Name
Abstract | ||
|---|---|---|
In this paper, we present a new network-based approach to help experts investigate neurological disorders in which the connections among brain areas play a key role. Our approach receives the EEG of a patient and associates a network with it, with nodes that represent electrodes and with edges that denote the disconnection degree of the corresponding brain areas, measured by means of a new string-based metric. Then, it performs some suitable projections on this network, depending on the neurological disorder to investigate. After this, it computes the values of a new coefficient, called connection coefficient, on them. These values can be employed to help neurologists in their analyses. We show how our approach can be employed for three different disorders, namely Creutzfeldt-Jacob disease, childhood absence epilepsy and Alzheimer's disease. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1504/IJDMMM.2019.102730 | INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT |
Keywords | Field | DocType |
network analysis, connection coefficient, consensus multi-parameterised edit distance, cMPED, electroencephalogram, neurological disorders | Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 4 | 1759-1163 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Cauteruccio | 1 | 0 | 0.34 |
| Paolo Lo Giudice | 2 | 3 | 1.76 |
| Giorgio Terracina | 3 | 701 | 70.85 |
| Domenico Ursino | 4 | 897 | 104.96 |
| Nadia Mammone | 5 | 136 | 19.69 |
| Francesco Carlo Morabito | 6 | 339 | 54.83 |