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.
|Title of host publication||Computing in Cardiology 2013|
|Number of pages||4|
|Publication status||Published - 2013|
|Name||Computing in Cardiology Conference|
- hidden Markov models
- Markov processes
- real-time systems
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.