Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Model of Vital Signs Analysis based on Big Data using OCL

OCL을 이용한 빅데이터 기반의 생체신호 분석 모델

  • Received : 2019.08.31
  • Accepted : 2019.09.09
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

As the type and size of vital signs become extensive at the moment lately, a research is actively progressing to define vital signs as big data and analyze it. We generally use a similar method of processing big data on social network as a way to treat vital signs as big data. Vital Sign Big Data should be extracted as feature data, stored separately, and analyzed with various analytical instruments. In other words, it should ensure interoperability and compatability of data, and the index expression in analytical tools should be concise. For this end, I defined the vital sign as the standard meta-model base of HL7 in this dissertation, and I propose a model for analyzing vital signs using OCL, the OMG's standard mathematical specification language. In addition, the proposed model can be confirmed the applicability by figuring out the consumption of calories using ECG data.

오늘날 생체신호의 종류 및 크기가 방대해짐에 따라 생체신호를 빅데이터로 정의하고 이를 분석하고자 하는 연구가 활발히 진행되고 있다. 하지만 생체신호를 빅데이터로 처리함에 있어 소셜 네트워크에서 발생하는 빅데이터 처리와 비슷한 방법을 이용하고 있다. 생체신호 빅데이터는 특징값을 추출하여 이를 별도로 저장하고 다양한 분석기기로 분석할 수 있어야 한다. 즉 데이터의 상호운용성과 호환성이 보장되어야 하며 분석도구에서의 지표 표현이 간결해야 한다. 이를 위하여 본 논문에서는 생체신호를 표준화된 HL7의 표준 메타모델기반으로 표현하고 OMG의 표준 수학적 명세 언어인 OCL을 이용하여 생체신호를 분석하는 모델을 제안한다. 또한 제안한 모델을 이용하여 심전도 데이터를 이용한 칼로리 소모량을 구해봄으로써 활용 가능성을 확인한다.

Keywords

References

  1. Y. Khan, A. E. Ostfeld, C. M. Lochner, A. Pierre, and A. C. Arias, "Monitoring of vital signs with flexible and wearable medical devices," Advanced Materials, vol. 28, issue 22, pp. 4373-4395, June 2016. https://doi.org/10.1002/adma.201504366
  2. A. Pantelopoulos, and N. G. Bourbakis, "A survey on wearable sensor-based systems for health monitoring and prognosis," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, issue 1, pp. 1-12, Jan. 2010. https://doi.org/10.1109/TSMCC.2009.2032660
  3. T. W. Kim, S. H. Yi, K. H. Park, and H. C. Kim, "A big data framework for u-Healthcare systems utilizing vital signs," in 2014 International Symposium Computer, Consumer and Control (IS3C) IEEE computer society, pp. 494-497, June 2014.
  4. W. Raghupathi, and V. Raghupathi, "Big data analytics in healthcare: promise and potential," Health Information Science and Systems, vol. 2, no. 3, pp. 1-10, Feb. 2014.
  5. J. Y. Choi, "Utilization value of medical Big Data created in operation of medical information system," Journal of the KIECS, vol. 10, no. 12, pp. 1403-1410, 2015.
  6. C. Vidaurre, T. H. Sander, and A. Schlogl, "BioSig: the free and open source software library for biomedical signal processing," in Computational intelligence and neuroscience, pp. 1-12, Mar. 2011.
  7. P. E. McSharry, and G. D. Cifford, "Open-source software for generating electrocardiogram signals," in arXiv preprint physics/0406017, 2004.
  8. M. I. Joo, D. H. Ko, and H. C. Kim, "Development of Smart Healthcare Wear System for Acquiring Vital Signs and Monitoring Personal Health," Journal of Korea Multimedia Society, vol. 19, no. 5, pp. 808-817, May 2016. https://doi.org/10.9717/kmms.2016.19.5.808
  9. M. Yuksel, and A. Dogac, "Interoperability of medical device information and the clinical applications: an HL7 RIM based on the ISO/IEEE 11073 DIM," in IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 4, pp. 557-566, July 2011. https://doi.org/10.1109/TITB.2011.2151868
  10. S. U. Heo, S. W. Choi, G. H. Kim, and A. S. Oh, "The Study on HL7 Message improvement for Supporting of Efficient Healthcare Service," Journal of the Korea Institute of Information and Communication Engineering, vol. 18, no. 6, pp. 1388-1394, June 2014. https://doi.org/10.6109/jkiice.2014.18.6.1388
  11. P. Ziemann, and M. Gogolla, "Validating OCL specifications with the use tool an example based on the BART case study," Electronic Notes in Theoretical Computer Science, vol. 80, pp. 157-169, Aug. 2003. https://doi.org/10.1016/S1571-0661(04)80816-8
  12. Apache Group. Apache Pig. Project [Internet]. Available: https://pig.apache.org/.
  13. Apache Group. Apache Drill Schema-free SQL Query Engine for Hadoop, No-SQL and Cloud Storage [Internet]. Available: https://drill.apache.org/.
  14. Apache Group. Apache Impala is the open source, native analytic database for Apache Hadoop[Internet]. Available: https://impala.apache.org/.
  15. Eclipse Platform. Parsing OCL Documents[Internet]. Available:https://help.eclipse.org/kepler/index.jsp?topic=%2Forg.eclipse.ocl.doc%2Fhelp%2FParsingDocuments.html.
  16. Eclipse Platform. Working with the Classic OCL, Parsing OCL Expressions[Internet]. Available: https://help.eclipse.org/luna/topic/org.eclipse.ocl.doc/help/OCLInterpreterTutorial.html.