Temporal Scenario Recognition for Intelligent Patient Monitoring

The recognition of high level clinical scenes as they are developing is fundamental in patient monitoring. In this paper, we propose a technique to recognize on the fly a session, i.e. the clinical process's evolution, by comparison to a predetermined set of scenarios, i.e. the possible behaviours for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of proximity is introduced to quantify the degree of matching between two temporal networks and used to select the best scenario fitting a session. We explore the application of our technique to the recognition of typical scenarios for mechanical ventilation management.

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