Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloIntracranial pressure wave morphological classification: automated analysis and clinical validation
Anno di pubblicazione2016
Formato-
Autore/iNucci, Carlotta Ginevra; De Bonis, Pasquale; Mangiola, Annunziato; Santini, Pietro; Sciandrone, Marco; Risi, Arnaldo; Anile, Carmelo
Affiliazioni autoriInstitute of Neurosurgery, Catholic University School of Medicine; Department of Information Engineering, University of Florence; Institute of System Analysis and Informatics, National Research Council
Autori CNR e affiliazioni
  • MARCO SCIANDRONE
Lingua/e
  • inglese
AbstractRecently, 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 abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da581
Pagine a588
Pagine totali8
RivistaActa neurochirurgica
Attiva dal 1950
Editore: Springer - Wien ;
Paese di pubblicazione: Austria
Lingua: inglese
ISSN: 0001-6268
Titolo chiave: Acta neurochirurgica
Titolo proprio: Acta neurochirurgica.
Titolo abbreviato: Acta neurochir.
Titolo alternativo: Acta neurochirurgica (Print)
Numero volume della rivista158
Fascicolo della rivista3
DOI10.1007/s00701-015-2672-5
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000370074500031)
Parole chiaveIntracranial pressure, Artificial neural network, Waveform analysis, Morphological classification, Elastance index, Cerebrospinal fluid hydrodynamics
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IASI — Istituto di analisi dei sistemi ed informatica "Antonio Ruberti"
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD021.027.001 : OPTIMA - Ottimizzazione, Matematica Discreta e Applicazioni per la Società e l'Industria
Progetti Europei-
Allegati