Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner
1
School of Life Science and Technology, Xidian University, Xi’an 710071, China
2
School of Electronics Engineering, Kungpook National University, Daegu 702-701, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Wan-Young Chung
Sensors 2017, 17(6), 1386; https://doi.org/10.3390/s17061386
Received: 13 April 2017
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Revised: 22 May 2017
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Accepted: 22 May 2017
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Published: 14 June 2017
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.
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Keywords:
time-frequency decomposition; truncated fourier series model; sparse linear regression; ℓ1 regularization; ADMM
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MDPI and ACS Style
Wang, Y.; Veluvolu, K.C. Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner. Sensors 2017, 17, 1386. https://doi.org/10.3390/s17061386
AMA Style
Wang Y, Veluvolu KC. Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner. Sensors. 2017; 17(6):1386. https://doi.org/10.3390/s17061386
Chicago/Turabian StyleWang, Yubo, and Kalyana C. Veluvolu. 2017. "Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner" Sensors 17, no. 6: 1386. https://doi.org/10.3390/s17061386
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