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Fuzzy-Hybrid Neural Network Based ECG Beat Recognition Using Three Different Types of Feature Sets

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Cardiovascular Engineering: An International Journal

Abstract

This paper represents the application on the fuzzy-hybrid neural network for electrocardiographic (ECG) beat recognition and classification. We proposed the approach to ECG beat recognition that is based on QRS complex classification. Instead of the original QRS waveform, we have used three different types of QRS features sets. The main objective of this work was to develop a technique which was less sensitive to the morphological variation of the QRS waveform. Linear predictive coefficients (LPC), third-order cumulant-based auto regressive coefficient (AR), and the variance of the wavelet transform detail coefficients of the isolated QRS complexes are used as the features. It will be shown that the wavelet transform based approach is better than the other techniques. The main properties of the proposed method are simplicity, moderate recognition rate, and fast computation time.

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References

  • Barro S, Fernandez-Delgado M, Villa-Sobrino JA, Regueiro CV, and Sanchez E. Classifying multichannel ECG patterns with an adaptive neural network. IEEE Eng Med Biol Mag17: 45-55, 1998.

    Google Scholar 

  • Dickhaus H, and Heinrich H. Classifying biosignals with wavelet networks. IEEE Eng Med Biol Mag15: 103-111, 1996.

    Google Scholar 

  • Haykin S. Neural Networks. A Comprehensive Foundation. New York: Mcmillan College, 1994.

    Google Scholar 

  • Hinich MJ. Testing for gaussianity and linearity of a stationary time. Time Series Anal3: 169-176, 1982.

    Google Scholar 

  • Hu YH, Palreddy S, and Tompkins W. A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans Biomed Eng44: 891-900, 1997.

    Google Scholar 

  • Hu YH, and Tompkins W. A real time QRS detection algorithm. IEEE Trans Biomed Eng32: 230-236, 1985.

    Google Scholar 

  • Minami K, Nakajima H, and Toyoshima T. Real-time discrimination of ventricular tachyarrhytmia with Fourier-transform neural network. IEEE Trans Biomed Eng46: 179-185, 1999.

    Google Scholar 

  • Nikias C, and Petropulu A. Higher Order Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1993.

    Google Scholar 

  • Oien GE, Bertelsen NA, Eftestol T, and Husoy JH. ECG rhythm classification using artificial neural networks. In Proceedings of the IEEE Digital Signal Processing Workshop, 1996, pp. 514-517.

  • Osowski S, and Linh TH. 'ECG beat recognition using fuzzy hybrid neural network. IEEE Trans Biomed Eng48: 1265-1271, 2001.

    Google Scholar 

  • Wang Li-Xin. A Course in Fuzzy Systems and Control. Englewood Cliffs NJ: Prentice-Hall, pp. 347-350.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Faculty of Engineering, Ege University, Bornova, İzmir, Turkey

    Mehmet Engin & Serdar Demirağ

Authors
  1. Mehmet Engin
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  2. Serdar Demirağ
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Engin, M., Demirağ, S. Fuzzy-Hybrid Neural Network Based ECG Beat Recognition Using Three Different Types of Feature Sets. Cardiovascular Engineering 3, 71–80 (2003). https://doi.org/10.1023/A:1025515632674

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  • DOI: https://doi.org/10.1023/A:1025515632674

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