Uncovering seizure connectivity and directionality patterns across patients with focal epilepsy

Abstract

Epilepsy is a chronic neurological disease characterized by recurring seizures. While most epileptic patients can manage their condition with antiseizure drugs, approximately 25% of individuals experience drug-resistant epilepsy and their only option is to undergo surgery to remove the seizure-generating tissue. However, 65% of these surgeries result unsuccessful. Therefore, this bachelor thesis focuses on exploring a way to identify the seizure onset zone (SOZ) through electroencephalogram (EEG) recordings of seizures. The aim of this study is to analyze EEG seizure data using the Nonlinear Time Series Analysis (NTSA) measure L to identify the connectivity patterns and directionality of signals originating from the seizure onset zone. Our hypothesis is that these signals exhibit distinct connectivity profiles compared to signals from healthy brain tissue. Additionally, the study aims to classify electrodes depending if they are placed in the region which generates the seizures based on their connectivity profiles. The methodology consists of three main stages. Firstly, the measure L is applied to known dynamics like the Lorenz attractor. This measure is a distance rank-based measure able to detect directional coupling from time series that has been proven to be robust to noise and dynamics asymmetries. Secondly, suitable multi-channel EEG datasets with several seizures are searched for. Lastly, the measure L is applied to the EEG recordings to identify connectivity patterns and facilitate machine learning-based electrode classification. The results that are obtained in this thesis show that the connectivity profiles of the seizures evolve in time and allow for this electrode classification. With this, we bring insight into the connectivity dynamics of the SOZ that may allow for a more precise SOZ detection in the future and therefore, improve the resection surgery outcomes.