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AuthorDong, Shiying
AuthorBoashash, Boualem
AuthorAzemi, Ghasem
AuthorLingwood, Barbara E.
AuthorColditz, Paul B.
Available date2016-03-30T08:22:57Z
Publication Date2014-02
Publication NameMedical and Biological Engineering and Computing
ResourceScopus
CitationDong, S., Boashash, B., Azemi, G., Lingwood, B.E., Colditz, P.B. "Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features" (2014) Medical and Biological Engineering and Computing, 52 (2), pp. 183-191.
ISSN0140-0118
URIhttp://dx.doi.org/10.1007/s11517-013-1129-3
URIhttp://hdl.handle.net/10576/4288
AbstractPerinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time-frequency analysis of heart rate variability (HRV) signals. This approach uses features extracted from the instantaneous frequency and instantaneous amplitude of HRV signal components as well as features based on matrix decomposition of the signals' time-frequency distributions using singular value decomposition and non-negative matrix factorization. The classification between hypoxia and non-hypoxia data is performed using a support vector machine classifier. The proposed method is tested on a dataset obtained from a newborn piglet model with a controlled hypoxic insult. The chosen HRV features show strong performance compared to conventional spectral features and other existing methods of hypoxia detection with a sensitivity 93.3 %, specificity 98.3 % and accuracy 95.8 %. The high predictive value of this approach to detecting hypoxia is a substantial step towards developing a more accurate and reliable hypoxia detection method for use in human foetal monitoring.
SponsorQNRF, a member of Qatar foundation, under NPRP Grant No: 09-626-2-243
Languageen
PublisherSpringer Berlin Heidelberg
SubjectFoetal hypoxia detection
Heart rate variability
Instantaneous frequency
Matrix decomposition
Time-frequency distribution
TitleAutomated detection of perinatal hypoxia using time-frequency-based heart rate variability features
TypeArticle
Pagination183-191
Issue Number2
Volume Number52


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