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Acta Neurochir (Wien). 2016 Mar;158(3):581-8; discussion 588. doi: 10.1007/s00701-015-2672-5. Epub 2016 Jan 8.

Intracranial pressure wave morphological classification: automated analysis and clinical validation.

Author information

1
Institute of Neurosurgery, Catholic University School of Medicine, Largo A. Gemelli 8, Rome, Italy. carlottaginevra@gmail.com.
2
Institute of Neurosurgery, Catholic University School of Medicine, Largo A. Gemelli 8, Rome, Italy.
3
Department of Information Engineering, University of Florence, Via di Santa Marta 3, Florence, Italy.
4
Institute of System Analysis and Informatics, National Research Council, Viale Manzoni 30, Rome, Italy.

Abstract

BACKGROUND:

Recently, different software has been developed to automatically analyze multiple intracranial pressure (ICP) parameters, but the suggested methods are frequently complex and have no clinical correlation. The objective of this study was to assess the clinical value of a new morphological classification of the cerebrospinal fluid pulse pressure waveform (CSFPPW), comparing it to the elastance index (EI) and CSF-outflow resistance (Rout), and to test the efficacy of an automatic ICP analysis.

METHODS:

An artificial neural network (ANN) was trained to classify 60 CSFPPWs in four different classes, according to their morphology, and its efficacy was compared to an expert examiner's classification. The morphology of CSFPPW, recorded in 60 patients at baseline, was compared to EI and Rout calculated at the end of an intraventricular infusion test to validate the utility of the proposed classification in patients' clinical evaluation.

RESULTS:

The overall concordance in CSFPPW classification between the expert examiner and the ANN was 88.3 %. An elevation of EI was statistically related to morphological class' progression. All patients showing pathological baseline CSFPPW (class IV) revealed an alteration of CSF hydrodynamics at the end of their infusion test.

CONCLUSIONS:

The proposed morphological classification estimates the global ICP wave and its ability to reflect or predict an alteration in CSF hydrodynamics. An ANN can be trained to efficiently recognize four different CSF wave morphologies. This classification seems helpful and accurate for diagnostic use.

KEYWORDS:

Artificial neural network; Cerebrospinal fluid hydrodynamics; Elastance index; Intracranial pressure; Morphological classification; Waveform analysis

PMID:
26743919
DOI:
10.1007/s00701-015-2672-5
[Indexed for MEDLINE]

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