Next Article in Journal
Ereptiospiration
Previous Article in Journal
4D Flow Assessment of Vorticity in Right Ventricular Diastolic Dysfunction
Article Menu

Export Article

Open AccessArticle
Bioengineering 2017, 4(2), 32; doi:10.3390/bioengineering4020032

Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study

1
Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
2
Rush University Medical Center, 1653 W Congress Pky, Chicago, IL 60612, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Gou-Jen Wang
Received: 10 February 2017 / Revised: 1 April 2017 / Accepted: 5 April 2017 / Published: 7 April 2017
View Full-Text   |   Download PDF [7668 KB, uploaded 7 April 2017]   |  

Abstract

Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification. View Full-Text
Keywords: seismocardiography; vibrocardiography; time-frequency analysis; short-time Fourier transform; continuous wavelet transform; chirplet transform; signal processing; signal characteristics seismocardiography; vibrocardiography; time-frequency analysis; short-time Fourier transform; continuous wavelet transform; chirplet transform; signal processing; signal characteristics
Figures

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).

Supplementary material

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

Taebi, A.; Mansy, H.A. Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study. Bioengineering 2017, 4, 32.

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.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Bioengineering EISSN 2306-5354 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top