Soft Computing Approach to Predict Intracranial Pressure Values

The estimation and the prediction of the values related to the Intracranial Pression (ICP) represents an
important step for the evaluation of the compliance of the human brain, above all in those cases in which the
increase of the ICP values determines high risk conditions for the patient. The regular therapy is neurosurgical
but, waiting for it, it is needed an aimed pharmacological therapy leading to an overload of the
kidneys’ functionality. Thus, it becomes evident the necessity to set an effective and efficient procedure for
the prediction of the ICP values with a suitable time recordings to mark the systematic pharmacological
action addressed towards really necessary deliverings. The prediction techniques most commonly used in
the literature, while providing a good window of time, are characterized by heavy computational complexity
unappetizing to real time applications and technology transfer. In addition, ICP sampling techniques are not
free from uncertainties due to affected elements (breath, heartbeat, voluntary/involuntary movement)
requesting the manipulation of uncertain and imprecise data. Thus, the choice of predictive techniques of
soft computing type appears reasonable firstly, because it manipulates data effectively with uncertainty and
/or imprecision and, secondly, for the same time frame predictive requires are duce computational load. In
this study the author presents a study of the prediction of the ICP values through a two factors fuzzy time
series comparing the results with more sophisticated techniques.