Clustering of Intracranial Pressure using Hidden Markov Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03096836" target="_blank" >RIV/68407700:21230/04:03096836 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Clustering of Intracranial Pressure using Hidden Markov Models
Original language description
We present a clustering algorithm based on continuous Hidden Markov Models (HMM) to automatically classify intracranial pressure (ICP) beats based on their morphology. The algorithm detects, classifies and labels each beat as a low--pressure or high--pressure beat based on morphology. An ICP beat detection algorithm is used to automatically detect each beat. In order to avoid the numerical problems with classical Expectation-Maximization (EM) algorithm we applied Variational Bayes Learning for HMM optimization. We measured the performance of the algorithm compared to expert classification of ICP beats acquired from intensive care unit patients using both partitional and hierarchical clustering schemes. We showed that neither partitional nor hierarchical scheme are superior to each other; the clustering performance about 80 % was achieved both on synthetic and real-icp data.
Czech name
Není k dispozici
Czech description
Není k dispozici

Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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