Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features

Dong, Shiying, Boashash, Boualem, Azemi, Ghasem, Lingwood, Barbara E. and Colditz, Paul B. (2014) Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features. Medical and Biological Engineering and Computing, 52 2: 183-191. doi:10.1007/s11517-013-1129-3

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Author Dong, Shiying
Boashash, Boualem
Azemi, Ghasem
Lingwood, Barbara E.
Colditz, Paul B.
Title Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features
Journal name Medical and Biological Engineering and Computing   Check publisher's open access policy
ISSN 0140-0118
Publication date 2014-02
Year available 2013
Sub-type Article (original research)
DOI 10.1007/s11517-013-1129-3
Open Access Status
Volume 52
Issue 2
Start page 183
End page 191
Total pages 9
Place of publication Heidelberg, Germany
Publisher Springer
Collection year 2014
Language eng
Formatted abstract
Perinatal 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.
Keyword Foetal hypoxia detection
Heart rate variability
Time–frequency distribution
Instantaneous frequency
Matrix decomposition
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 24 November 2013

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2014 Collection
School of Medicine Publications
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
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Created: Mon, 09 Dec 2013, 13:25:35 EST by Roheen Gill on behalf of UQ Centre for Clinical Research