A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD

Paolo LoLo Giudice, Domenico Ursino, Nadia Mammone, Francesco Carlo Morabito, Umberto Aguglia, Vittoria Cianci, Edoardo Ferlazzo, Sara Gasparini

Research output: Contribution to journalArticlepeer-review

Abstract

Creutzfeldt-Jacob disease (CJD) is a rapidly progressive, uniformly fatal transmissible spongiform encephalopathy. Sporadic CJD (sCJD) is the most common form of CJD. Electroencephalography (EEG) is one of the main methods to perform clinical diagnosis of CJD, mainly because of periodic sharp wave complexes (PSWCs). In this paper, we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD. Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs, which characterize the presence of PSWCs in an EEG tracing. The new coefficient, called connection coefficient, and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs. Furthermore, network motifs are able to detect what are the most active and/or connected brain areas in the tracing segments with PSWCs. The results obtained show that, analogously to what happens for other neurological diseases, network analysis can be successfully exploited to investigate sCJD.

Original languageEnglish
Pages (from-to)19-29
Number of pages11
JournalInternational Journal of Medical Informatics
Volume121
DOIs
Publication statusPublished - Jan 2019

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