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AuthorDong S.
AuthorAzemi G.
AuthorBoashash B.
Available date2022-05-31T19:01:36Z
Publication Date2014
Publication NameBiomedical Signal Processing and Control
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.bspc.2013.11.008
URIhttp://hdl.handle.net/10576/31917
AbstractThe analysis of heart rate variability (HRV) provides a non-invasive tool for assessing the autonomic regulation of cardiovascular system. Quadratic time-frequency distributions (TFDs) have been used to account for the non-stationarity of HRV signals, but their performance is affected by cross-terms. This study presents an improved type of quadratic TFD with a lag-independent kernel (LIK-TFD) by introducing a new parameter defined as the minimal frequency distance among signal components. The resulting TFD 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 qualitative tests on both simulated and real HRV signals show that the proposed LIK-TFDs outperform other TFDs commonly used in HRV analysis. The findings of the study indicate that these LIK-TFDs provide more reliable TF characterization of HRV signals for extracting new instantaneous frequency (IF) based clinically related features. These IF based measurements shown to be important in detecting perinatal hypoxic insult - a severe cause of morbidity and mortality in newborns.
Languageen
PublisherElsevier Ltd
SubjectHeart
Robustness (control systems)
Heart rate variability
Hypoxia detection
Instantaneous frequency
Lag-independent kernel
Time-frequency distributions
Signal processing
animal experiment
article
decision making
electrocardiogram
heart rate variability
kernel method
newborn
newborn hypoxia
nonhuman
piglet
priority journal
time
time frequency analysis
TitleImproved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time-frequency distributions with data-adapted kernels
TypeArticle
Pagination153-165
Issue Number1
Volume Number10
dc.accessType Abstract Only


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