Name
Title | ||
|---|---|---|
Geriatric depression symptoms coexisting with cognitive decline: A comparison of classification methodologies. |
Abstract | ||
|---|---|---|
•Depressive signs in concurrent cognitive decline are detected through mining EEG resting-state activity.•Random Forest, Random Tree, MLP Network and Support Vector Machines (SVM) are employed for data classification.•Random Forest demonstrated the highest accuracy.•Synchronization features significantly contributed to the decision tree formation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.bspc.2015.10.006 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Geriatric depression,Cognitive decline,Electroencephalography (EEG),Synchronization patterns,Random Forest,Random Tree,MLP Network,Support Vector Machines (SVM),Data mining,Elderly | Decision tree,Artificial intelligence,Data classification,Audiology,Cognition,Random forest,Electroencephalography,Synchronization,Pattern recognition,Correlation,Mathematics,Machine learning,Cognitive decline | Journal |
Volume | ISSN | Citations |
25 | 1746-8094 | 3 |
PageRank | References | Authors |
0.38 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioanna-Maria Spyrou | 1 | 3 | 0.72 |
| Christos Frantzidis | 2 | 170 | 16.32 |
| Charalampos Bratsas | 3 | 186 | 22.74 |
| Ioannis Antoniou | 4 | 60 | 12.93 |
| Panagiotis D. Bamidis | 5 | 419 | 74.89 |