6533b86efe1ef96bd12cbe49
RESEARCH PRODUCT
Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children
Anton PopovVolodymyr KharytonovIvan KotiuchyiLuca FaesIvan SeleznovRiccardo Pernicesubject
Multivariate statisticsepilepsy epileptic seizures EEG brain connectivity common spatial patterns VAR model ICAmedicine.diagnostic_testComputer sciencebusiness.industryBrain activity and meditationPattern recognitionCoherence (statistics)Electroencephalographymedicine.diseaseSettore ING-INF/01 - ElettronicaEpilepsyAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticamedicineIctalArtificial intelligencebusinessPre and postdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-09-01 |