Intracranial pressure pulse morphological features improved detection of decreased cerebral blood flow

We investigated whether intracranial pressure (ICP) pulse morphological metrics could be used to realize continuous detection of low cerebral blood flow. Sixty-three acutely brain injured patients with ICP monitoring, daily (133)Xenon cerebral blood flow (CBF) and daily transcranial Doppler (TCD) assessments were studied. Their ICP recordings were time-aligned with the CBF and TCD measurements so that a 1 h ICP segment near the CBF and TCD measurements was obtained. Each of these recordings was processed by the Morphological Cluster and Analysis of Intracranial Pressure (MOCAIP) algorithm to extract pulse morphological metrics. Then the differential evolution algorithm was used to find the optimal combination of the metrics that provided, using the regularized linear discriminant analysis, the largest combined positive predictivity and sensitivity. At a CBF threshold of 20 ml/min/100 g, a sensitivity of 81.8 +/- 0.9% and a specificity of 50.1 +/- 0.2% were obtained using the optimal combination of conventional TCD and blood analysis metrics as input to a regularized linear classifier. However, using the optimal combination of the MOCAIP metrics alone we were able to achieve a sensitivity of 92.5 +/- 0.7% and a specificity of 84.8 +/- 0.8%. Searching the optimal combination of all available metrics, we achieved the best result that was marginally better than those from using MOCAIP alone. This study demonstrated that the potential role of ICP monitoring may be extended to provide an indicator of low global cerebral blood perfusion.

[1]  R. Adolph,et al.  Origin of cerebrospinal fluid pulsations. , 1967, The American journal of physiology.

[2]  J. Hamer,et al.  Influence of systemic and cerebral vascular factors on the cerebrospinal fluid pulse waves. , 1977, Journal of neurosurgery.

[3]  T. Gennarelli,et al.  Relation of cerebral blood flow to neurological status and outcome in head-injured patients. , 1979, Journal of neurosurgery.

[4]  R. Aaslid,et al.  Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries. , 1982, Journal of neurosurgery.

[5]  S. Galbraith,et al.  Analysis of the cerebrospinal fluid pulse wave in intracranial pressure. , 1983, Journal of neurosurgery.

[6]  R. Aaslid,et al.  Evaluation of cerebrovascular spasm with transcranial Doppler ultrasound. , 1984, Journal of neurosurgery.

[7]  J. Friedman Regularized Discriminant Analysis , 1989 .

[8]  C. Robertson,et al.  Comparison of brain tissue oxygen tension to microdialysis-based measures of cerebral ischemia in fatally head-injured humans. , 1998, Journal of neurotrauma.

[9]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[10]  U. Ungerstedt,et al.  Bedside detection of brain ischemia using intracerebral microdialysis: subarachnoid hemorrhage and delayed ischemic deterioration. , 1999, Neurosurgery.

[11]  N. Thakor,et al.  A novel quantitative EEG injury measure of global cerebral ischemia , 2000, Clinical Neurophysiology.

[12]  N. Thakor,et al.  Time-Dependent Entropy Estimation of EEG Rhythm Changes Following Brain Ischemia , 2003, Annals of Biomedical Engineering.

[13]  Jan Claassen,et al.  Quantitative continuous EEG for detecting delayed cerebral ischemia in patients with poor-grade subarachnoid hemorrhage , 2004, Clinical Neurophysiology.

[14]  H. Amthauer,et al.  Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage: A Correlative Microdialysis-PET Study , 2004, Stroke.

[15]  Marvin Bergsneider,et al.  Metabolic Crisis without Brain Ischemia is Common after Traumatic Brain Injury: A Combined Microdialysis and Positron Emission Tomography Study , 2005, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[17]  Xiao Hu,et al.  Estimation of Hidden State Variables of the Intracranial System Using Constrained Nonlinear Kalman Filters , 2007, IEEE Transactions on Biomedical Engineering.

[18]  Scott A. Stevens,et al.  A Model for Idiopathic Intracranial Hypertension and Associated Pathological ICP Wave-Forms , 2008, IEEE Transactions on Biomedical Engineering.

[19]  Darrin J. Lee,et al.  Morphological changes of intracranial pressure pulses are correlated with acute dilatation of ventricles. , 2008, Acta neurochirurgica. Supplement.

[20]  Xiao Hu,et al.  An algorithm for extracting intracranial pressure latency relative to electrocardiogram R wave , 2008, Physiological measurement.

[21]  Per Kristian Eide,et al.  Comparison of simultaneous continuous intracranial pressure (ICP) signals from ICP sensors placed within the brain parenchyma and the epidural space. , 2008, Medical engineering & physics.

[22]  Xiao Hu,et al.  A subspace decomposition approach toward recognizing valid pulsatile signals. , 2009, Physiological measurement.

[23]  Xiao Hu,et al.  Morphological Clustering and Analysis of Continuous Intracranial Pressure , 2009, IEEE Transactions on Biomedical Engineering.

[24]  S. A. Stevens,et al.  A mathematical model of idiopathic intracranial hypertension incorporating increased arterial inflow and variable venous outflow collapsibility. , 2009, Journal of neurosurgery.

[25]  Xiao Hu,et al.  Regression analysis for peak designation in pulsatile pressure signals , 2009, Medical & Biological Engineering & Computing.

[26]  Xiao Hu,et al.  Inferring Cerebrovascular Changes from Latencies of Systemic and Intracranial Pulses: A Model-Based Latency Subtraction Algorithm , 2009, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[27]  Guy Nagels,et al.  Reproducibility and clinical relevance of quantitative EEG parameters in cerebral ischemia: A basic approach , 2009, Clinical Neurophysiology.