Intracranial pressure wave morphological classification: automated analysis and clinical validation

Carlotta Ginevra Nucci, Pasquale De Bonis, Annunziato Mangiola, Pietro Santini, Carmelo Anile, Marco Sciandrone, Arnaldo Risi

Risultato della ricerca: Contributo in rivistaArticolo in rivista

14 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)581-8-588
RivistaActa Neurochirurgica
Volume158
DOI
Stato di pubblicazionePubblicato - 2016

Keywords

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

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