| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 558133 | Biomedical Signal Processing and Control | 2014 | 9 Pages |
•Phase synchronization in brain using recurrence plot based technique is studied.•A novel application of the technique to multichannel seizure EEG data is made.•Contour plots of Correlation between Probability of Recurrence (CPR) matrix show clear contrast between seizure and pre-seizure signals and a classification accuracy of 100% has been achieved.•CPR could identify the focus of epilepsy and the identification is much better than that obtained using linear correlation.•The paper has shown the utility of a nonparametric nonlinear model in studying synchronization in the brain.
Complex biological systems such as the human brain can be expected to be inherently nonlinear and hence difficult to model. Most of the previous studies on investigations of brain function have either used linear models or parametric nonlinear models. In this paper, we propose a novel application of a nonlinear measure of phase synchronization based on recurrences, correlation between probabilities of recurrence (CPR), to study seizures in the brain. The advantage of this nonparametric method is that it makes very few assumptions thus making it possible to investigate brain functioning in a data-driven way. We have demonstrated the utility of CPR measure for the study of phase synchronization in multichannel seizure EEG recorded from patients with global as well as focal epilepsy. For the case of global epilepsy, brain synchronization using thresholded CPR matrix of multichannel EEG signals showed clear differences in results obtained for epileptic seizure and pre-seizure. Brain headmaps obtained for seizure and pre-seizure cases provide meaningful insights about synchronization in the brain in those states. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Comparative studies with linear correlation have shown that the nonlinear measure CPR outperforms the linear correlation measure.
