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EnglishIn this research, the study of functional connectivity between sources of electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) were obtained for EEG segments describing 95% of Riemannian distance between pairs of classes, followed by estimation of multivariate autoregressive (MVAR) models’ coefficients. The MVAR models were further used to extract coherence as a functional connectivity measures. Our results show that the coherence between CSP sources differs between baseline and pre-ictal segments: it has the larger values in low and high frequency ranges during pre-ictal segment. This might correspond to increased coupling of slow and fast oscillations just before the seizure onset which poses a defining attribute of epileptiform activity. Our results indicate that a reorganization of EEG source activations occurs before the onset of focal seizures, which is promising for seizure prediction algorithms.
| Data di pubblicazione: | 2019 |
| Titolo: | Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children |
| Autori: | |
| Citazione: | Kotiuchyi, I., Seleznov, I., Faes, L., Pernice, R., Kharytonov, V., & Popov, A. (2019). Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children. In 2019 Signal Processing Symposium (SPSympo) (pp. 126-129). |
| Abstract: | In this research, the study of functional connectivity between sources of electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) were obtained for EEG segments describing 95% of Riemannian distance between pairs of classes, followed by estimation of multivariate autoregressive (MVAR) models’ coefficients. The MVAR models were further used to extract coherence as a functional connectivity measures. Our results show that the coherence between CSP sources differs between baseline and pre-ictal segments: it has the larger values in low and high frequency ranges during pre-ictal segment. This might correspond to increased coupling of slow and fast oscillations just before the seizure onset which poses a defining attribute of epileptiform activity. Our results indicate that a reorganization of EEG source activations occurs before the onset of focal seizures, which is promising for seizure prediction algorithms. |
| ISBN: | 978-1-7281-1715-7 |
| Digital Object Identifier (DOI): | 10.1109/SPS.2019.8882099 |
| Settore Scientifico Disciplinare: | Settore ING-INF/06 - Bioingegneria Elettronica E Informatica Settore ING-INF/01 - Elettronica |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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