In Physiological measurement ; h5-index 36.0
OBJECTIVE : We present a framework for analyzing the intracranial pressure (ICP) morphology. Analyzing ICP signals is challenging due to the non-linear and non-Gaussian characteristics of the signal dynamics, inevitable corruption with noise and artifacts, and variations in the ICP pulse morphology among individuals with different neurological conditions. Existing frameworks make unrealistic assumptions regarding ICP dynamics and are not tuned for individual patients.
APPROACH : We propose a dynamic Bayesian network (DBN) for automated detection of three major ICP pulsatile components. The proposed model captures the non-linear and non-Gaussian dynamics of the ICP morphology and further adapts to a patient as the individual's ICP measurements are received. To make the approach more robust, we leverage evidence reversal and present an inference algorithm to obtain the posterior distribution over the locations of pulsatile components.
RESULTS : We evaluate our approach on a dataset with over 700 hours of recordings from 66 neurological patients, where the pulsatile components have been annotated in prior studies. The algorithm obtains an accuracy of 96.56%, 92.39%, and 94.04% for detecting each pulsatile component on the test set, showing significant improvements over existing approaches.
SIGNIFICANCE : Continuous ICP monitoring is essential in guiding the treatment of neurological conditions such as traumatic brain injuries. An automated approach for ICP morphology analysis takes a step toward enhancing patient care with minimal supervision. Compared to previous methods, our framework offers several advantages. It learns the parameters that model each patient's ICP in an unsupervised manner, resulting in an accurate morphology analysis. The Bayesian model-based framework provides uncertainty estimates and reveals interesting facts about ICP dynamics. The framework can be readily applied to replace existing morphological analysis methods and support the application of ICP pulse morphological features to aid the monitoring of pathophysiological changes of relevance to the care of patients with acute brain injuries.
Rashidinejad Paria, Hu Xiao, Russell Stuart
Artificial Intelligence in Healthcare, Dynamic Bayesian Network, ICP, Model-Based Probabilistic Inference, Particle Filter, Patient Monitoring