Abstract
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.
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Reyes, M.M., Lorenzo-Ginori, J.V., Taboada-Crispí, A., Carvajal, Y.L. (2006). System Classification by Using Discriminant Functions of Time-Frequency Features. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_95
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DOI: https://doi.org/10.1007/11892755_95
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