Classification of patients using novel multivariate time series representations of physiological data

Patricia Ordóñez, Tom Armstrong, Tim Oates, Jim Fackler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present two novel multivariate time series representations to classify physiological data of different lengths. The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bags of-Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. We also borrow techniques from the natural language processing domain such as term frequency and inverse document frequency to improve classification accuracy. We introduce a technique named inverse frequency and present experimental results on classifying patients who have experienced acute episodes of hypotension.

Original languageEnglish (US)
Title of host publicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Pages172-179
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
Duration: Dec 18 2011Dec 21 2011

Publication series

NameProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume2

Other

Other10th International Conference on Machine Learning and Applications, ICMLA 2011
CountryUnited States
CityHonolulu, HI
Period12/18/1112/21/11

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Keywords

  • Multivariate Bag-of-Patterns
  • Stacked Bags-of-Patterns
  • classification
  • inverse frequency
  • multivariate time series

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Ordóñez, P., Armstrong, T., Oates, T., & Fackler, J. (2011). Classification of patients using novel multivariate time series representations of physiological data. In Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 (pp. 172-179). [6147668] (Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011; Vol. 2). https://doi.org/10.1109/ICMLA.2011.46