 18-29.
    2. [2]     B. Anuradha and V.C. Veera Reddy, “Cardiac arrhythmia classification using fuzzy classifiers”, Journal of Theoretical and Applied Information Technology, 2008, pp. 353-359.
    3. [3]     Introductory Guide to Identifying ECG Irregularities, DailyCareBioMedical Inc.
    4. [4]     Miad Faezipour, Adnan Saeed, Suma Chandrika Bulusu, Mehrdad Nourani, Hlaing Minn & Lakshman Tamil, “A Patient-Adaptive Profiling Scheme for ECG Beat Classification,” IEEE Transactions On Information Technology In Biomedicine, Vol. 14, No. 5, September 2010, pp. 1153-1165.
    5. [5]     Ludmila I. Kuncheva (2008), Scholarpedia, 3(1):2925.
    6. [6]     Ryan J. Urbanowicz and Jason H. Moore, “Learning Classifier Systems: A Complete Introduction, Review, and Roadmap”, Journal of Artificial Evolution and Applications, Volume 2009, Article ID 736398, 25 pages.
    7. [7]     Rahime Ceylan, Yüksel Özbay, “Wavelet Neural Network for Classification of Bundle Branch Blocks”, Proceedings of the World Congress on Engineering 2011, Vol. II, WCE 2011, London, U.K., July 6 - 8, 2011, pp. 1003-1007.
    8. [8]     R. Acharya, J. S. Suri, J. A. E. Spaan and S. M. Krishnan, “Advances in Cardiac Signal Processing”, ISBN-13 978-3-540-36674-4, Springer Berlin Heidelberg New York, 2007, pp. 327-338.
    9. [9]     M. Owis, A. Abou-Zied, A. B. Youssef and Y. Kadah, “Robust feature extraction from ECG signals based on nonlinear dynamical modeling”, 23rd Annual International Conference IEEE Engineering in Medicine and Biology Society, Vol. 2, 2001, pp. 1585-1588.
    10. [10]  O. T. Inan, L. Giovangrandi and G. T. A. Kovacs, “Robust neural network- based classification of premature ventricular contractions using wavelet transform and timing interval features”, IEEE Transaction on Biomedical Engineering, Vol. 53, No. 12, 2006, pp. 2507-2515.
    11. [11]  A. R. Naghsh-Nilchi and A. R. K. Mohammadi, “Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network”, EURASIP Journal on Advances in Signal Processing, Vol. 2008, Article no. 202, 2008.
    12. [12]  T. Ince, S. Kiranyaz and M. Gabbouj, “A Generic and Robust System for Automated Patient-specific Classification of Electrocardiogram Signals”, IEEE Transactions on Biomedical Engineering, Vol. 56, No. 5, May 2009, pp- 1415-1426
    13. [13]  S. S. Mehta and N. S. Lingayat, “Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram”, IAENG, International Journal of Applied Mathematics, 2007, pp. 1630-1635.
    14. [14]  Jalal A. Nasiri, Mahmoud Naghibzadeh, H. Sadoghi Yazdi, Bahram Naghibzadeh, “ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm”, Third UKSim European Symposium on Computer Modeling and Simulation, 2009, pp. 187-192.
    15. [15]  Narendra Kohli, Nishchal K. Verma, Abhishek Roy, “SVM Based Methods for Arrhythmia Classification in ECG” International Conference on Computer & Communication Technology, 2010, pp. 486-490.
    16. [16]  MiHye Song, Jeon Lee, Sung Pil Cho, KyoungJoung Lee, and Sun Kook Yoo, “Support Vector Machine Based Arrhythmia Classification Using Reduced Features”, International Journal of Control, Automation, and Systems, vol. 3, no. 4, December 2005, pp. 571-579.
    17. [17]  B. M. Z. Asl and S. K. Setarehdan, “Neural Network Based Arrhythmia Classification Using Heart Rate Variability Signal”, Proceedings of the 2nd International Symposium on Biomedical Engineering, Bangkok, Thailand, November 2006, pp.149-162.
    18. [18]  B. Anuradha and V. C. V. Reddy, “ANN for classification of cardiac arrhythmias”, ARPN Journal of Engineering and Applied Sciences, Vol. 3, No. 3, June 2008.
    19. [19]  R. P. W. Duin and M. Loog, “Linear dimensionality reduction via a heteroscedastic extension of lda: the chernoff criterion”, IEEE Trans. PAMI, vol. 26, no. 6, June 2004, pp. 732–739.
    20. [20]  M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt and L. Sörnmo, “Clustering ECG Complexes Using Hermite Functions and Selforganizing Maps”, IEEE Transaction on Biomedical Engineering, vol. 47, no. 7, July 2000, pp. 838-848.
    21. [21]  Martin Lagerholm, Carsten Peterson, Guido Braccini, Lars Edenbrandt, and Leif Sörnmo, “Clustering ECG Complexes Using Hermite Functions and Self-Organizing Maps”, IEEE Transactions On Biomedical Engineering, Vol. 47, NO. 7, JULY 2000, pp. 838-848.
    22. [22]  P. de Chazal, M. O’Dwyer and R. B. Reilly, “Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features”, IEEE Transaction on Biomedical Engineering, Vol. 51, No. 7, July2004, pp. 1196- 1206.
    23. [23]  Iva Bogdanova, Francisco Rinc´onand David Atienza, “A Multi-lead ECG Classification Based On Random Projection Features”, IEEE, ICASSP 2012, pp. 625-628.
    24. [24]  D. Benitez, P. A. Gaydecki, A. Zaidib and A. P. Fitzpatrick, ‘The use of the Hilbert transform in ECG signal analysis’, Computers in Biology and Medicine, 2001, 31, pp. 399-406.
    25. [25]  J.C. Nunes, and A. Nait-Ali, ‘Hilbert transform-based ECG modeling’. Biomedical Engineering, 2005, Vol.39, No. 3, pp. 133-137.
    26. [26]  S. Karpagachelvi, Dr. M. Arthanari, M. Sivakumar, “Classification of ECG Signals Using Extreme Learning Machine”, Computer and Information Science Vol. 4, No. 1, January 2011, pp. 42-52.
    27. [27]  V. Mahesh, A. Kandaswamy, C. Vimal, B. Sathish, “ECG arrhythmia classification based on logistic model tree”, J. Biomedical Science and Engineering 2, 2009, pp. 405-411.
    28. [28]  Roshan Joy Martis, Chandan Chakraborty, Ajoy K. Ray, “A two-stage mechanism for registration and classification of ECG using Gaussian mixture model”, Pattern Recognition 42, 2009, pp. 2979 – 2988.
    29. [29]  Saniya Siraj Godil, Muhammad Shahzad Shamim, Syed Ather Enam, Uvais Qidwai, “Fuzzy logic: A ‘simple’ solution for complexities in neurosciences?” Surgical Neurology International 2011, Vol-2, Issue-1, page 24.
    30. [30]  Raj Kumar Bansal, Ashok Kumar Goel, Manoj Kumar Sharma, “MATLAB and Its Application in Engineering”, Pearson Publication, Fifth Impression, 2012.
    31. [31]  Wen Wei and Jerry M. Mendel, “A Fuzzy Logic Method for Modulation Classification in Nonideal Environments”, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 3, June 1999, pp. 333-344.
    32. [32]  Tomoharu Nakashima, Gerald Schaefer, Yasuyuki Yokota, Hisao Ishibuchi, “A weighted fuzzy classifier and its application to image processing tasks”, Fuzzy Sets and Systems 158, 2007, pp. 284 – 294.
    33. [33]  Reza Boostani, MojtabaRismanchib,Abbas Khosravani, Lida Rashidi, Samaneh Kouchaki, Payam Peymani, Seyed Taghi Heydari, B. Sabayan, K. B. Lankarani, “Presenting a hybrid method in order to predict the2009 pandemic influenza A (H1N1)”, Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.1, January 2012, pp. 31-43.
    34. [34]  Ken Nozaki, Hisao Ishibuchi and Hideo Tanaka, “Adaptive Fuzzy Rule-Based Classification Systems”, IEEE Transactions on Fuzzy Systems, Vol. 4, No. 3, 1996, pp. 238-250.
    35. [35]  Jia Zeng and Zhi-Qiang Liu, “Type-2 Fuzzy Sets for Pattern Recognition: The State-of-the-Art”, Journal of Uncertain Systems, Vol.1, No.3, 2007, pp.163-177.
    36. [36]  F. Hoffmann, B. Baesens, J. Martens, F. Put and J. Vanthienen,   "Comparing a genetic fuzzy and a Neuro-fuzzy classifier for credit scoring", presented at Int. J. Intell. Syst., 2002, pp.1067-1083.
    37. [37]  F. M. Schleif, T. Villmann, B. Hammer, “Prototype based Fuzzy Classification in Clinical Proteomics”, International Journal of Approximate Reasoning, 2008, 47(1), pp. 4-16.
    38. [38]  Aaron K. Shackelfordand Curt H. Davis, “A Hierarchical Fuzzy Classification Approach for High-Resolution Multispectral Data Over Urban Areas”, IEEE Transactions onGeo-science And Remote Sensing, Vol. 41, No. 9, SEPTEMBER 2003, pp. 1920-1932.
    39. [39]  Wai Kei Lei, Bing Nan LI, Ming Chui Dong, Mang I. Vai, “AFC-ECG: An Intelligent Fuzzy ECG Classifier”, A. Saad et al. (Eds.): Soft Computing in Industrial Applications, ASC 39, 2007, pp. 189–199.
    40. [40]  Yun-Chi Yeh, Wen-June Wang, and Che Wun Chiou, “Heartbeat Case Determination Using Fuzzy Logic Method on ECG Signals”, International Journal of Fuzzy Systems, Vol. 11, No. 4, December 2009, pp. 250-261.
    41. [41]  Mohammad Reza Homaeinezhad , Ehsan Tavakkoli, Ali Ghaffari, “Discrete Wavelet-based Fuzzy Network Architecture for ECG Rhythm-Type Recognition: Feature Extraction and Clustering-Oriented Tuning of Fuzzy Inference System”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 3, September, 2011, pp. 107-130.
    42. [42]  R. R. Gharieb, M. Massoud, S. Nady, M. Moness, “Fuzzy C-Means in Features Space of Teager-Kaiser Energy of Continuous Wavelet Coefficients for Detection of PVC Beats in ECG”, 8th Cairo International Biomedical Engineering Conference (CIBEC) (IEEE Conferences), 2016, pp. 72-75.
    43. [43]  Liang-Yu Shyu, Ying-Hsuan Wu, Weichih Hu, “Using Wavelet Transform and Fuzzy Neural Network for VPC Detection From the Holter ECG”, IEEE Transactions on Biomedical Engineering, Vol. 51, No. 7, July 2004, pp. 1269-1273.
    44. [44]  N. Özlem Özcan, Fikret Gurgen, “Fuzzy Support Vector Machines for ECG Arrhythmia Detection”, International Conference on Pattern Recognition, 2010, pp. 2973-2976.
    45. [45]  S. Murugan & Dr. S. Radhakrishnan, “Improving Ischemic Beat Classification Using Fuzzy-Genetic Based PCA and ICA”, International Journal on Computer Science and Engineering (IJCSE), Vol. 02, No. 05, 2010, pp. 1532-1538.
    46. [46]  Eduardo Ramírez, Oscar Castillo, and José Soria, “Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System”, P. Melin et al. (Eds.): Soft Comp. for Recogn. Based on Biometrics, SCI 312, 2010, pp. 37–55.
    47. [47]  Muhammad Arif, Muhammad Usman Akram, Fayyaz-ul-Afsar Amir Minhas, “Pruned fuzzy K-nearest neighbor classifier for beat classification”, J. Biomedical Science and Engineering, 2010, 3, pp-380-389.
    48. [48]  Glayol Nazari Golpayegani & Amir Homayoun Jafari, “A novel approach in ECG beat recognition using adaptive neural fuzzy filter”, J. Biomedical Science and Engineering, 2009, 2, pp. 80-85.
    49. [49]  T.M. Nazmy, H. El-Messiry, B.  Al-Bokhity, “Adaptive Neuro-Fuzzy Inference System for classification of ECG signals”, The 7th International Conference on Informatics and Systems (INFOS), Date of Conference: 28-30, March 2010, pp. 1-6.
    50. [50]  Prarthana B. Sakhare, Rajesh Ghongade, “An Approach for ECG Beats Classification using Adaptive Neuro Fuzzy Inference System”, Annual IEEE India Conference (INDICON), 2015, pp. 1-6
    51. [51]  A. Dallali, A. Kachouri and M. Samet, “Fuzzy C-Means Clustering, Neural Network, WT and HRV For Classification of Cardiac Arrhythmia”, ARPN Journal of Engineering and Applied Sciences, Vol. 6, No. 10, October 2011, pp. 112-118.
    52. [52]  R. B. Ghongade and A. A. Ghatol, “Optimization of a multi-class MLP ECG classifier using FCM”, Indian Journal of Science and Technology Vol. 3, No. 9, Sep 2010, pp. 1102-1105.
    53. [53]  Rahime Ceylan, Yuksel Ozbay, Bekir Karlik, “A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network”, Expert Systems with Applications, 30 August 2008, pp. 1-6.
    54. [54]  Victor-Emil Neagoe, Iuliana-Florentina Iatan and Sorin Grunwald, “A Neuro-Fuzzy Approach to Classification of ECG Signals for Ischemic Heart Disease Diagnosis”, AMIA Annu Symp Proc. 2003; pp. 494–498.
    55. [55]  Nong Weixin, “A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach,” Australian Physical & Engineering Sciences in Medicine, Vol. 39, No. 4, Dec 2016, pp. 903-912.
    56. [56]  S. Mahapatra, D. Mohanta, P. Mohanty, S. K. Nayak, and P. K. Behari, “A Neuro-fuzzy Based Model for Analysis of an ECG Signal Using Wavelet Packet Tree,” Procedia Comput. Sci., vol. 92, pp. 175–180, 2016.