TAILIEUCHUNG - Báo cáo sinh học: " Research Article Approximating the Time-Frequency Representation of Biosignals with Chirplets"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Approximating the Time-Frequency Representation of Biosignals with Chirplets | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 857685 10 pages doi 2010 857685 Research Article Approximating the Time-Frequency Representation of Biosignals with Chirplets Omid Talakoub Jie Cui and Willy Wong Department of Electrical and Computer Engineering University of Toronto On Canada M5S 1A1 Correspondence should be addressed to Willy Wong willy@ Received 14 January 2010 Accepted 29 April 2010 Academic Editor Syed Ismail Shah Copyright 2010 Omid Talakoub et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A new member of the Cohen s class time-frequency distribution is proposed. The kernel function is determined adaptively based on the signal of interest. The kernel preserves the chirp-like components while removing interference terms generated due to the quadratic characteristic of Wigner-Ville distribution. This approach is based on the chirplet as an underlying model of biomedical signals. We illustrate the method using a number of common biological signals including echo-location and evoked potential signals. Finally the results are compared with other techniques including chirplet decomposition via matching pursuit and the Choi-Williams distribution function. 1. Introduction Many signals of biological origin are nonstationary in nature. Examples include speech signals bat calls as well as neuroelectric signals like electroencephalography EEG 1 2 heart rate variability 3 or event-related potentials ERPs 4 . Time-frequency or time-scale representations in recent years have found significant application in nonstationary analysis of a wide-range of signals including biomedical signals 5-13 . Constructing a time-frequency representation involves mapping a one-dimensional time-domain signal x t into a two-dimensional

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