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Early Detection of Sepsis Induced Deterioration Using Machine Learning

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Artificial Intelligence (BNAIC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1021))

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Abstract

Sepsis is an excessive bodily reaction to an infection in the bloodstream, which causes one in five patients to deteriorate within two days after admission to the hospital. Until now, no clear tool for early detection of sepsis induced deterioration has been found. This research uses electrocardiograph (ECG), respiratory rate, and blood oxygen saturation continuous bio-signals collected from 132 patients from the University Medical Center of Groningen during the first 48 h after hospital admission. This data is examined under a range of feature extraction strategies and Machine Learning techniques as an exploratory framework to find the most promising methods for early detection of sepsis induced deterioration. The analysis includes the use of Gradient Boosting Machines, Random Forests, Linear Support Vector Machines, Multi-Layer Perceptrons, Naive Bayes Classifiers, and k-Nearest Neighbors classifiers. The most promising results were obtained using Linear Support Vector Machines trained on features extracted from single heart beats using the wavelet transform and autoregressive modelling, where the classification occurred as a majority vote of the heart beats over multiple long ECG segments.

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References

  1. Singer, M., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8), 801–810 (2016). 26903338[pmid]

    Article  Google Scholar 

  2. Bone, R.C., Fisher, C.J., Clemmer, T.P., Slotman, G.J., Metz, C.A., Balk, R.A.: Sepsis syndrome: a valid clinical entity. Methylprednisolone severe sepsis study group. Crit. Care Med. 17(5), 389–393 (1989)

    Article  Google Scholar 

  3. Buchan, C.A., Bravi, A., Seely, A.J.E.: Variability analysis and the diagnosis, management, and treatment of sepsis. Curr. Infect. Dis. Rep. 14(5), 512–521 (2012)

    Article  Google Scholar 

  4. Danai, P., Martin, G.S.: Epidemiology of sepsis: recent advances. Curr. Infect. Dis. Rep. 7(5), 329–334 (2005)

    Article  Google Scholar 

  5. Glickman, S.W., et al.: Disease progression in hemodynamically stable patients presenting to the emergency department with sepsis. Acad. Emerg. Med. 17(4), 383–390 (2010)

    Article  MathSciNet  Google Scholar 

  6. Brindley, P.G., Zhu, N., Sligl, W.: Best evidence in critical care medicine early antibiotics and survival from septic shock: it’s about time. Can. J. Anesth./Journal canadien d’anesthésie 53(11), 1157–1160 (2006)

    Article  Google Scholar 

  7. Dellinger, R.P., et al.: Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock 2012. Crit. Care Med. 41(2), 580–637 (2013)

    Article  Google Scholar 

  8. Moorman, J.R., et al.: Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial. J. Pediatrics 159(6), 900–906.e1 (2011)

    Article  Google Scholar 

  9. Ahmad, S., et al.: Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS ONE 4(8), 1–10 (2009)

    Article  Google Scholar 

  10. Bravi, A., Green, G., Longtin, A., Seely, A.J.E.: Monitoring and identification of sepsis development through a composite measure of heart rate variability. PLoS ONE 7(9), e45666 (2012). PONE-D-12-18432[PII]

    Article  Google Scholar 

  11. Quinten, V.M., van Meurs, M., Renes, M.H., Ligtenberg, J.J.M., ter Maaten, J.C.: Protocol of the SepsiVit study: a prospective observational study to determine whether continuous heart rate variability measurement during the first 48 hours of hospitalisation provides an early warning for deterioration in patients presenting with infec. BMJ Open 7(11), e018259 (2017)

    Article  Google Scholar 

  12. Zhao, Q., Zhang, L.: ECG feature extraction and classification using wavelet transform and support vector machines. In: 2005 International Conference on Neural Networks and Brain, vol. 2, pp. 1089–1092, October 2005

    Google Scholar 

  13. Levy, M.M., et al.: 2001 SCCM/ESICM/ACCP/ATS/SIS international sepsis definitions conference. Crit. Care Med. 31(4), 1250–1256 (2003)

    Article  Google Scholar 

  14. Cardoso, J.F., Laheld, B.H.: Equivariant adaptive source separation. IEEE Trans. Signal Process. 44(12), 3017–3030 (1996)

    Article  Google Scholar 

  15. Peltola, M.: Role of editing of R-R intervals in the analysis of heart rate variability. Front. Physiol. 3, 148 (2012)

    Article  Google Scholar 

  16. Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017). 29034226[pmid]

    Article  Google Scholar 

  17. Moridani, M.K., Setarehdan, S.K., Nasrabadi, A.M., Hajinasrollah, E.: Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient. J. Med. Eng. Technol. 40(3), 87–98 (2016). PMID: 27028609

    Article  Google Scholar 

  18. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005

    Google Scholar 

  19. Hamilton, P.: Open source ECG analysis. Comput. Cardiol. 29, 101–104 (2002)

    Article  Google Scholar 

  20. Kadambe, S., Murray, R., Boudreaux-Bartels, G.F.: Wavelet transform-based QRS complex detector. IEEE Trans. Biomed. Eng. 46(7), 838–848 (1999)

    Article  Google Scholar 

  21. Morlet, J., Arens, G., Fourgeau, E., Glard, D.: Wave propagation and sampling theory - Part i: complex signal and scattering in multilayered media. Geophysics 47(2), 203–221 (1982)

    Article  Google Scholar 

  22. Grossmann, A.: Wavelet transforms and edge detection. In: Albeverio, S., Blanchard, P., Hazewinkel, M., Streit, L. (eds.) Stochastic Processes in Physics and Engineering, pp. 149–157. Springer, Dordrecht (1988). https://doi.org/10.1007/978-94-009-2893-0_7

    Chapter  Google Scholar 

  23. Lee, G., et al.: Pywavelets - wavelet transforms in Python (2006). Accessed 2018

    Google Scholar 

  24. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Parzen, E., Tanabe, K., Kitagawa, G. (eds.) Selected Papers of Hirotugu Akaike, pp. 199–213. Springer, New York (1998). https://doi.org/10.1007/978-1-4612-1694-0_15

    Chapter  Google Scholar 

  25. Jolliffe, I.: Principal component analysis. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science, pp. 1094–1096. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2

    Chapter  Google Scholar 

  26. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  27. Li, Q., Rajagopalan, C., Clifford, G.D.: Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans. Biomed. Eng. 61(6), 1607–1613 (2014)

    Article  Google Scholar 

  28. Song, M.H., Lee, J., Cho, S.P., Lee, K.J., Yoo, S.K.: Support vector machine based arrhythmia classification using reduced features. Int. J. Control Autom. Syst. 3(4), 571–579 (2005)

    Google Scholar 

  29. Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Schölkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

  30. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  31. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7

    Book  MATH  Google Scholar 

  32. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  33. Hechenbichler, K., Schliep, K.: Weighted k-nearest-neighbor techniques and ordinal classification (2004). Accessed 2018

    Google Scholar 

  34. Kriesel, D.: A brief introduction to neural networks (2007)

    Google Scholar 

  35. Soman, T., Bobbie, P.O.: Classification of arrhythmia using machine learning techniques. WSEAS Trans. Comput. 4, 548–552 (2005)

    Google Scholar 

  36. Chan, T.F., Golub, G.H., LeVeque, R.J.: Updating formulae and a pairwise algorithm for computing sample variances. In: Caussinus, H., Ettinger, P., Tomassone, R. (eds.) COMPSTAT 1982 5th Symposium Held at Toulouse 1982, pp. 30–41. Physica-Verlag, Heidelberg (1982)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Groningen, 9700 AB, Groningen, The Netherlands

    Francesco Dal Canton, Vincent M. Quinten & Marco A. Wiering

  2. University Medical Center Groningen, 9713 GZ, Groningen, The Netherlands

    Vincent M. Quinten

Authors
  1. Francesco Dal Canton
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  2. Vincent M. Quinten
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  3. Marco A. Wiering
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Corresponding author

Correspondence to Marco A. Wiering .

Editor information

Editors and Affiliations

  1. Tilburg University, Tilburg, The Netherlands

    Martin Atzmueller

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Wouter Duivesteijn

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Dal Canton, F., Quinten, V.M., Wiering, M.A. (2019). Early Detection of Sepsis Induced Deterioration Using Machine Learning. In: Atzmueller, M., Duivesteijn, W. (eds) Artificial Intelligence. BNAIC 2018. Communications in Computer and Information Science, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-31978-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-31978-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31977-9

  • Online ISBN: 978-3-030-31978-6

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