title: Bayesian tracking of intracranial pressure signal morphology.
abstract: BACKGROUND
  The waveform morphology of intracranial pressure (ICP) pulses holds essential informations about intracranial and cerebrovascular pathophysiological variations. Most of current ICP pulse analysis frameworks process each pulse independently and therefore do not exploit the temporal dependency existing between successive pulses. We propose a probabilistic framework that exploits this temporal dependency to track ICP waveform morphology in terms of its three peaks.
  
  
  METHODS
  ICP and electrocardiogram (ECG) signals were recorded from a total of 128 patients treated for various intracranial pressure related conditions.
  
  
  METHODS
  The tracking is posed as inference in a graphical model that associates a random variable to the position of each peak. A key contribution is to exploit a nonparametric Bayesian inference algorithm that offers robustness and real time performance. A simple, yet effective learning procedure estimates the statistical, nonlinear, dependencies between the peaks in a nonparametric way using evidence collected from manually annotated pulses.
  
  
  RESULTS
  Experiments demonstrate the effectiveness of the tracking framework on real ICP pulses and its robustness to occlusion and missing peaks. On artificialy distorted ICP sequences, the average error in latency in comparision with MOCAIP detector was reduced as follows: 11.88-8.09 ms, 11.80-6.90 ms, and 11.76-7.46 ms for the first, second, and third peak, respectively.
  
  
  CONCLUSIONS
  The proposed tracking algorithm sucessfuly increases the temporal resolution of detecting ICP pulse morphological changes from the minute-level to the beat-level.
authors.affiliation: Neural Systems and Dynamics Laboratory, Department of Neurosurgery, Geffen School of Medicine, University of California, 924 Westwood Plaza, Los Angeles, CA 90024, USA. fscalzo@mednet.ucla.edu
authors.forename: Fabien
authors.initials: F
authors.lastname: Scalzo
authors.forename: Shadnaz
authors.initials: S
authors.lastname: Asgari
authors.forename: Sunghan
authors.initials: S
authors.lastname: Kim
authors.forename: Marvin
authors.initials: M
authors.lastname: Bergsneider
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D004562:Electrocardiography; D004867:Equipment Design; D006801:Humans; D007427:Intracranial Pressure; D008390:Markov Chains; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014160:Transducers, Pressure