Skip to main content
SpringerLink
Log in

Title Cardiovascular Big Data Analytics

  • Chapter
  • First Online:
Cardiovascular Computing—Methodologies and Clinical Applications

Part of the book series: Series in BioEngineering ((SERBIOENG))

  • 789 Accesses

Abstract

The evolving pattern towards precision medicine is based on analytics at the—omics, physiology and eHealth domains. One of the main driving forces is the management and analytics of multi-level multi-scale and multi-source data containing different semantics and possibilities for the realization of personalized health through the precision medicine approach. In the case of cardiovascular big data analytics progress is evident especially at the levels of physiology and eHealth. Specifically there is progress in the ICU domain, where streaming data are the big data source comprising a paradigm for point-of-care (PoC) diagnostics helping the clinical decision support system (CDSS) for physiological assessment of the cardiorespiratory system, as well as in the eHealth domain through the uptake of advanced closed loop telemonitoring systems addressing connected health and coordinated care for multimorbid chronic disease patients. The quantification of specific data analytics, data management and computational needs for the realization of interfaces conveying from cloud to local level the necessary input to the medical users is a complex procedure, which calls for the optimal use of all the cloud, computing and machine learning and visualization tools, let alone the interoperability and data security standards and apps. In this chapter, we shall give an overview of the current state-of-the-art concerning cardiovascular big data analytics as a function of available information and communication technology, computational engineering, and user driven systems platforms for use in connected health and integrated care environments, as well as one basic example related to big data analytics as a result of streaming data at a PoC from a clinically controlled environment such as the Intensive Care Unit (ICU). The output from the application of advanced machine learning and multiparametric analytics methodology conveyed as a CDSS to the users is considered and viewed as perhaps the most important function for the success of the big data analytics and management platforms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://hadoop.apache.org/.

  2. 2.

    http://spark.apache.org/.

  3. 3.

    https://www.openice.info/index.html.

  4. 4.

    http://www.aegle-uhealth.eu/en/.

  5. 5.

    https://loinc.org/.

References

  1. Viceconti M, Hunter P, Hose R (2015) Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health Informat. 19(4):1209–1215

    Article  Google Scholar 

  2. Martin-Sanchez F, Verspoor K (2014) Big data in medicine is driving big changes. Yearbook Med Informat 9(1):14–20

    Google Scholar 

  3. Andreu-Perez J et al (2015) Big Data for Health. IEEE Journal of Biomedical and Health Informatics 19(4):1193–1208

    Article  Google Scholar 

  4. Rumsfeld JS, Joynt KE, Maddox TM (2016) Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 13(6):350–359. http://www.ncbi.nlm.nih.gov/pubmed/27009423

    Article  Google Scholar 

  5. Hu H et al (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687

    Article  Google Scholar 

  6. Kokkinaki A, Chouvarda I, Maglaveras N (2012) Searching biosignal databases by content and context: research oriented integration system for ECG signals (ROISES). Comput Methods Prog Biomed 108(2):453–466

    Article  Google Scholar 

  7. Natter MD et al (2013) An i2b2-based, generalizable, open source, self-scaling chronic disease registry. J Am Med Informat Assoc 20(1):172–179. http://jamia.oxfordjournals.org/content/20/1/172

    Article  Google Scholar 

  8. Segagni D et al (2012) CARDIO-i2b2: integrating arrhythmogenic disease data in i2b2. Stud Health Technol Informat 1126–1128

    Google Scholar 

  9. Gabetta M et al (2015) BigQ: a NoSQL based framework to handle genomic variants in i2b2. BMC Bioinformat 16(1):415. http://www.biomedcentral.com/1471-2105/16/415

  10. Krishnan K (2013) Data warehousing in the age of big data

    Chapter  Google Scholar 

  11. Dean J, Ghemawat S (2010) MapReduce. Commun ACM 53(1):72

    Article  Google Scholar 

  12. Lam MPY et al (2016) Cardiovascular proteomics in the era of big data: experimental and computational advances. Clin Proteom 13:23

    Article  Google Scholar 

  13. Lindsey ML et al (2015) Transformative impact of proteomics on cardiovascular health and disease: a scientific statement from the American heart association. Circulation 132(9):852–872

    Article  MathSciNet  Google Scholar 

  14. Goloborodko AA et al (2013) Pyteomics—a python framework for exploratory data analysis and rapid software prototyping in proteomics. J Am Soc Mass Spectr 24(2):301–304

    Article  Google Scholar 

  15. Haberman ZC et al (2015) Wireless smartphone ECG enables large-scale screening in diverse populations. J Cardiovasc Electrophysiol 26(5):520–526

    Article  Google Scholar 

  16. Hsieh JC, Li AH, Yang CC (2013) Mobile, cloud, and big data computing: Contributions, challenges, and new directions in telecardiology. Int J Environ Res Public Health 10(11):6131–6153

    Article  Google Scholar 

  17. Johnson AEW et al (2016) MIMIC-III, a freely accessible critical care database. Scientif Data 3:160035

    Article  Google Scholar 

  18. Lee J, Ribey E, Wallace JR (2016) A web-based data visualization tool for the MIMIC-II database. BMC Med Informat Decision Making 16:15. http://www.ncbi.nlm.nih.gov/pubmed/26846781%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4743095

  19. Lubitz SA, Ellinor PT (2015) Next-generation sequencing for the diagnosis of cardiac arrhythmia syndromes. Heart Rhythm Official J Heart Rhythm Soc 12(5):1062–1070. http://www.sciencedirect.com/science/article/pii/S1547527115000405

    Article  Google Scholar 

  20. McGregor C (2013) Big Data in neonatal intensive care. Computer 46(6):54–59

    Article  Google Scholar 

  21. Nath C, Albaghdadi MS, Jonnalagadda SR (2016) A natural language processing tool for large-scale data extraction from echocardiography reports. PLoS One 11(4)

    Article  Google Scholar 

  22. Nouhravesh N et al (2016) Analyses of more than 60,000 exomes questions the role of numerous genes previously associated with dilated cardiomyopathy. Mol Genetic Genom Med 4(6):617–623

    Article  Google Scholar 

  23. Saeed M et al (2011) Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Crit Care Med 39(5):952–960

    Article  Google Scholar 

  24. Sahoo SS et al (2014) Heart beats in the cloud: distributed analysis of electrophysiological “Big Data” using cloud computing for epilepsy clinical research. J Am Med Informat Assoc JAMIA 21(2):263–271. http://jamia.oxfordjournals.org/content/21/2/263.abstract

    Article  Google Scholar 

  25. Sahoo SS et al (2016) NeuroPigPen: a scalable toolkit for processing electrophysiological signal data in neuroscience applications using apache pig. Front Neuroinformat 10:18. http://journal.frontiersin.org/article/10.3389/fninf.2016.00018

  26. Sheynkman GM et al (2014) Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations. BMC Genom 15(1):703. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4158061&tool=pmcentrez&rendertype=abstract

  27. Bernabeu MO et al (2009) CHASTE: incorporating a novel multi-scale spatial and temporal algorithm into a large-scale open source library. Philos Trans Ser A Math Phys Eng Sci 367(1895):1907–30. http://www.ncbi.nlm.nih.gov/pubmed/19380318

  28. Cooper J, Scharm M, Mirams GR (2016) The cardiac electrophysiology web lab. Biophys J 110(2):292–300

    Article  Google Scholar 

  29. Soulakis ND et al (2015) Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J Am Med Inform Assoc 22(2):299–311

    Article  Google Scholar 

  30. Chouvarda I et al (2016) Clinical flows and decision support systems for coordinated and integrated care in COPD. In: 3rd IEEE EMBS international conference on biomedical and health informatics, BHI, pp 477–480

    Google Scholar 

  31. Beredimas N et al (2015) A reusable ontology for primitive and complex HL7 FHIR data types. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2547–2550. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7318911. Accessed 11 Dec 2015

  32. Bruining N et al (2014) Acquisition and analysis of cardiovascular signals on smartphones: potential, pitfalls and perspectives by the task force of the e-Cardiology working group of European society of cardiology. European J Prevent Cardiol 21(2 suppl):4–13. http://cpr.sagepub.com/content/21/2_suppl/4%5Cnhttp://cpr.sagepub.com/content/21/2_suppl/4.full.pdf%5Cnhttp://cpr.sagepub.com/content/21/2_suppl/4.short%5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/25354948

    Article  Google Scholar 

  33. Agneeswaran VS et al (2013) Real-time analytics for the healthcare industry: arrhythmia detection. Big Data 1(3) 176–182. http://online.liebertpub.com/doi/abs/10.1089/big.2013.0018

    Article  Google Scholar 

  34. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, SH (2000) PhysioBank, PhysioToolkit, and PhysioNet components of a new research resource for complex physiologic signals. Circulation 101(23):215–220

    Google Scholar 

  35. Blount M et al (2010) Real-time analysis for intensive care: development and deployment of the artemis analytic system. IEEE Eng Med Biol Mag Quarter Mag Eng Med Biol Soc 29(2):110–118. http://www.ncbi.nlm.nih.gov/pubmed/20659848

    Article  Google Scholar 

  36. Arney D et al (2012) Simulation of medical device network performance and requirements for an integrated clinical environment. Biomedical Instrumentation & Technology/Association for the Advancement of Medical Instrumentation 46(4). https://doi.org/10.2345/0899-8205-46.4.308. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810397/

    Article  Google Scholar 

  37. Chytas A et al (2016). Ineffective efforts in ICU assisted ventilation: feature extraction and analysis platform. In: Iliadis L, Maglogiannis I (eds.) Artificial intelligence applications and innovations: 12th IFIP WG 12.5 international conference and workshops, AIAI 2016, Thessaloniki, Greece, September 16–18, 2016, Proceedings. Cham: Springer International Publishing, pp. 642–650. http://dx.doi.org/10.1007/978-3-319-44944-9_57

    Google Scholar 

  38. Vaporidi K et al (2016) Clusters of ineffective efforts during mechanical ventilation: impact on outcome. Intensiv Care Med 1–8. http://dx.doi.org/10.1007/s00134-016-4593-z

Download references

Acknowledgements

This work was supported in part by the EC projects AEGLE (Grant agreement 644906) and WELCOME (611223).

Author information

Authors and Affiliations

  1. Laboratory of Computing, Medical Informatics & Biomedical-Imaging Technologies, The Medical School, Aristotle University, Thessaloniki, Greece

    Ioanna Chouvarda & Nicos Maglaveras

  2. Deptartment of Industrial Engineering & Management Sciences, Department of Electrical Engineering & Computer Science, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA

    Nicos Maglaveras

  3. Center for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece

    Ioanna Chouvarda & Nicos Maglaveras

Authors
  1. Ioanna Chouvarda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicos Maglaveras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ioanna Chouvarda or Nicos Maglaveras .

Editor information

Editors and Affiliations

  1. Medical School, National Kapodistrian University of Athens, Athens, Greece

    Spyretta Golemati

  2. Biomedical Simulations and Imaging Laboratory, National Technical University of Athens, Athens, Greece

    Konstantina S. Nikita

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chouvarda, I., Maglaveras, N. (2019). Title Cardiovascular Big Data Analytics. In: Golemati, S., Nikita, K. (eds) Cardiovascular Computing—Methodologies and Clinical Applications. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-5092-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-5092-3_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5091-6

  • Online ISBN: 978-981-10-5092-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation