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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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References

  1. Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS (2006) Heart rate variability: a review. Med Biol Eng Comput 44(12):1031–1051

    Article  Google Scholar 

  2. Becker JH, Westerhuis ME, Sterrenburg K, van den Akker ES, van Beek E, Bolte AC, van Dessel TJ, Drogtrop AP, van Geijn HP, Graziosi GC, van Lith JM, Mol BW, Moons KG, Nijhuis JG, Oei SG, Oosterbaan HP, Porath MM, Rijnders RJ, Schuitemaker NW, Wijnberger LD, Willekes C, Visser GH, Kwee A (2011) Fetal blood sampling in addition to intrapartum ST-analysis of the fetal electrocardiogram: evaluation of the recommendations in the Dutch STAN® trial. BJOG 118(10):1239–1246

    Article  CAS  PubMed  Google Scholar 

  3. Berry MW, Browne M, Langville AN, Pauca VP, Plemmons RJ (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52(1):155–173

    Article  Google Scholar 

  4. Bjorkman ST, Foster KA, O’driscoll SM, Healy GN, Lingwood BE, Burke C, Colditz PB (2006) Hypoxic/ischemic models in newborn piglet: comparison of constant FiO2 versus variable FiO2 delivery. Brain Res 1100(1):110–117

    Article  PubMed  Google Scholar 

  5. Björkman ST, Miller SM, Rose SE, Burke C, Colditz PB (2010) Seizures are associated with brain injury severity in a neonatal model of hypoxia–ischemia. Neuroscience 166(1):157–167

    Article  PubMed  Google Scholar 

  6. Boardman A, Schlindwein FS, Thakor NV, Kimura T, Geocadin RG (2002) Detection of asphyxia using heart rate variability. Med Biol Eng Comput 40(6):618–624

    Article  CAS  PubMed  Google Scholar 

  7. Boashash B (2003) Time frequency signal analysis and processing: a comprehensive reference. Elsevier, Oxford

    Google Scholar 

  8. Boashash B, Azemi G, O’Toole JM (2013) Time–frequency processing of non-stationary signals: advanced TFD design to aid diagnosis with highlights from medical applications. IEEE Signal Process Mag 30(6):108–119

    Article  Google Scholar 

  9. Chudáček V, Spilka J, Janků P, Koucký M, Lhotská L, Huptych M (2011) Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features. Physiol Meas 32(8):1347–1360

    Article  PubMed  Google Scholar 

  10. Dong S, Azemi G, Boashash B (n.d.) Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels. Under review in the journal of Biomed Signal Process Control

  11. Dong S, Xu F, Lingwood B, Mesbah M, Boashash B (2010) R-wave detection: a comparative analysis of four methods using newborn piglet ECG. In: Proceeding of the 10th international conference on information sciences signal processing and their applications (ISSPA), Kuala Lumpur, Malaysia, pp 320–323

  12. Dong S, Mesbah M, Lingwood BE, O’Toole JM, Boashash B (2011) Time–frequency analysis of heart rate variability in neonatal piglets exposed to hypoxia. In: Proceedings of computing in cardiology, Vol 38. Hangzhou, China, pp 701–704

  13. Doyle OM, Korotchikova I, Lightbody G, Marnane W, Kerins D, Boylan GB (2009) Heart rate variability during sleep in healthy term newborns in the early postnatal period. Physiol Meas 30(8):847–860

    Article  CAS  PubMed  Google Scholar 

  14. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874

    Article  Google Scholar 

  15. Ferriero DM (2004) Neonatal brain injury. N Engl J Med 351(19):1985–1995

    Article  CAS  PubMed  Google Scholar 

  16. Georgoulas G, Stylios D, Groumpos P (2006) Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines. IEEE Trans Biomed Eng 53(5):875–884

    Article  PubMed  Google Scholar 

  17. Ghoraani B, Krishnan S (2011) Time–frequency matrix feature extraction and classification of environmental audio signals. IEEE Trans Audio Speech Lang Process 19(7):2197–2209

    Article  Google Scholar 

  18. Hamilton EF, Warrick PA (2013) New perspectives in electronic fetal surveillance. J Perinat Med 41(1):83–92

    Article  PubMed  Google Scholar 

  19. Hussain ZM, Boashash B (2006) The T-class of time-frequency distributions: time-only kernels with amplitude estimation. J Frankl Inst 343(7):661–675

    Article  Google Scholar 

  20. Kim H, Park H (2008) Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J Matrix Anal Appl 30(2):713–730

    Article  Google Scholar 

  21. Krupa N, Ma MA, Zahedi E, Ahmed S, Hassan FM (2011) Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine. Biomed Eng Online 10:1–15

    Article  Google Scholar 

  22. Laar JV, Porath MM, Peters CHL, Oei SG (2008) Spectral analysis of fetal heart rate variability for fetal surveillance: review of the literature. Acta Obstet Gynecol Scand 87(3):300–306

    Article  PubMed  Google Scholar 

  23. Macones GA, Hankins GDV, Spong CY, Hauth J, Moore T (2008) The 2008 National Institute of Child Health and Human Development workshop report on electronic fetal monitoring: update on definitions, interpretation, and research guidelines. Obstet Gynecol 112(3):661–666

    Article  PubMed  Google Scholar 

  24. Monti A, Médigue C, Mangin L (2002) Instantaneous parameter estimation in cardiovascular time series by harmonic and time–frequency analysis. IEEE Trans Biomed Eng 49(12):1547–1556

    Article  PubMed  Google Scholar 

  25. Orini M, Bailón R, Enk R, Koelsch S, Mainardi L, Laguna P (2010) A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis. Med Biol Eng Comput 48(5):423–433

    Article  PubMed  Google Scholar 

  26. Rankine L, Mesbah M, Boashash B (2007) IF estimation for multicomponent signals using image processing techniques in the time–frequency domain. Signal Process 87(6):1234–1250

    Article  Google Scholar 

  27. Royal Australian New Zealand College of Obstetricians and Gynaecologists (2006) Intrapartum fetal surveillance: clinical guidelines, 2nd edn. RANZCOG, Melbourne

    Google Scholar 

  28. Salamalekis E, Thomopoulos P, Giannaris D, Salloum I, Vasios G, Prentza A, Koutsouris D (2002) Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks. BJOG 109(10):1137–1142

    Article  CAS  PubMed  Google Scholar 

  29. Sejdić E, Djurović I, Jiang J (2009) Time–frequency feature representation using energy concentration: an overview of recent advances. Digit Signal Process 19(1):153–183

    Article  Google Scholar 

  30. Spilka J, Chudáček V, Kouckỳ M, Lhotská L, Huptych M, Janků P, Georgoulas G, Stylios C (2012) Using nonlinear features for fetal heart rate classification. Biomed Signal Process Control 7(4):350–357

    Article  Google Scholar 

  31. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93(5):1043–1065

    Article  Google Scholar 

  32. Von Borell E, Langbein J, Després G, Hansen S, Leterrier C, Marchant-Forde J, Marchant-Forde R, Minero M, Mohr E, Prunier A, Valance D, Veissier I (2007) Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals—a review. Physiol Behav 92(3):293–316

    Article  Google Scholar 

  33. Warrick PA, Hamilton EF, Precup D, Kearney RE (2010) Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography. IEEE Trans Biomed Eng 57(4):771–779

    Article  PubMed  Google Scholar 

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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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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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