Evaluating a nonlinear interdependence measure in electroencephalographic recordings from epilepsy patients
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- dc.contributor.author Espinoso Palacín, Anaïs
- dc.date.accessioned 2019-11-08T13:45:51Z
- dc.date.available 2019-11-08T13:45:51Z
- dc.date.issued 2019-07
- dc.description Treball fi de màster de: Master in Computational Biomedical Engineeringca
- dc.description Tutor: Ralph Gregor Andrzejak
- dc.description.abstract The analysis of neuronal recordings is important for the understanding of the human brain. Epilepsy is a neurological disorder characterized by a synchronous neuronal activity in the brain, that causes seizures. This synchrony can also be found in seizure-free intracranial electroencephalographic (EEG) recordings. Using nonlinear interdependence measures we can characterize the dynamical interdependencies in epileptic brain. Nowadays, there are different methods able to identify interaction between different brain areas, such as the cross-correlation or the linear coherence. However, these methods may not be optimal because they are not sensitive for nonlinear interdependence. In this thesis, we use a rank-based nonlinear interdependence measure (L) able to detect the direction of coupling and the strength between two dynamics. However, L can be affected by noise or cross-correlation of the underlying dynamics. To enhance the specificity of the approach, we apply a surrogate correction (ΔL) to test the results against a specific null hypothesis. We apply this technique to EEG signals measured at different spatial scales of neuronal organization (micro and macrocontacts), stages of the sleep-wake cycle and hemispheres. These hemispheres can be focal, where seizures are produced, or nonfocal, where there are no evidences of seizures. We first evaluate our measure with bivariate model dynamics (stochastic and deterministic), where ΔL demonstrates to find the correct direction of coupling. Then, we apply ΔL to 960 seizure-free EEG signals from 3 patients. We obtain the interdependency between macrocontacts, between microcontacts and across macro and microcontacts. In general, results show higher values of interdependency for the focal hemisphere as compared to the nonfocal hemisphere. This high interdependency is consistent in all patients when ΔL is applied between macro and microcontacts. Regarding the stages of the sleep-wake cycle, the deepest sleep stages present more differences between focal and nonfocal hemispheres. In conclusion, we found that ΔL shows promising results to localise the focal hemisphere without the presence of seizures. This is very important since seizures are considered a health impairing phenomenon. A potential therapy for epilepsy patients is resecting the area that produces seizures performing a surgery. This thesis provides further evidence that the analysis of multichannel EEG recordings using nonlinear techniques can be useful for diagnostic purposes.ca
- dc.format.mimetype application/pdf*
- dc.identifier.uri http://hdl.handle.net/10230/42813
- dc.language eng
- dc.language.iso engca
- dc.rights Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanyaca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/es/deed.caca
- dc.subject.keyword Signal analysis
- dc.subject.keyword EEG
- dc.subject.keyword Nonlinear Time Series Analysis
- dc.subject.keyword Nonlinear interdependence measure
- dc.subject.keyword Epilepsy
- dc.subject.other Epilèpsia
- dc.subject.other Intel·ligencia Artificial ; Aprenentatge automàtic
- dc.title Evaluating a nonlinear interdependence measure in electroencephalographic recordings from epilepsy patientsca
- dc.type info:eu-repo/semantics/masterThesisca