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
Open Access Status
Volume 10
Issue 1
Start page 153
End page 165
Total pages 13
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 1711 Signal Processing
2718 Health Informatics
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.
Keyword Engineering, Biomedical
Medical Laboratory Technology
Engineering
Medical Laboratory Technology
ENGINEERING, BIOMEDICAL
MEDICAL LABORATORY TECHNOLOGY
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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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 11 Dec 2013, 21:22:34 EST by Roheen Gill on behalf of UQ Centre for Clinical Research