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Investigation of Machine Learning Techniques in Forecasting of Blood Pressure Time Series Data

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Artificial Intelligence XXXVI (SGAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11927))

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Abstract

The aim of this paper is to investigate different machine learning based forecasting techniques for forecasting of blood pressure and heart rate. Forecasting of blood pressure could potentially help a clinician to take preventative steps even before dangerous medical situations occur. This paper examines forecasting blood pressure 30 min in advance. Univariate and multivariate forecast models are considered. Different forecast strategies are also considered. To compare different forecast strategies, LSTM and BI-LSTM machine learning algorithms were included. Then univariate and multivariate LSTM, BI-LSTM and CNN machine learning algorithms were compared using the two best forecasting strategies. Comparative analysis between forecasting strategies suggest that MIMO and DIRMO forecast strategies provide the best accuracy in forecasting physiological time series data. Results also appear to show that multivariate forecast models for blood pressure and heart rate are more reliable compared to blood pressure alone. Comparative analysis between MIMO and DIRMO forecasting strategies appear to show that DIRMO is more reliable for both univariate and multivariate cases. Results also appear to show that the forecast model that uses BI-LSTM with the DIRMO strategy is the best overall.

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References

  1. Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., Inman, D.J.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)

    Article  Google Scholar 

  2. Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F.M., Preen, D.B., Stewart, L.M., Fatovich, D.M.: A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in western australia. J. Biomed. Inform. 57, 62–73 (2015)

    Article  Google Scholar 

  3. Ahmed, N.K., Atiya, A.F., Gayar, N.E., El-Shishiny, H.: An empirical comparison of machine learning models for time series forecasting. Econ. Rev. 29(5–6), 594–621 (2010)

    Article  MathSciNet  Google Scholar 

  4. Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25446-8_4

    Chapter  Google Scholar 

  5. Bassale, J.: Hypotension prediction arterial blood pressure variability. Technical report (2001)

    Google Scholar 

  6. Bengio, Y., Simard, P., Frasconi, P., et al.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

  7. Berardi, V.L., Zhang, G.P.: An empirical investigation of bias and variance in time series forecasting: modeling considerations and error evaluation. IEEE Trans. Neural Netw. 14(3), 668–679 (2003)

    Article  Google Scholar 

  8. Billis, A., Bamidis, P.D.: Employing time-series forecasting to historical medical data: an application towards early prognosis within elderly health monitoring environments. In: AI-AM/NetMed ECAI, pp. 31–35 (2014)

    Google Scholar 

  9. Dietterich, T.G.: Machine learning for sequential data: a review. In: Caelli, T., Amin, A., Duin, R.P.W., de Ridder, D., Kamel, M. (eds.) SSPR/SPR 2002. LNCS, vol. 2396, pp. 15–30. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-70659-3_2

    Chapter  Google Scholar 

  10. Du Preez, J., Witt, S.F.: Univariate versus multivariate time series forecasting: an application to international tourism demand. Int. J. Forecast. 19(3), 435–451 (2003)

    Article  Google Scholar 

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

    Google Scholar 

  12. Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep learning for solar power forecasting an approach using autoencoder and LSTM neural networks. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 002858–002865 (2016)

    Google Scholar 

  13. Hill, T., O’Connor, M., Remus, W.: Neural network models for time series forecasts. Manag. Sci. 42(7), 1082–1092 (1996)

    Article  Google Scholar 

  14. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  15. Ince, T., Kiranyaz, S., Eren, L., Askar, M., Gabbouj, M.: Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron. 63(11), 7067–7075 (2016)

    Article  Google Scholar 

  16. Jain, L.C., Medsker, L.R.: Recurrent Neural Networks: Design and Applications. CRC Press, Boca Raton (2000)

    Google Scholar 

  17. Janghorbani, A., Arasteh, A., Moradi, M.H.: Prediction of acute hypotension episodes using logistic regression model and support vector machine: a comparative study. In: 19th Iranian Conference on Electrical Engineering, pp. 1–4. IEEE (2011)

    Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015)

    Google Scholar 

  19. Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2016)

    Article  Google Scholar 

  20. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data (2001)

    Google Scholar 

  21. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  22. Lee, J., Mark, R.: A hypotensive episode predictor for intensive care based on heart rate and blood pressure time series. In: Computing in Cardiology, pp. 81–84. IEEE (2010)

    Google Scholar 

  23. Lee, J., Mark, R.G.: An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. Biomed. Eng. Online 9(1), 62 (2010)

    Article  Google Scholar 

  24. Li, X., Wu, S., Wang, L.: Blood pressure prediction via recurrent models with contextual layer. In: 26th International Conference on the World Wide Web, pp. 685–693 (2017)

    Google Scholar 

  25. Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint: arXiv:1506.00019 (2015)

  26. Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.C.: Learning to diagnose with LSTM recurrent neural networks. In: 4th International Conference on Learning Representations (2016)

    Google Scholar 

  27. Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)

    Article  Google Scholar 

  28. Mann, D.L., Zipes, D.P., Libby, P., Bonow, R.O.: Braunwald’s Heart Disease E-Book: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences, Philadelphia (2014)

    Google Scholar 

  29. Masum, S., Liu, Y., Chiverton, J.: Comparative analysis of the outcomes of differing time series forecasting strategies. In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 1964–1968. IEEE (2017)

    Google Scholar 

  30. Masum, S., Liu, Y., Chiverton, J.: Multi-step time series forecasting of electric load using machine learning models. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018, Part I. LNCS (LNAI), vol. 10841, pp. 148–159. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91253-0_15

    Chapter  Google Scholar 

  31. McKinney, W.: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc., Sebastopol (2012)

    Google Scholar 

  32. McLachlan, K., Jenkins, A., O’Neal, D.: The role of continuous glucose monitoring in clinical decision-making in diabetes in pregnancy. Aust. N. Z. J. Obstet. Gynaecol. 47(3), 186–190 (2007)

    Article  Google Scholar 

  33. Nguyen, P., Tran, T., Venkatesh, S.: Deep learning to attend to risk in ICU. In: 2nd International Workshop on Knowledge Discovery in Healthcare Data Co-located 26th International Joint Conference on Artificial Intelligence, pp. 25–29 (2017)

    Google Scholar 

  34. Ongenae, F., et al.: Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks. Eng. Appl. Artif. Intell. 26(3), 984–996 (2013)

    Article  Google Scholar 

  35. Rendle, S.: Factorization machines. In: IEEE International Conference on Data Mining, pp. 995–1000 (2010)

    Google Scholar 

  36. Rocha, T., Paredes, S., De Carvalho, P., Henriques, J.: Prediction of acute hypotensive episodes by means of neural network multi-models. Comput. Biol. Med. 41(10), 881–890 (2011)

    Article  Google Scholar 

  37. Saeed, M., Lieu, C., Raber, G., Mark, R.G.: MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring. In: Computers in Cardiology, pp. 641–644. IEEE (2002)

    Google Scholar 

  38. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  39. Sideris, C., Kalantarian, H., Nemati, E., Sarrafzadeh, M.: Building continuous arterial blood pressure prediction models using recurrent networks. In: IEEE International Conference on Smart Computing, pp. 1–5 (2016)

    Google Scholar 

  40. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  41. Su, P., Ding, X.R., Zhang, Y.T., Liu, J., Miao, F., Zhao, N.: Long-term blood pressure prediction with deep recurrent neural networks. In: IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 323–328 (2018)

    Google Scholar 

  42. Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067–7083 (2012)

    Article  Google Scholar 

  43. Vespa, P.M., Nenov, V., Nuwer, M.R.: Continuous EEG monitoring in the intensive care unit: early findings and clinical efficacy. J. Clin. Neurophysiol. 16(1), 1–13 (1999)

    Article  Google Scholar 

  44. Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: 54th Annual Meeting of the Association for Computational Linguistics. Short Papers, vol. 2, pp. 207–212 (2016)

    Google Scholar 

  45. Zhu, Y., Fan, X., Wu, J., Liu, X., Shi, J., Wang, C.: Predicting ICU mortality by supervised bidirectional LSTM networks. In: 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (2018)

    Google Scholar 

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Authors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Shamsul Masum, John P. Chiverton, Ying Liu & Branislav Vuksanovic

Authors
  1. Shamsul Masum
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  2. John P. Chiverton
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  3. Ying Liu
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  4. Branislav Vuksanovic
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Corresponding author

Correspondence to John P. Chiverton .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Masum, S., Chiverton, J.P., Liu, Y., Vuksanovic, B. (2019). Investigation of Machine Learning Techniques in Forecasting of Blood Pressure Time Series Data. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_21

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  • DOI: https://doi.org/10.1007/978-3-030-34885-4_21

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