Time-Frequency Analysis of Non-Stationary Biological Signals with SparseLinear Regression Based Fourier Linear Combiner

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/486283.do
DOI
10.3390/s17061386
Title
Time-Frequency Analysis of Non-Stationary Biological Signals with SparseLinear Regression Based Fourier Linear Combiner
Description
The research work of Yubo Wang was supported by the Fundamental ResearchFunds for the Central Universities (Xidian University, Grant No.XJS16055). The research work of Kalyana C. Veluvolu was supported by theNational Research Foundation of Korea (NRF) grant funded by the Koreagovernment (Ministry of Science, ICT & Future Planning) (No.2017R1A2B2006032) and in part by the BK21 Plus project funded by theMinistry of Education, Korea (21A20131600011).
abstract
I t 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.
provenance
Made available in Cube on 2018-09-28T16:03:27Z (GMT). No. of bitstreams: 0
language
English
author
Wang, Yubo
Veluvolu, Kalyana C.
orcid
Veluvolu, Kalyana/0000-0003-1542-8627; Wang, Yubo/0000-0002-2708-3526
accessioned
2018-09-28T16:03:27Z
available
2018-09-28T16:03:27Z
issued
2017
citation
SENSORS(17): 6
issn
1424-8220
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/486283.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345274001
Jurisdiction
Rep.of Korea
Project Name
KNU ICT Research and Education Center
rights
openAccess
subject
time-frequency decomposition
truncated fourier series model
sparselinear regression
l(1) regularization
ADMM
type
article


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