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.