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 3 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 parameters 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 features 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 new approach for investigating intracranial pressure signal: filtering and morphological features extraction from continuous recording

CALISTO, ANDREA;GALEANO, MASSIMILIANO;SERRANO, Salvatore;AZZERBONI, Bruno
2013-01-01

Abstract

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 3 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 parameters 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 features set to be used by classification software to obtain more precise and easier diagnosis in all those cases that involve brain damages or diseases.
2013
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/1909497
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