Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels

Dong, Shiying, Azemi, Ghasem and Boashash, Boualem (2013) Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels. Biomedical Signal Processing and Control, 10 1: 153-165. doi:10.1016/j.bspc.2013.11.008


Author Dong, Shiying
Azemi, Ghasem
Boashash, Boualem
Title Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels
Journal name Biomedical Signal Processing and Control   Check publisher's open access policy
ISSN 1746-8094
Publication date 2013-12-15
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.bspc.2013.11.008
Volume 10
Issue 1
Start page 153
End page 165
Total pages 13
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract The analysis of heart rate variability (HRV) provides a non-invasive tool for assessing the autonomicregulation of cardiovascular system. Quadratic time–frequency distributions (TFDs) have been used toaccount for the non-stationarity of HRV signals, but their performance is affected by cross-terms. Thisstudy presents an improved type of quadratic TFD with a lag-independent kernel (LIK-TFD) by introducinga new parameter defined as the minimal frequency distance among signal components. The resultingTFD with this LIK can effectively suppress the cross-terms while maintaining the time–frequency (TF)resolution needed for accurate characterization of HRV signals. Results of quantitative and qualitativetests on both simulated and real HRV signals show that the proposed LIK-TFDs outperform other TFDscommonly used in HRV analysis. The findings of the study indicate that these LIK-TFDs provide morereliable TF characterization of HRV signals for extracting new instantaneous frequency (IF) based clinicallyrelated features. These IF based measurements shown to be important in detecting perinatal hypoxicinsult – a severe cause of morbidity and mortality in newborns.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 15 December 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2014 Collection
 
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Created: Wed, 11 Dec 2013, 21:22:34 EST by Roheen Gill on behalf of UQ Centre for Clinical Research