@inproceedings{5f6d7c92107747eda9ee9c90ca127d94,
title = "Probabilistic modeling of the oxygen saturation pattern for the detection of anomalies during clinical interventions",
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.",
keywords = "indexes, testing, hidden Markov models, proposals, Markov processes, protocols, real-time systems",
author = "{Martin Martinez}, D. and {Casaseca de la Higuera}, P. and {Martin Fernandez}, M. and {Alberola Lopez}, C.",
year = "2013",
language = "English",
isbn = "9781479908844",
volume = "40",
series = "Computing in Cardiology Conference",
publisher = "IEEE",
pages = "213--216",
editor = "Alan Murray",
booktitle = "Computing in Cardiology 2013",
address = "United States",
}