Name
Abstract | ||
|---|---|---|
in this paper, the electroencephalogram (EEG) signal that consists of the information about neurological activities of the brain has been considered to detect epileptic seizures. In connection with neural electrical activities, there is a very limited study using graph theory for seizure detection. In this work, a complete regular weighted undirected graph has been constructed in which the nodes are selected based on the variance among different channels of EEG signals, whereas weights of the edges are assigned based on the phase synchronization between associated nodes. Further, several properties of the graph have been extracted and used as feature sets to perform classification tasks. The well-known classifier k-NN has been applied for classification. In addition, principal component analysis has been used to reduce the dimension of feature sets that leads to improving the analysis and classification performance. The experimental analysis has been performed using EEG signals from the PhysioNet database for three cases — pre-ictal, ictal, and post-ictal. The results demonstrate that the proposed method achieved classification accuracy by up to 100%. The other two parameters\u0027 sensitivity and specificity are approximately 100%. The results demonstrate the usability and reliability of the proposed idea. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MWSCAS48704.2020.9184667 | MWSCAS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anand Shankar | 1 | 0 | 0.34 |
| S. Dandapat | 2 | 261 | 28.51 |
| Shovan Barma | 3 | 0 | 0.34 |