A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals |
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| Affiliation: | 1. Faculty of Engineering, Autonomous University of Queretaro, Campus San Juan del Rio, Moctezuma 249, Col. San Cayetano, 76807, San Juan del Rio, Queretaro, Mexico;2. Departments of Biomedical Engineering, Biomedical Informatics, Civil, Environmental, and Geodetic Engineering, Electrical and Computer Engineering, Neuroscience, and Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43220, USA;1. Autonomous University of Queretaro (UAQ), Faculty of Engineering, Departments Biomedical and Electromechanical, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P. 76807, San Juan del Río, Qro., Mexico;2. IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo c/da Casazza, SS. 113, 98124, Messina, Italy;3. Department DICEAM of the Mediterranean University of Reggio Calabria, 89060, Reggio Calabria, Italy;4. Departments of Biomedical Informatics, Neurology, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43220, USA;1. Department of Electrical Engineering, Foundation University, Islamabad, Pakistan;2. Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan;1. Vibrations and Acoustic Laboratory (LVA), University of Lyon (INSA), F-69621 Villeurbanne Cedex, France;2. Technical Center of Mechanical industries (CETIM), CS 80067, 60304 Senlis Cedex, France;3. Laboratory of Contacts and Structural Mechanics (LaMCoS), University of Lyon (INSA), F-69621 Villeurbanne Cedex, France;4. PSA Peugeot Citroën, Paris, France;1. Anna University, Chennai, India;2. Velammal Engineering College, Chennai, India |
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| Abstract: | The goal of signal processing is to estimate the contained frequencies and extract subtle changes in the signals. In this paper, a new adaptive multiple signal classification-empirical wavelet transform (MUSIC-EWT) methodology is presented for accurate time–frequency representation of noisy non-stationary and nonlinear signals. It uses the MUSIC algorithm to estimate the contained frequencies in the signal and build the appropriate boundaries to create the wavelet filter bank. Then, the EWT decomposes the time-series signal into a set of frequency bands according to the estimated boundaries. Finally, the Hilbert transform is applied to observe the evolution of calculated frequency bands over time. The usefulness and effectiveness of the proposed methodology are validated using two simulated signals and an ECG signal obtained experimentally. The results demonstrate clearly that the proposed methodology is immune to noise and capable of estimating the optimal boundaries to isolate the frequencies from noise and estimate the main frequencies with high accuracy, especially the closely-spaced frequencies. |
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| Keywords: | Signal processing Wavelet transform Fourier transform Hilbert transform Spectral decomposition |
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