Non-invasive Intracranial Pressure estimation using Support Vector Machine

Intracranial Pressure (ICP) measurements are of great importance for the diagnosis, monitoring and treatment of many vascular brain disturbances. The standard measurement of the ICP is performed invasively by the perforation of the cranial scalp in the presence of traumatic brain injury (TBI). Measuring the ICP in a noninvasive way is relevant for a great number of pathologies where the invasive measurement represents a high risk. The method proposed in this paper uses the Arterial Blood Pressure (ABP) and the Cerebral Blood Flow Velocity (CBFV) - which may be obtained by means of non-invasive methods - to estimate the ICP. A non-linear Support Vector Machine was used and reached a low error between the real ICP signal and the estimated one, allowing an on-line implementation of the ICP estimation, with an adequate temporal resolution.

[1]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[2]  R. Aaslid,et al.  Estimation of Cerebral Perfusion Pressure from Arterial Blood Pressure and Transcranial Doppler Recordings , 1986 .

[3]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[4]  M. Ursino,et al.  Intracranial pressure dynamics in patients with acute brain damage: a critical analysis with the aid of a mathematical model , 1995, IEEE Transactions on Biomedical Engineering.

[5]  M. Ursino,et al.  A simple mathematical model of the interaction between intracranial pressure and cerebral hemodynamics. , 1997, Journal of applied physiology.

[6]  D. Sander,et al.  Noninvasive prediction of intracranial pressure curves using transcranial Doppler ultrasonography and blood pressure curves. , 1998, Stroke.

[7]  J. Pickard,et al.  Cerebral perfusion pressure in head-injured patients: a noninvasive assessment using transcranial Doppler ultrasonography. , 1998, Journal of neurosurgery.

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[10]  M. Czosnyka,et al.  Evaluation of a method for noninvasive intracranial pressure assessment during infusion studies in patients with hydrocephalus. , 2000, Journal of neurosurgery.

[11]  G. Saade,et al.  EVALUATION OF A NONINVASIVE TRANSCRANIAL DOPPLER AND BLOOD PRESSURE-BASED METHOD FOR THE ASSESSMENT OF CEREBRAL PERFUSION PRESSURE IN PREGNANT WOMEN , 2000, Hypertension in pregnancy.

[12]  E. Bridges,et al.  Monitoring arterial blood pressure: what you may not know. , 2002, Critical care nurse.

[13]  R. Panerai,et al.  Assessment of dynamic cerebral autoregulation based on spontaneous fluctuations in arterial blood pressure and intracranial pressure , 2002, Physiological measurement.

[14]  D. Sackerer,et al.  Adaptive Noninvasive Assessment of Intracranial Pressure and Cerebral Autoregulation , 2003, Stroke.

[15]  Jiann-Shing Shieh,et al.  Intracranial pressure model in intensive care unit using a simple recurrent neural network through time , 2004, Neurocomputing.

[16]  S. Voci,et al.  Ultrasound of the Intracranial Arteries , 2006 .

[17]  Xiao Hu,et al.  A Data mining framework of noninvasive intracranial pressure assessment , 2006, Biomed. Signal Process. Control..

[18]  M. Ursino,et al.  A mathematical model of the relationship between cerebral blood volume and intracranial pressure changes: The generation of plateau waves , 2006, Annals of Biomedical Engineering.

[19]  A. Demchuk,et al.  Role of transcranial Doppler in neurocritical care , 2007, Critical care medicine.