Phase-based signal analysis measures applied to electroencephalographic recordings from epilepsy patients
This thesis presents new phase-based signal analysis techniques to study brain dynamics from patients with pharmacoresistant focalonset epilepsy. We analyze electroencephalographic (EEG) recordings from seizure and seizure-free periods to characterize the dynamics underlying signals measured in brai...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/689830 |
| Acceso en línea: | http://hdl.handle.net/10803/689830 |
| Access Level: | acceso abierto |
| Palabra clave: | Epilepsy Quantitative EEG analysis EEG Hilbert phase Phase irregularity Phase synchronization Network phaselocking Rössler dynamics Epilepsia Análisis cuantitativo de EEG Fase de Hilbert Irregularidad de fase Sincronización de fase Dinámica de Rössler Epilèpsia Anàlisi quantitativa d’EEG Irregularitat de fase Sincronització de fase Sincronització de fase en xarxa Dinàmica de Rössler 62 |
| Sumario: | This thesis presents new phase-based signal analysis techniques to study brain dynamics from patients with pharmacoresistant focalonset epilepsy. We analyze electroencephalographic (EEG) recordings from seizure and seizure-free periods to characterize the dynamics underlying signals measured in brain areas where seizures start (focal signals) and from other brain areas (nonfocal signals). To first evaluate our approach under controlled conditions, we use the Rössler model system. We introduce the univariate coefficient of phase velocity variation and the multivariate phase-locking contribution measure. As a bivariate approach, we use the wellestablished mean phase coherence. All measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. Beyond confirming that focal signals have a higher mean phase coherence compared to nonfocal signals, we find that they have less phase variability and contribute more to the overall network’s phase-locking. Thus, our approach to analyze EEG signals across different spatial scales of neuronal organization holds promise to contribute to the diagnosis of epilepsy patients. |
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