Title :
A New Approach for Investigating Intracranial Pressure Signal: Filtering and Morphological Features Extraction from Continuous Recording
Author :
Calisto, A. ; Galeano, M. ; Serrano, S. ; Calisto, A. ; Azzerboni, Bruno
Author_Institution :
Dept. of Electron. Eng., Ind. Chem. & Eng., Univ. of Messina, Messina, Italy
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, 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.
Keywords :
brain; classification; diseases; feature extraction; medical signal processing; neurophysiology; patient diagnosis; patient monitoring; ICP recordings; ICP signals; automatic subpeak identification; automatic system; brain damages; chronic neural pathologies; classification software; continuous recording; diseases; filtering features extraction; intracranial pressure monitoring; intracranial pressure signal; morphological features extraction; neurosurgeon; patient diagnosis; pulse wave mean value; single waveform; subpeak amplitude; subpeak position; traumatic pathologies; triphasic signal; Feature extraction; Filtering algorithms; Finite impulse response filter; Iterative closest point algorithm; Noise; Signal processing algorithms; Biomedical monitoring; Computer aided diagnosis; Hypertension; Intracranial pressure (ICP) sensors; Medical signal detection; signal processing; transducers; Adult; Aged; Algorithms; Diagnosis, Computer-Assisted; Female; Humans; Hydrocephalus, Normal Pressure; Intracranial Pressure; Male; Middle Aged; Monitoring, Physiologic; Reproducibility of Results; Signal Processing, Computer-Assisted; Spinal Puncture; Transducers;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2012.2191550