Soft Computing Approach to Predict Intracranial Pressure Values

Abstract

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.

A Modified Heart Dipole Model for the Generation of Pathological ECG Signals

“…Uni-polar leads are recorded by coupling a single scanning electrode with a central terminal, while bipolar leads record the potential difference between two electrodes. Leads I, II, II are bipolar; leads aVR, AVL, aVF, and Wilson leads are uni-polar [30][31][32][33].…”

“…When the electrical impulse leaves the sinus node it produces the depolarization of the neighboring myofibrils which contract, and then continues to propagate in the radial sinus. The PR interval concerns the wave front which, passing through the atria, passes into the atrio-ventricular node (AV) within which the activated cells are few and the dipole generated is too weak to be registered [30][31][32]34,35]. As soon as the depolarization wave reaches the AV node, the electrical conduction slows down until the ventricular conduction system is reached.…”

“…The U wave is a wave that is not always appreciable because it is often of minimal size. It is due to the re-polarization of the papillary muscles, which can be evidenced in the case of myocardial hypertrophy or altered dimensions of the ventricular cavities [30][31][32]34,35]. The most salient characters of the human ECG are shown in Table 1 ( [36]).…”

“…The resulting atrial vector is directed from the upper right to the lower left, approximately in the same direction as the standard II lead. The PR interval runs from the beginning of the P wave to the beginning of the QRS complex (which represents the depolarization of the ventricles) [30][31][32]34] and indicates the time required for a pulse to travel from the sinus node to the ventricles and includes conduction through the atria and the AV node. This is the last element as certain drugs or pathologies can delay conduction through the atrioventricular node.…”

Frequent Correlated Periodic Pattern Mining for Large Volume Set Using Time Series Data

“…The observation is to categorize duplicate patterns that provide important observations and its updated information of time series data (Weigend and Gershenfeld, 1994;Versaci, 2014) and assist in decision making based on the result achieved (Rasheed et al, 2011). A time series (Sheng et al, 2005a) is said to have three type of periodic pattern: (1) Symbol periodicity, (2) sequence periodicity or partial periodic pattern and (3) segment or full-cycle periodicity (Rasheed et al, 2011).…”