@inproceedings{ddd5c76ed69747838de6c2b790451649,
title = "Functional connectivity network based on graph analysis of scalp EEG for epileptic classification",
abstract = "The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.",
keywords = "Epilepsy, Functional Connectivity, Graph Theory, Scalp EEG",
author = "Saman Sargolzaei and Mercedes Cabrerizo and Mohammed Goryawala and Eddin, {Anas Salah} and Malek Adjouadi",
year = "2013",
doi = "10.1109/SPMB.2013.6736779",
language = "English (US)",
isbn = "9781479930074",
series = "2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013",
publisher = "IEEE Computer Society",
booktitle = "2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013",
note = "2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013 ; Conference date: 07-12-2013 Through 07-12-2013",
}