Abstract
Any natural or biological signal can be seen as a linear combination of meaningful and non-meaningful structures. According to the theory of multiresolution wavelet expansions, one can quantify the degree of information those structures using entropy and then select the most meaningful ones. Herein we propose to use adaptive time and frequency transform (ATFT) to measure wavelet entropy, where one line of approach to ATFT is to use a matching pursuit (MP) framework. The proposed method is tested on a set of heartbeat intervals whose population is composed of healthy and pathological subjects. Our results show that wavelet entropy measure based on MP decomposition can capture significant differences between the analyzed cardiac states that are intrinsically related to the structure of the signal.
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Lucena, F., Cavalcante, A., Takeuchi, Y., Barros, A.K., Ohnishi, N. (2010). Wavelet Entropy Measure Based on Matching Pursuit Decomposition and Its Analysis to Heartbeat Intervals. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_62
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DOI: https://doi.org/10.1007/978-3-642-17537-4_62
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