Massimiliano Galeano

A New Approach for Investigating Intracranial Pressure Signal: Filtering and Morphological Features Extraction from Continuous Recording

Nowadays, the Intracranial Pressure (ICP) monitoring has become the most common method of investigation for both traumatic and chronic neural pathologies. ICP signals are typically triphasic, that is, in a single waveform, three subpeaks can be identified. This work outlines a new algorithm to identify subpeaks from the ICP recordings and to extract a number of 20 meaningful parameter trends. The validity of the implemented method has been proved through a comparison between the automatic subpeaks identification by the algorithm and the manually marked subpeaks by a neurosurgeon. The automatic marking system has identified subpeaks for the 63.74% (mean value) of pulse waves, providing the position and amplitude of each identified subpeak within a tolerance of ±7 samples. This automatic system provides a feature set to be used by classification software to obtain more precise and easier diagnosis in all those cases that involve brain damages or diseases.

A preliminary study for investigating idiopatic normal pressure hydrocephalus by means of statistical parameters classification of intracranial pressure recordings

The objective of this study is to investigate Id-iopatic Normal Pressure Hydrocephalus (INPH) through a multidimensional and multiparameter analysis of statistical data obtained from accurate analysis of Intracranial Pressure (ICP) recordings. Such a study could permit to detect new factors, correlated with therapeutic response, which are able to validate a predicting significance for infusion test. The algorithm developed by the authors computes 13 ICP parameter trends on each of the recording, afterward 9 statistical information from each trend is determined. All data are transferred to the datamining software WEKA. According to the exploited feature-selection techniques, the WEKA has revealed that the most significant statistical parameter is the maximum of Single-Wave-Amplitude: setting a 27 mmHg threshold leads to over 90% of correct classification.

Classification of morphological features extracted from intracranial pressure recordings in the diagnosis of Normal Pressure Hydrocephalus (NPH)

The intracranial pressure (ICP) monitoring is a common procedure in neuro-intensive care for pathologies such traumatic brain injuries or hemorrhages, but also for chronic ones as the Normal Pressure Hydrocephalus (NPH). The only available treatment for NPH is the surgical implantation of a shunt with the aim of routing cerebrospinal fluid (CSF) away from the brain to another part of the body. In this study, using the classification software WEKA, an intensive investigation of ICP signals has been conducted. In particular we studied 14 ICP recordings of different patients who underwent an infusion test, with the aim of investigating the presence of NPH through the ICP recording. More precisely, 20 morphological features are extracted from the ICP pulsed wave, the trend have been computed and, for each one, 9 statistical functions determined. The 180 features have been selected and passed for the classification. The results obtained shows how, among the 14 patients, a number of 12 out of 14 (85.7%) have been correctly classified, looking at just 3 features. In particular 8 out of 9 not-NPH-affected patients were correctly identified (88.89%) while 4 out of 5 NPH-affected patients were correctly identified (80%).

Analysis of intracranial pressure recordings: Comparison of PCA and signal averaging based filtering methods and signal period estimation

Intracranial pressure monitoring is a common used approach for neuro-intensive care in cases of brain damages and injuries or to investigate chronic pathologies. Several types of noises and artifacts normally contaminate ICP recordings. They can be sorted in 2 classes, i.e. high-frequency noises (due to measurement and amplifier devices or electricity supply presence) and low-frequency noises (due to unwanted patients movement, speeches, coughing during the recording and quantization noise). Thus, deep investigations on ICP components aimed to extract features from ICP signal, require a denoised signal. For this reason the authors have addressed a study upon the most common filtering techniques. On each ICP recording we have performed 4 configurations of filters, which involve the use of a FIR filter together with Signal Averaging filters or PCA based filters. Next step is period estimation for absolute minima detection. The results obtained by the algorithm for automatic ICP marking are compared to those ones obtained from manual marking (peaks are manually identified and annotated by a brain surgeon). The procedure is repeated varying the filters sliding window size to minimize the mean square error. The results show how the configurations FIR filter + Signal averaging provides smaller mean squared error (MSE=118.84[sample2]) than the others 3 configurations FIR filter + PCA filter based (MSE=135.29−147.15[sample2]).

Digital Processing of Intracranial Pressure Signal Acquired by Means of a Strain Gauge Sensor

The aim of this work is to exploit an automatic system to make it easier, more quickly and costless the evaluation of Intracranial pressure (ICP) signal in cerebral pathology affected patients. The authors have developed a tool able to filter, analyze and extract features from ICP signal or recording. For the conducted study we have used the ICP MicroSensor, a catheter with a micro miniature silicon strain gauge type sensor mounted at one end and an electrical connector at the other end. We studied 16 continuous pressure recordings of different patients who underwent an infusion test. The digital signal processing (DSP) performed consists in: signal filtering, peaks identification, location of single pressure waves and extraction of features from each single wave. The outflow of the elaboration is composed by the 14 parameters trends which allows an easy analysis of intracranial pressure. It can be intended as a valid, consistent, reliable and easy-computing tool that might be used by the medical team in all those cases that involve brain damages or diseases.