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

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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.

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Acknowledgments

This work is financially supported by QNRF, a member of Qatar foundation, under NPRP Grant No: 09-626-2-243.

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Authors and Affiliations

  1. UQ Centre for Clinical Research, The University of Queensland, Herston, QLD, Australia

    Shiying Dong, Boualem Boashash, Ghasem Azemi, Barbara E. Lingwood & Paul B. Colditz

  2. College of Engineering, Qatar University, Doha, Qatar

    Boualem Boashash

  3. Department of Electrical Engineering, Razi University, Kermanshah, Iran

    Ghasem Azemi

Authors
  1. Shiying Dong
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  2. Boualem Boashash
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  3. Ghasem Azemi
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  4. Barbara E. Lingwood
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  5. Paul B. Colditz
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Corresponding author

Correspondence to Shiying Dong.

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Dong, S., Boashash, B., Azemi, G. et al. Automated detection of perinatal hypoxia using time–frequency-based heart rate variability features. Med Biol Eng Comput 52, 183–191 (2014). https://doi.org/10.1007/s11517-013-1129-3

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  • DOI: https://doi.org/10.1007/s11517-013-1129-3

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