title: Forecasting intracranial pressure elevation using pulse waveform morphology.
abstract: Management of intracranial pressure (ICP) following a traumatic brain injury (TBI) is an essential aspect of minimizing such secondary brain injuries as intracranial hypertension and cerebral hypoxia. Currently, ICU management of ICP elevations is reactive in nature; we propose a quantitative method to predict potentially harmful elevations in ICP.
  
  
  METHODS
  Continuous intracranial pressure measurements were obtained from 37 patients at the UCLA Medical Center. Intracranial hypertension (IH) episodes were identified along with slow wave segments (used for control sets). Four, five minute segments were then constructed from the IH episode: one from the onset of ICP elevation (pre-IH #0) along with sets 5, 20, and 35 minutes prior to the elevation (pre-IH #5, #20, #35 respectively). Quantification and recognition of the three ICP sub peaks was performed using our group's algorithm termed Morphological Clustering and Analysis of Intracranial Pressure (MOCAIP). Furthermore, a quadratic classifier (QDC) was used to determine the metrics with the greatest predictive power. These metrics were then used to compare the control data set to the data sets described previously.
  
  
  RESULTS
  From the ten most frequently selected metrics each of the four pre- intracranial hypertension (pre-IH) segments were compared with the control. Sensitivity (SEN), specificity (SPE), and accuracy (AC) were determined for each set with a SEN and SPE for the data set five minutes prior to ICP elevation of 90% and 75% respectively.
  
  
  CONCLUSIONS
  Combining the MOCAIP analysis, QDC classification, and bootstrap method of statistical sampling, our analysis has the potential to predict an ICP elevation event 20 minutes prior to the event onset.
authors: Neural Systems and Dynamics Laboratory, Department of Neurosurgery, University of California, Los Angeles, CA, USA. rhamilton@mednet.ucla.edu
authors: Robert
authors: R
authors: Hamilton
authors: Peng
authors: P
authors: Xu
authors: Shadnaz
authors: S
authors: Asgari
authors: Magdalena
authors: M
authors: Kasprowicz
authors: Paul
authors: P
authors: Vespa
authors: Marvin
authors: M
authors: Bergsneider
authors: Xaio
authors: X
authors: Hu
mesh_terms: D000465:Algorithms; D001930:Brain Injuries / Q000175:diagnosis / Q000503:physiopathology; D016000:Cluster Analysis; D001330:Electronic Data Processing; D004867:Equipment Design; D006801:Humans; D002534:Hypoxia, Brain; D019586:Intracranial Hypertension / Q000175:diagnosis* / Q000503:physiopathology*; D007427:Intracranial Pressure; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014160:Transducers, Pressure