System Classification by Using Discriminant Functions of Time-Frequency Features

  • Miguel Mendoza Reyes
  • Juan V. Lorenzo-Ginori
  • A. Taboada-Crispí
  • Yakelin Luna Carvajal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Time-frequency representations (TFR) convey relevant information about systems that can not be obtained under stationary conditions. In this paper, a methodology to classify systems using the information obtained from time-frequency representations during transient phenomena is described and tested experimentally. The study includes an assessment of the features to be extracted from the TFR, which are relevant for the desired classification, as well as the construction of the appropriate discriminant functions using them. The methodology is tested by means of a biomedical example related to patient’s classification.


time-frequency distributions feature extraction 


  1. 1.
    Cohen, L.: Time Frequency Distribution – A Review. Proceedings of IEEE 77, 941–981 (1989)CrossRefGoogle Scholar
  2. 2.
    Luccini, D., et al.: Impairment in Cardiac Autonomic Regulation Preceding Arterial Hypertension in Humans. Insights from Spectral Analysis of Beat-by-Beat Cardiovascular Variability. Circulation, 2673–2679 (November 2002)Google Scholar
  3. 3.
    Task Force of European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart Rate Variability. Standards of Measurement, Physiological Interpretation and Clinical Use. European Heart Journal 17, 354–381 (1996)Google Scholar
  4. 4.
    Davrath, L.R., Goren, Y., Pinhas, I., Toledo, E., Akselrod, S.: Early autonomic malfunction in normotensive individuals with a genetic predisposition to essential hypertension. Am J Physiol Heart Circ. Physiol. 285, H1697–H1704 (2003), Google Scholar
  5. 5.
    Benet, M., Apollinaire, J., Torres, J., Peraza, S.: Reactividad cardiovascular y factores de riesgos cardiovasculares en individuos normotensos menores de 40 años. Revista Española de Salud Pública 77(1), 143–150 (2003)Google Scholar
  6. 6.
    Steenis, H.G., Martens, W.L.J., Tulen, J.H.M.: Time–Frequency Parameters of Heart rate Variability. IEEE Engineering in Medicine and Biology 21(4), 46–58 (2002)CrossRefGoogle Scholar
  7. 7.
    Houle, M., Billman, G.: Low-frequency component of the heart rate variability spectrum: a poor marker of sympathetic activity. Am J Physiol. Heart Circ. Physiol. 276(1), H215–H223 (1999)Google Scholar
  8. 8.
    Ebden, M., Tarassenko, L., Payne, S., Darowski, A., A., Price, J.: Time-frequency analysis of the ECG in the diagnosis of vasovagal syndrome in older people. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, September 1-5, pp. 290–293 (2004)Google Scholar
  9. 9.
    Yoshiuchi, K., et al.: Use of time-frequency analysis to investigate temporal patterns of cardiac autonomic response during head-up tilt in chronic fatigue syndrome. Autonomic Neuroscience: Basic and Clinical 113, 55–62 (2004)CrossRefGoogle Scholar
  10. 10.
    Jasson, S., et al.: Instant Power Spectrum Analysis of Heart Rate Variability During Orthostatic Tilt Using a Time-/Frequency-Domain Method. Circulation 96, 3521–3526 (1997)Google Scholar
  11. 11.
    Clariá, F., Vallverdú, M., Baranowski, R., Chonowska, L., Martínez, P., Caminal, P.: Time-Frequency Representation of the HRV: A Tool to Characterize Sudden Cardiac Death in Hypertrophy Cardiomyopatthy Patients. In: Proceedings of the 22nd Annual EMBS International Conference, Chicago, IL, July 23-28, pp. 71–73 (2000)Google Scholar
  12. 12.
    Williams, W., Jeong, J.: Reduced Interference Time-Frequency Distributions. In: Boashash, B. (ed.) Time-Frequency Signal Analysis, Longman Cheshire, pp. 74–97 (1992)Google Scholar
  13. 13.
    Auger, F., Flandrin, P., Goncalves, P., Lemoine, O.: Time Frequency Toolbox for use with MATLAB, Available,,
  14. 14.
    Romeu, J.L.: Anderson-Darling: A Goodness of Fit Test for Small Simples Assumptions. RAC START,10(5). Available:
  15. 15.
    SPSS for Windows, Release 9.01 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miguel Mendoza Reyes
    • 1
  • Juan V. Lorenzo-Ginori
    • 1
  • A. Taboada-Crispí
    • 1
  • Yakelin Luna Carvajal
    • 2
  1. 1.Center for Studies on Electronics and Information TechnologiesUniversidad Central “Marta Abreu” de Las VillasSanta ClaraCuba, USA
  2. 2.Ministry of Public HealthSanta ClaraCuba

Personalised recommendations