Name
Abstract | ||
|---|---|---|
This work presents a novel modeling of neuronal activity of the brain by capturing the synchronization of EEG signals along the scalp. The pair-wise correspondence between electrodes recording EEG signals are used to establish edges between these electrodes which then become the nodes of a synchronization graph. As EEG signals are recorded over time, we discretize the time axis into overlapping epochs, and build a series of time-evolving synchronization graphs for each epoch and for each traditional frequency band. We show that graph theory provides a rich set of graph features that can be used for mining and learning from the EEG signals to determine temporal and spatial localization of epileptic seizures. We present several techniques to capture the pair-wise synchronization and apply unsupervised learning algorithms, such as k-means clustering and multiway modeling of third-order tensors, to analyze the labeled clinical data in the feature domain to detect the onset and origin location of the seizure. We use k-means clustering on two-way feature matrices for detection of seizures, and Tucker3 tensor decomposition for localization of seizures. We conduct an extensive parametric search to determine the best configuration of the model parameters including epoch length, synchronization metrics, and frequency bands, to achieve the highest accuracy. Our results are promising: we are able to detect the onset of seizure with an accuracy of 88.24%, and localize the onset of the seizure with an accuracy of 76.47%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2649387.2649423 | BCB |
Keywords | Field | DocType |
algorithms,tensor decomposition,synchronization graphs,seizure localization,measurement,feature extraction,graph theory,eeg recordings,epileptic seizures,seizure detection,unsupervised learning | Computer science,Unsupervised learning,Artificial intelligence,Cluster analysis,Electroencephalography,Radio spectrum,Graph theory,Synchronization,Pattern recognition,Frequency band,Feature extraction,Speech recognition,Machine learning | Conference |
Citations | PageRank | References |
4 | 0.48 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nimit Dhulekar | 1 | 14 | 3.08 |
| Basak Oztan | 2 | 64 | 10.31 |
| Bülent Yener | 3 | 1075 | 94.51 |
| Haluk Bingol | 4 | 107 | 13.01 |
| Gulcin Irim | 5 | 4 | 0.48 |
| Berrin Aktekin | 6 | 4 | 0.82 |
| Canan Aykut-Bingöl | 7 | 4 | 0.82 |