A review of big data applications of physiological signal data

  • Christina OrphanidouEmail author


The proliferation of smart physiological signal monitoring sensors, combined with the advancement of telemetry and intelligent communication systems, has led to an explosion in healthcare data in the past few years. Additionally, access to cheaper and more effective power and storage mechanisms has significantly increased the availability of healthcare data for the development of big data applications. Big data applications in healthcare are concerned with the analysis of datasets which are too big, too fast, and too complex for healthcare providers to process and interpret with existing tools. The driver for the development of such systems is the continuing effort in making healthcare services more efficient and sustainable. In this paper, we provide a review of current big data applications which utilize physiological waveforms or derived measurements in order to provide medical decision support, often in real time, in the clinical and home environment. We focus mainly on systems developed for continuous patient monitoring in critical care and discuss the challenges that need to be overcome such that these systems can be incorporated into clinical practice. Once these challenges are overcome, big data systems have the potential to transform healthcare management in the hospital of the future.


Physiological signal data Smart sensors Healthcare data Medical decision support Big data 


Compliance with ethical standards

Conflict of interest

Christina Orphanidou declares that she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by the author.


  1. Almeida JP (2016) A disruptive big data approach to leverage the efficiency in management and clinical decision support in a hospital. Porto Biomed J 1(1):40–42CrossRefGoogle Scholar
  2. Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang GZ (2015) Big data for health. IEEE J Biomed Health Inf 19(4):1193–1208CrossRefGoogle Scholar
  3. Attin M, Feld G, Lemus H et al (2015) Electrocardiogram characteristics prior to in-hospital cardiac arrest. J Clin Monit Comput 29(3):385–392CrossRefGoogle Scholar
  4. Belle A, Thiagarajan A, Reza SM et al (2015) Big data analytics in healthcare. Biomed Res Int 370194:16Google Scholar
  5. Bressan N, James A, McGregor C (2012) Trends and opportunities for integrated real time neonatal clinical decision support. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ‘12), pp 687–690Google Scholar
  6. Cao H, Eshelman L, Chbat N, Nielsen L, Gross B, Saeed M(2008) Predicting ICU hemodynamic instability using continuous multiparameter trends, EMBC ’08, pp 3803–3806Google Scholar
  7. Churpek MM, Yuen TC, Winslow C et al (2014) Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med 190(6):649–655CrossRefGoogle Scholar
  8. Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP (2016) Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 44(2):368–374CrossRefGoogle Scholar
  9. Clifton L, Clifton DA, Pimentel MAF, Watkinson PJ, Tarassenko L (2014) Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors. IEEE J Biomed Health Inf 18(3):722–730CrossRefGoogle Scholar
  10. Dunitz M, Verghese G, Heldt T (2015) Predicting hyperlactatemia in the MIMIC II database. Proc. EMBC ’15, pp 985–988Google Scholar
  11. Dürichen R, Pimentel MAF, Clifton L, Schweikard A, Clifton DA (2015) Multitask Gaussian processes for multivariate physiological time-series analysis. IEEE Trans Biomed Eng 62(1):314–322CrossRefGoogle Scholar
  12. Durrant-Whyte H, Henderson TC (2008). Multisensor data fusion. Springer Handbook of Robotics, pp 585–610Google Scholar
  13. Futoma J, Morris J, Lucas J (2015) A comparison of models for predicting early hospital readmissions. J Biomed Inform 56:229–238CrossRefGoogle Scholar
  14. Ghassemi M, Pimentel MA, Naumann T, et al. (2015) A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse. Heterogeneous clinical data. Proc Conf AAAI Artif Intell 2015:446–453Google Scholar
  15. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefGoogle Scholar
  16. Güiza F, Depreitere B, Piper I, Van den Berghe G, Meyfroidt G (2013) Novel methods to predict increased intracranial pressure during intensive care and long-term neurologic outcome after traumatic brain injury: development and validation in a multicenter dataset. Crit Care 41(2):554–564CrossRefGoogle Scholar
  17. Han H, Ryoo HC, Patrick H (2006) An infrastructure of stream data mining, fusion and management for monitored patients. In Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS ‘06), pp 461–468Google Scholar
  18. Hood L, Flores M (2012) A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnol 29(6):613–624CrossRefGoogle Scholar
  19. Hu M, Chen Y, Kwok JT (2009) Building sparse multiple-kernel SVM classifiers. IEEE Trans Neural Netw 20(5):827–839CrossRefGoogle Scholar
  20. Johnson AEW, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) MIMIC-III, a freely accessible critical care database. Scientific DataGoogle Scholar
  21. Joshi R, Szolovits P (2012) Prognostic physiology: modeling patient severity in intensive care units using radial domain folding. Paper presented at: AMIA Annual Symposium ProceedingsGoogle Scholar
  22. Klann JG, Anand V, Downs SM (2013) Patient tailored prioritization for a pediatric care decision support system through machine learning. J Am Med Inform Assoc 20(e2):e267–e274CrossRefGoogle Scholar
  23. Krumholz HM (2014) Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff 33(7):1163–1170CrossRefGoogle Scholar
  24. Lee J, Mark RG (2010) A hypotensive episode predictor for intensive care based on heart rate and blood pressure time series. IEEE Comput Cardiol 81–84Google Scholar
  25. Leff DR, Yang G-Z (2015) Big data for precision medicine, engineering. 1(3):277–279Google Scholar
  26. Nair BG, Newman SF, Peterson GN, Wu WY, Schwid HA (2010) Feedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg 111(5):1293–1300CrossRefGoogle Scholar
  27. Orphanidou C (2018) Signal quality assessment in physiological monitoring: state of the art and practical considerations. Springer, ChamCrossRefGoogle Scholar
  28. Orphanidou C, Wong D (2017) Machine learning models for multidimensional clinical data. In: Khan SU, Zomaya AY, Assad A (eds) Handbook of large-scale distributed computing in smart healthcare, scalable computing and communications. Springer, Cham, pp 177–216Google Scholar
  29. Orphanidou C, Bonnici T, Charlton P, Clifton D, Valance D, Tarassenko L (2015) Signal-quality indices for the electrocardiogram and photoplethysmogram: Derivation and applications to wireless monitoring. IEEE J Biomed Health Inform 19(3):832–838PubMedGoogle Scholar
  30. Palanisamy V, Thirunavukarasu R (2017) Implications of big data analytics in developing healthcare frameworks – a review. J King Saud Univ Comput Inf SciGoogle Scholar
  31. Pimentel MAF, Clifton DA, Clifton L, Watkinson PJ, Tarassenko L (2013) Modelling physiological deterioration in post-operative patient vital-sign data. Med Biol Eng Comput 51:869–877CrossRefGoogle Scholar
  32. Pimentel MAF, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Signal Process 99:215–249CrossRefGoogle Scholar
  33. Pimentel MAF et al (2016) Outcome prediction for patients with traumatic brain injury with dynamic features from intracranial pressure and arterial blood pressure signals: a Gaussian process approach. Acta Neurochir Suppl 122:85–91CrossRefGoogle Scholar
  34. Pirracchio R, Petersen ML, Carone M, Rigon MR, Chevret S, van der Laan MJ (2014) Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med 3(1):42–52CrossRefGoogle Scholar
  35. Roederer A, Weimer J, DiMartino J, Gutsche J, Lee I (2015) Robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals. Proc. EMBC ‘15, pp 1504–1507Google Scholar
  36. Roski J, Bo-Linn GW, Andrews TA (2014) Creating value in health care through big data: opportunities and policy implications. Health Aff 33(7):1115–1122CrossRefGoogle Scholar
  37. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517CrossRefGoogle Scholar
  38. Sanchez-Pinto LN, Luo Y, Churpek MM (2018) Big data and data science in critical care. Chest 154(5):1239–1248CrossRefGoogle Scholar
  39. Sow D, Turaga DS, Schmidt M (2013) Mining of sensor data in healthcare: a survey. Managing and Mining Sensor Data, pp 459–504Google Scholar
  40. Sun J, Sow D, Hu J, Ebadollahi S(2010) A system for mining temporal physiological data streams for advanced prognostic decision support, in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM’ 10), pp 1061–1066Google Scholar
  41. Tarassenko L, Hann A, Young D (2006) Integrated monitoring and analysis for early warning of patient deterioration. Br J Anaesth 97(1):64–68CrossRefGoogle Scholar
  42. Tarassenko L, Villarroel M, Guazzi A, Jorge J, Clifton DA, Pugh C (2014) Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiol Meas 35(5):807–831CrossRefGoogle Scholar
  43. Wagholikar KB et al (2012) Clinical decision support with automated text processing for cervical cancer screening. J Am Med Inform Assoc 19(5):833–839CrossRefGoogle Scholar
  44. Wilson SJ, Wong D, Pullinger RM, Way R, Clifton DA, Tarassenko L (2016) Analysis of a data-fusion system for continuous vital sign monitoring in an emergency department. Eur J Emerg Med 23(1):28–32CrossRefGoogle Scholar

Copyright information

© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Oxygen Research LtdLimassolCyprus

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