Fuzzy Clustering in Medicine: Applications to Electrophysiological Signal Processing

  • Amir B. Geva
  • Dan H. Kerem
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 83)


The essence of modern medicine is a continuous process of decision-making based on the intelligent evaluation of voluminous yet often inconclusive data gathered from patients. In many clinical setups such as intensive care units and epilepsy care units, monitored patients produce a vast amount of biomedical data from online continuous recordings of ECG, EEG, blood pressure, temperature, etc., as well as from X-ray, CT and MRI imaging. In the current state of affairs, there are objective difficulties in processing and interpreting all this data with the aim of extracting the relevant information.


Fuzzy Cluster Fuzzy Cluster Algorithm Euclidean Distance Function Fuzzy Cluster Analysis Heart Rate Variability Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Andresen D, Bruggemann T, Behrens S, Ehlers C (1995) Heart rate response to provocative maneuvers. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publ., Armonk, NY.Google Scholar
  2. Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, pp 228.CrossRefGoogle Scholar
  3. Bezdek JC, Pal NR (1995) Tow Soft Relatives of Learning Vector Quantization. Neural Networks 8(5):729–743.CrossRefGoogle Scholar
  4. Bezdek JC, Hall LO, Clark MC, Goldgof DB, Clarke LP (1997) Medical image analysis with fuzzy models. Stat. Methods Med. Res. 6:191–214.PubMedCrossRefGoogle Scholar
  5. Bankman I, Gath I (1987) Feature extraction and clustering of EEG during anaesthesia. Med. & Biol. Eng. & Comput. 25:474–477.CrossRefGoogle Scholar
  6. Bianchi, A. M., Mainardi, L. T., Signorini, M. G., Mainardi, M. And Cerutti, S. (1993) Time variant power spectrum analysis for the detection of transient episodes in HRV signal. IEEE Trans. Biom. Eng. 40:136–144.CrossRefGoogle Scholar
  7. Brown TB, Beightol LA, Koh J, Ecckberg DL (1993) Important influence of respiration on human R-R interval power spectra is largely ignored. J. Appl. Physiol. 75:2310–2317.PubMedGoogle Scholar
  8. Cabello D, Barro S, Salceda JM, Ruiz R, Mira J (1991) Fuzzy K-nearest neighbor classifiers for ventricular arrythmia detection. Int. J. Biomed. Comput. 27:77–93.PubMedCrossRefGoogle Scholar
  9. Cannon RL, Dave JV, Bezdek JC (1986) Efficient implementation of the fuzzy-c-means clustering algorithm. IEEE trans. Pattern Anal. & Mach. Intell. 8:248–255.CrossRefGoogle Scholar
  10. Cerutti S, Bianchi AM, Mainardi LT (1995) Spectral analysis of the heart rate variability signal. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publ., Armonk, NY.Google Scholar
  11. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh R, Silbiger MS (1998) Unsupervised brain tumor segmentation using knowledge-based fuzzy techniques. In: Teodorescu Hn, Kandel A, Jain LCJ Fuzzy and Neurofuzzy Systems in Medicine. CRC International Series on Computational Inteligence, CRC Press, Boca Raton, Florida, pp 137–169.Google Scholar
  12. Deller JR, Proakis JG, Hansen JHL (1987) Discrete-time processing of speech signals. Prentice-Hall,Google Scholar
  13. Gath I, Bar-On E (1980) Computerized method for scoring of polygraphic sleep recordings. Comput. Progr. Biomed. 11:217–223,.CrossRefGoogle Scholar
  14. Gath I, Geva AB (1989 a) Unsupervised Optimal Fuzzy Clustering. IEEE Trans. on Pattern Anal. Machine Intell. 7:773–781.CrossRefGoogle Scholar
  15. Gath I, Geva AB (1989 b) Fuzzy clustering for the estimation of the parameters of the components of mixtures of normal distributions. Pattern Recognition Letters 9:77–86.CrossRefGoogle Scholar
  16. Gath I, Hoory D, (1995) Fuzzy clustering of elliptic ring-shaped clusters. Pattern Recog. Let. 16: 727–741.CrossRefGoogle Scholar
  17. Gath I, Lehman D, Bar-On E (1983) Fuzzy clustering of EEG signal and vigilance performance. Int. J. Neurosci. 20: 303–312,PubMedCrossRefGoogle Scholar
  18. Geva AB (1998) Feature extraction and state recognition in biomedical signals with hierarchical unsupervised fuzzy clustering methods. Medical & Biological Engineering & Computing 36: 608–614.CrossRefGoogle Scholar
  19. Geva AB, Pratt H (1994) Unsupervised clustering of evoked potentials by Waveform. Medical & Biological Engineering & Computing 32:543–550.CrossRefGoogle Scholar
  20. Geva AB, Pratt H, Zeevi YY (1997) Multichannel wavelet-type decomposition of evoked potentials: model-based recognition of generator activity. Med. & Biol. Eng. & Comput. 95:40–46.CrossRefGoogle Scholar
  21. Geva AB, Kerem DH (1998) Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering. IEEE Trans. Biomed Engin. 45:1205–1216.CrossRefGoogle Scholar
  22. Geva AB, Kerem DH (1999) Brain state identification and forecasting of acute pathology using unsupervised fuzzy clustering of EEG temporal patterns. In: Teodorescu HN, Kandel A, Jain LC (eds) Fuzzy and Neurofuzzy systems in Medicine. CRC International Series on Computational Inteligence, CRC Press, Boca Raton, Florida, pp 57–93.Google Scholar
  23. Goldberger Al, West B J (1987) Applications of nonlinear dynamics to clinical cardiology. Ann. NY Acad. Sci. 504:195–213.CrossRefGoogle Scholar
  24. Hamilton D (1994) Time Series Analysis. Princeton University Press, pp. 677–699.Google Scholar
  25. Harel T, Gath I, Ben-Haim S (1997) High resolution estimation of the heart rate variability signal. Med. & Biol. Eng. & Comput. 35:1–5.CrossRefGoogle Scholar
  26. Kamath MV, Fallen EL (1995) Correction of the heart rate variability signal for ectopies and missing beats. In: Malik M, Camm AJ (eds) (1995) Heart rate variability. Futura Publ., Armonk, NY.Google Scholar
  27. Krishnapuram R, Keller J (1993) A possibilistic Approach to Clustering. IEEE Transactions on Fuzzy Systems 1(2):98–110.CrossRefGoogle Scholar
  28. Le Van Quyen M, Martinerie J, Baulac M, Varela F (1999) Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. Neuroreport 13: 2149–2155.CrossRefGoogle Scholar
  29. Le Van Quyen M, Martinerie J, Navarro V, Adam C, Varela F, Baulac M (1999) Evidence of pre-seizure changes on scalp EEG recordings by non linear analysis. Epilepsia 40 suppl 7: 174.Google Scholar
  30. Loiseau P (1995) Epilepsies. In: Guide to clinical neurology. Churchill, Livingstone NY, pp 903–914.Google Scholar
  31. Lopes da Silva FH, Pijn JP, Veli DN (1996) Signal processing of EEG: evidence for chaos or noise. An application to seizure activity in epilepsy. In: Advances in processing and pattern analysis of biological signals. Plenum Press, New York, pp 21–32.Google Scholar
  32. Lipsitz LA, Mietus J, Moody JB Goldberger AL (1990) Spectral characteristics of heart rate variability before and during postural tilt. Relations to aging and risk of syncope. Circulation 81:1803–1810.PubMedCrossRefGoogle Scholar
  33. Long TJ, Robinson SE, Quinlivan LS (1999) Effectiveness of heart rate seizure detection compared to EEG in an epilepsy monitoring unit (EMU). Epilepsia 40 suppl. 7:174.Google Scholar
  34. Malik M (1995) Geometrical methods for heart rate variability assessment. In: Malik M Camm AJ (eds) Heart rate variability. Futura Publ., Armonk NY, pp 47–62.Google Scholar
  35. Malik, M. (chairman) (1996) Heart rate variability: standards of measurements, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing & Electrophysiology. Circulation 93:1043–1065.CrossRefGoogle Scholar
  36. Malik M, Camm AJ (eds) (1995) Heart rate variability. Futura Publ., Armonk, NY.Google Scholar
  37. Masulli F and Schenone A (1999) A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif. Intell. Med. 16:129–147.PubMedCrossRefGoogle Scholar
  38. O’Malley MJ, Abel MF, Damiano DL, Vaughan CL (1997) Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters. IEEE Trans. Rehabil. Engin. 5: 300–309.CrossRefGoogle Scholar
  39. Pagani, M., Malfatto, G., Pierini, S., Casati, R., Masu, A.M., Poli, M., Guzzetti, S., Lombardi, F., Cerutti, S., and Malliani, A. (1988) Spectral analysis of heart rate variability in the assessment of autonomic diabetic neuropathy. J. Auton Nery Syst. 23: 143–153.CrossRefGoogle Scholar
  40. Peters RM, Shanies SA, Peters JC (1998) Fuzzy cluster analysis — a new method to predict future cardiac events in patients with positive stress tests. Jpn. Circ. J. 62:750–754.PubMedCrossRefGoogle Scholar
  41. Sackellares C, Iasemidis LD (1999) Detection of the preictal transition in scalp EEG. Epilepsia 40 supp17:174.Google Scholar
  42. Schlactman M, Green JS (1991) Signal-averaged electrocardiography: a new technique for determining which patients may be at risk for sudden cardiac death. Focus. Crit. Care 18: 202–221.Google Scholar
  43. Schmidt G, Morfill GE (1995) Nonlinear methods for heart rate variability assessment. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publ., Armonk, NY.Google Scholar
  44. Skinner JE, Carpeggiani C, Landisman CE, Fulton KW (1991) The correlation-dimension of the heartbeat is reduced by myocardial ischemia in conscious pigs. Circ. Res. 68:966–976.PubMedCrossRefGoogle Scholar
  45. Skinner, J. E., C. M. Pratt And T. Vybiral (1993) Reduction in the correlation dimension of heartbeat intervals precedes imminent ventricular fibrilation in human subjects. Am. Heart J. 125:731–743.PubMedCrossRefGoogle Scholar
  46. Suckling J, Sigmundsson T, Greenwood K, Bullmore ET (1999) A modified fuzzy clustering algorithm for operator in dependent brain tissue classification of dual echo MR images. Magn. Reson. Imaging 17:1065–1076.PubMedCrossRefGoogle Scholar
  47. Tolias YA, Panas SM (1998) A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imaging 17:263–273,PubMedCrossRefGoogle Scholar
  48. Vila J, Palacios F, Presedo J, Fernandez-Delgado M, Felix P, Barro S (1997) Timefrequency analysis of heart-rate variability: an improved method for monitoring and diagnosing miocardial ischemia. IEEE Eng. Med Biol. 16:119–126.CrossRefGoogle Scholar
  49. Weigend AS, Gershenfeld NA (eds) (1994) Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley,Google Scholar
  50. Wilkund U, Akay M, Niklasson U (1997) Short-term analysis of heart-rate variability by adapted wavelet transforms. IEEE Eng. in Med. & Biol. 16:113–118.CrossRefGoogle Scholar
  51. Zouridakis G, Boutros NN, Jansen BH (1997) A fuzzy clustering approach to study the auditory P50 component in schizophrenia. Psychiatry Res. 69:169–181.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Amir B. Geva
    • 1
  • Dan H. Kerem
    • 1
  1. 1.Electrical Engineering DepartmentBen-Gurion University of the NegevBeer-ShevaIsrael

Personalised recommendations