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.
| Language | English |
|---|---|
| Pages | 19-29 |
| Number of pages | 11 |
| Journal | International Journal of Medical Informatics |
| Volume | 121 |
| DOIs | |
| Publication status | Published - Jan 2019 |
Cite this
A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD. / LoLo Giudice, Paolo; Ursino, Domenico; Mammone, Nadia; Morabito, Francesco Carlo; Aguglia, Umberto; Cianci, Vittoria; Ferlazzo, Edoardo; Gasparini, Sara.
In: International Journal of Medical Informatics, Vol. 121, 01.2019, p. 19-29.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD
AU - LoLo Giudice, Paolo
AU - Ursino, Domenico
AU - Mammone, Nadia
AU - Morabito, Francesco Carlo
AU - Aguglia, Umberto
AU - Cianci, Vittoria
AU - Ferlazzo, Edoardo
AU - Gasparini, Sara
N1 - Copyright © 2018 Elsevier B.V. All rights reserved.
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
U2 - 10.1016/j.ijmedinf.2018.11.003
DO - 10.1016/j.ijmedinf.2018.11.003
M3 - Article
VL - 121
SP - 19
EP - 29
JO - International Journal of Medical Informatics
T2 - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
SN - 1386-5056
ER -