Pattern Characterization in Multivariate Data Series using Fuzzy Logic - Applications to e-Health

W. Fajardo, M. Molina-Solana, M. C. Valenza

Abstract

The application of classic models to represent and analyze time-series imposes strict restrictions to the data that do not usually fit well with real-case scenarios. This limitation is mainly due to the assumption that data are precise, not noisy. Therefore, classic models propose a preprocessing stage for noise removal and data conversion. However, there are real applications where this data preprocessing stage dramatically lowers the accuracy of the results, since these data being filtering out are of great relevance. In the case of the real problem we propose in this research, the diagnosis of cardiopulmonary pathologies by means of fitness tests, detailed fluctuations in the data (usually filtered out by preprocessing methods) are key components for characterizing a pathology. We plan to model time-series data from fitness tests in order to characterize more precise and complete patterns than those being currently used for the diagnosis of cardiopulmonary pathologies. We will develop similarity measures and clustering algorithms for the automatic identification of novel, refined, types of diagnoses; classification algorithms for the automatic assignment of a diagnosis to a given test result.

References

  1. Box, G. E.; Jenkins, G. M. and Reinsel, G. C. (eds). 2008. Time Series Analysis: Forecasting and Control, 4th edn. Published by John Wiley and Sons, New York.
  2. Cerrito, P.; Cerrito, J. 2010. Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis. IGI Global.
  3. Delgado, M; Fajardo, W.; Molina-Solana, M. 2009. Inmamusys: Intelligent multiagent music system, Expert Systems with Applications, 36(3), pp. 4574- 4580, ISSN 0957-4174
  4. Delgado, M; Fajardo, W.; Molina-Solana, M. 2011. A state of the art on computational music performance, Expert Systems with Applications, 38(1), pp. 155- 160, ISSN 0957-4174
  5. Dorffner, G. 1999. Neural Networks for Time Series Processing" Tech. Rep., Medical Cybernetics and A.I., Univ. Viena, Viena.
  6. Fu T C. 2011. A review on time-series data mining. Engineering Applications of Artificial Intelligence; 24(1); 164-181
  7. García, F.; López, F J.; Cano, C.; Blanco, A. 2009. FISim: a new similarity measure between transcription factor binding sites based on the fuzzy integral. BMC Bioinformatics. 10(224).
  8. García, F.; Blanco, A.; Shepherd, J. 2010. An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs. BMC Bioinformatics. 11(551) pp. 1-13.
  9. Garbancho, A.G. Estadística Elemental Moderna. 16. 1994. Economía.
  10. Han, J.; Kamber, M; Pei, J. 2011. DataMining Concepts and Techniques. Morgan Kaufmann.
  11. Jiménez, A; Molina-Solana, M.; Berzal, F.; Fajardo, W. 2009. Mining transposed motifs in music. Journal of Intelligence Information Systems, 36 (1), pp. 99-115.
  12. Korhonen, I., Parkka, J. 2003. Health monitoring in the home of the future Engineering in Medicine.
  13. Lamba, J; Simpson, C. S.; Redfearn, D. P.; Michael, K. A.; Fitzpatrick, M.; Baranchuk, A. 2011. Cardiac Resynchronization Therapy for the Treatment of Sleep Apnea: A Meta-analysis. Europace (in press)
  14. Warren Liao T. 2005. Clustering of time-series data - a survey. Pattern Recognition; 38, 1857-1874
  15. López, J.; Blanco, A.; García, F.; Cano, C.; Marín, A. 2008. Extracting Knowledge from Heterogenous Data Biological by Fuzzy Association Rules. BMC Bioinformatics. 9 (107) pp. 1-18.
  16. Lötjönen, J., Korhonen, I., Hirvonen, K., Eskelinen, S. 2003. Automatic sleep-wake and nap analysis with a new wrist worn online activity monitoring device Vivago Wristcare - Sleep.
  17. Martin, T., Jovanov E. 2000. Issues in wearable computing for medical monitoring applications: a case study of a wearable ECG monitoring device. Wearable Computers.
  18. Mirikitani, D. T.; Nikolaev, N. 2010. "Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling," Neural Networks, IEEE Transactions on, 21 (2), pp.262-274.
  19. Motro, A. 1996. Sources of uncertainty, imprecision, and incosistency in information systems. Vol. Uncertainty Management in Information Systems: From Needs to Solutions, in Uncertainty Management in Information Systems: From Needs to Solutions., edited by A. and Smets, P. Motro, 9-34. Kluwer Academic Publishers,
  20. Nistico, A; Iliescu, E. A.; Fitzpatrick, M. F; White C. A.. 2010. Polycythemia due to obstructive sleep apnea in a patient on hemodialysis. Hemodialysis International. 14: 333-6. Wiley
  21. Perez Riera, A. R.; Ferreira, M.; Hopman, W. M.; McIntyre, W. F.; Baranchuk, A. 2011. Electrovectorcardiographic Characterization of The Type-1 Brugada ECG Pattern. Europace; P1214: 139.
  22. Pyle, D. 1999. Data Preparation for Data Mining. San Francisco: Morgan Kaufmann Publishers Inc..
  23. Valenza-Peña, M. C. 2010. Patterns and attitudes of spanish Healt students who smoke European Repiratory Journal. pp. 780-781. ISBN 1399-3003.
  24. Valenza-Peña, M. C. 2011. Analysis of the main sleeprespiratory parameters in patients with fibromyalgia. European Respiratory Journal. pp. 805-806. ISBN 1399-3003.
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Paper Citation


in Harvard Style

Fajardo W., Molina-Solana M. and C. Valenza M. (2012). Pattern Characterization in Multivariate Data Series using Fuzzy Logic - Applications to e-Health . In Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems - Volume 1: ICE-B, (ICETE 2012) ISBN 978-989-8565-23-5, pages 123-128. DOI: 10.5220/0004124701230128


in Bibtex Style

@conference{ice-b12,
author={W. Fajardo and M. Molina-Solana and M. C. Valenza},
title={Pattern Characterization in Multivariate Data Series using Fuzzy Logic - Applications to e-Health},
booktitle={Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems - Volume 1: ICE-B, (ICETE 2012)},
year={2012},
pages={123-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004124701230128},
isbn={978-989-8565-23-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems - Volume 1: ICE-B, (ICETE 2012)
TI - Pattern Characterization in Multivariate Data Series using Fuzzy Logic - Applications to e-Health
SN - 978-989-8565-23-5
AU - Fajardo W.
AU - Molina-Solana M.
AU - C. Valenza M.
PY - 2012
SP - 123
EP - 128
DO - 10.5220/0004124701230128