Probabilistic modeling of the oxygen saturation pattern for the detection of anomalies during clinical interventions

D. Martin Martinez, P. Casaseca de la Higuera, M. Martin Fernandez, C. Alberola Lopez

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

Abstract

In this paper, we propose a Markov model-based methodology aimed at detecting in real time the anomalies that the oxygen saturation pattern suffers during clinical interventions or procedures. To this end, we first extract a reference pattern from the patient in nominal conditions before the procedure takes place. Then, in a second stage, a measurement of the similarity between the reference pattern and the pattern of the epoch to be tested is obtained through the Williams' Index. This measurement is compared with a threshold to determine the normal/abnormal character of the pattern under test. Experiments on real data show that the proposed methodology is sensitive to the anomalies induced when the respiratory function is impaired; this is accomplished through the simulation of several situations (shortness of breath, interrupted breathing, hyperventilation and CO2 increasing in blood) in which the respiratory impairment is manually emulated.
Original languageEnglish
Title of host publicationComputing in Cardiology 2013
EditorsAlan Murray
PublisherIEEE
Pages213-216
Number of pages4
Volume40
ISBN (Electronic)9781479908868
ISBN (Print)9781479908844
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameComputing in Cardiology Conference
PublisherIEEE
ISSN (Print)2325-887X
ISSN (Electronic)2325-8861

Keywords

  • indexes
  • testing
  • hidden Markov models
  • proposals
  • Markov processes
  • protocols
  • real-time systems

Cite this

Martin Martinez, D., Casaseca de la Higuera, P., Martin Fernandez, M., & Alberola Lopez, C. (2013). Probabilistic modeling of the oxygen saturation pattern for the detection of anomalies during clinical interventions. In A. Murray (Ed.), Computing in Cardiology 2013 (Vol. 40, pp. 213-216). (Computing in Cardiology Conference). IEEE.