Next Article in Journal
Detection Performance Improvement of Distributed Vibration Sensor Based on Curvelet Denoising Method
Next Article in Special Issue
A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces
Previous Article in Journal
Tracking the Evolution of the Internet of Things Concept Across Different Application Domains
Previous Article in Special Issue
A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(6), 1386; doi:10.3390/s17061386

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
Received: 13 April 2017 / Revised: 22 May 2017 / Accepted: 22 May 2017 / Published: 14 June 2017
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
View Full-Text   |   Download PDF [1162 KB, uploaded 15 June 2017]   |  

Abstract

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. View Full-Text
Keywords: time-frequency decomposition; truncated fourier series model; sparse linear regression; ℓ1 regularization; ADMM time-frequency decomposition; truncated fourier series model; sparse linear regression; ℓ1 regularization; ADMM
Figures

Figure 1

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top