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.

[1]  David S. Smith.  Intracranial pressure waveform indices in transient and refractory intracranial hypertension , 1995 .

[2]  Vincent M. Vacca,et al.  Diagnosis and Treatment of Idiopathic Normal Pressure Hydrocephalus , 2007, The Journal of neuroscience nursing : journal of the American Association of Neuroscience Nurses.

[3]  Marek Czosnyka,et al.  Cerebrospinal fluid dynamics. , 2004, Physiological measurement.

[4]  S. E. Børgesen,et al.  Single pulse pressure wave analysis by fast Fourier transformation. , 1989, Neurological research.

[5]  J. Pickard,et al.  INDEX OF CEREBROSPINAL COMPENSATORY RESERVE IN HYDROCEPHALUS , 2009, Neurosurgery.

[6]  J. Pickard,et al.  Significance of intracranial pressure waveform analysis after head injury , 2005, Acta Neurochirurgica.

[7]  Marek Czosnyka,et al.  Analysis of intracranial pressure during and after the infusion test in patients with communicating hydrocephalus. , 2005, Physiological measurement.

[8]  Francesco Beritelli,et al.  Biometric Identification Based on Frequency Analysis of Cardiac Sounds , 2007, IEEE Transactions on Information Forensics and Security.

[9]  J. Pickard,et al.  Monitoring and interpretation of intracranial pressure , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

[10]  Alessandra Russo,et al.  Speech Emotion Classification Using Machine Learning Algorithms , 2008, 2008 IEEE International Conference on Semantic Computing.