Key result
Bayesian dynamic graphical model enables real-time tracking of intracranial pressure waveform morphology.
Why the study?
Automatic analysis of ICP waveform morphology may help predict abnormal ICP increases and prevent severe complications in patients treated for TBIs.
A probabilistic framework using Bayesian inference can track intracranial pressure waveform morphology in real time, potentially aiding in predicting abnormal ICP increases in TBI patients.
May enable real-time ICP tracking in TBI; leaves open clinical utility pending prospective validation.
The waveform morphology of intracranial pressure (ICP) pulses holds essential information about intracranial and cerebrovascular pathophysiologies. Automatic analysis of the ICP waveforms may help to predict abnormal increase of ICP and thus prevent severe complications in patients treated for traumatic brain injuries (TBIs). This article describes a probabilistic framework to track the ICP waveform morphology in real time. The model represents the correlation between different ICP morphological metrics extracted within a single pulse as well as the temporal dependence of metrics extracted between successive pulses. Morphological tracking is solved using Bayesian inference in a dynamic graphical model that associates a random variable to each morphological metric.
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Scalzo et al. (2012) studied Traumatic brain injuries (TBIs). Probabilistic framework for real-time tracking of ICP waveform morphology was evaluated. A probabilistic framework using Bayesian inference in a dynamic graphical model was developed to track intracranial pressure waveform morphology in real time.
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