HIDDEN MARKOV MODELS FOR MODELING BLOOD PRESSURE DATA TO PREDICT ACUTE HYPOTENSION

The ability to predict episodes of acute hypotension (abnormal drop in arterial blood pressure) would be of immense benefit to the healthcare community, and is therefore a focus of research in both medical and engineering domains. This paper presents the use of Hidden Markov Models to predict the onset of acute hypotension, using blood pressure measurements over time. Our use of HMMs has been motivated by their ability to characterize sequential/temporal trends in a given time signal. This lends the ability to infer the health status based on blood pressure information collected over an interval of time, rather than just instantaneous measurements. We have tested the proposed technique on standard physiological signal datasets available online and have obtained promising results. As part of a bigger project, we see potential in the proposed technique being used in real time health monitoring systems.