Skip to main content
SpringerLink
Log in

Ventricular beat classifier using fractal number clustering

  • Computing and Data Processing
  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

A two-stage ventricular beat ‘associative’ classification procedure is described. The first stage separates typical beats from extrasystoles on the basis of area and polarity rules. At the second stage, the extrasystoles are classified in self-organised cluster formations of adjacent shape parameter values. This approach avoids the use of threshold values for discrimination between ectopic beats of different shapes, which could be critical in borderline cases. A pattern shape feature conventionally called a ‘fractal number’, in combination with a polarity attribute, was found to be a good criterion for waveform evaluation. An additional advantage of this pattern classification method is its good computational efficiency, which affords the opportunity to implement it in real-time systems.

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.

Similar content being viewed by others

References

  • Barro, S., Ruiz, R. andMira, J. (1990) Fuzzy beat labeling for intelligent arrhythmia monitoring.Comput. Biomed. Res.,23, 240–258.

    Article  Google Scholar 

  • Forster, C. andHandwerker, H. O. (1990) Automatic classification and analysis of microneurographic spike data using a PC/AT.J. Neurosci. Meth.,31, 109–118.

    Article  Google Scholar 

  • Friesen, G. M., Jannet, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R. andNagle, H. T. (1990) A comparison of the noise sensitivity of nine QRS detection algorithms.IEEE Trans.,BME-37, 85–98.

    Google Scholar 

  • Gelsema, E. S., Bao, H. F., Smeulders, A. W. M. andHarink, H. G. (1988) Application of the method of multiple thresholding to white blood cell classification.Comput. Biol. & Med.,18, 65–74.

    Article  Google Scholar 

  • Goldberger, A. L. (1987) Fractals in physics and medicine.Yale J. Biol. Med.,60, 421–435.

    Google Scholar 

  • Katz, M. J. (1988) Fractals and the analysis of waveforms.Comput. Biol. & Med.,18, 145–156.

    Article  Google Scholar 

  • Kohn, A. F. (1989) Fractal number and spectral skewness: two features for the pattern classification of motor unit action potentials. Proc. 11th Annual Int. Conf. IEEE Eng. in Med. & Biol. Soc., Seattle, Washington, 8th–12th Nov.

  • Lanza, G. A., Lucente, M., Rebuzzi, A. G., Cortellessa, M. C., Tamburi, S., Mancuso, P., Neri, R. andManzoli, U. (1990) Accuracy in clinical arrhythmia detection of a real-time Holter system (Oxford Medilog 4500).J. Electrocardiol.,23, 301–306.

    Article  Google Scholar 

  • Lin, D., Dicarlo, L. andJenkins, J. (1988) Identification of ventricular tachycardia using morphologic analysis of the intraventricular electrogram (abstract). —Ibid.,21, S120.

    Article  Google Scholar 

  • Lin, K. P. andChang, W. H. (1989) QRS feature extraction using linear prediction.IEEE Trans.,BME-36, 1050–1055.

    Google Scholar 

  • Lowery, M., De Marchena, E. J., Castellanos, A., Myerburg, R. J. andKessler, K. M. (1990) Interrelationship of variable coupling, multiformity and repetitive forms: Implications for classification of ventricular arrhythmias.Am. Heart J.,119, 301–307.

    Google Scholar 

  • Mandelbrot, B. R. (1983)The fractal geometry of nature. W. H. Freeman & Co., New York, USA.

    Google Scholar 

  • Merri, M., Benhorin, J., Alberti, M., Locati, E. andMoss, A. J. (1989) Electrocardiographic quantification of ventricular repolarization.Circ.,80, 1301–1308.

    Google Scholar 

  • Pahlm, O. andSörnmo, L. (1984) Software QRS detection in ambulatory monitoring—a review.Med. & Biol. Eng. & Comput.,22, 289–297.

    Article  Google Scholar 

  • Paparella, N., Alboni, P., Cappato, R., Pirani, R., Gruppillo, P., Preziosi, S., Battaglia, R., Corio, R., Occari, G., Berti, C. andSapigni, T. (1987) Prominent anterior QRS forces: clinical, electrocardiographic and prospective study.J. Electrocardiol.,20, 233–240.

    Google Scholar 

  • Salerno, D. M., Granrud, G. andHodges, M. (1987) Accuracy of commercial 24-hour electrocardiogram analyzers for quantitation of total repetitive ventricular arrhythmias.Am. J. Cardiol.,60, 1299–1305.

    Article  Google Scholar 

  • Sörnmo, L., Börjesson, P. O., Nygards, M. andPahlm, O. (1981) A method of evaluating QRS shapes features using a mathematical model for the ECG.IEEE Trans.,BME-28, 713–717.

    Google Scholar 

  • Wolberg, W. H. andMangasarian, O. L. (1990) Multisurface method of pattern separation for medical diagnosis applied to breast cytology.Proc. Nat. Acad. Sci. USA,87, 9193–9196.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Biomedical Engineering, Medical Academy, 1 G. Sofiiski, 1431, Sofia, Bulgaria

    H. Bakardjian

Authors
  1. H. Bakardjian
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bakardjian, H. Ventricular beat classifier using fractal number clustering. Med. Biol. Eng. Comput. 30, 495–502 (1992). https://doi.org/10.1007/BF02457828

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02457828

Keywords

Search

Navigation