Seizure Onset Zone Localisation Algorithms with Intracranial EEG: Evaluating Methodological Variations and Targeted Features
During clinical treatment for epilepsy, the area of the brain thought to be responsible for pathological activity - known as the Seizure Onset Zone (SOZ) - is identified. This is typically performed through visual assessment of EEG recordings; however, this is time consuming and prone to subjective inconsistency. Automated SOZ localisation algorithms provide objective identification of the SOZ by highlighting changes in signal features associated with seizure onset. In this work we investigate how methodological differences in such algorithms can result in different SOZ localisations. We analysed ictal intracranial EEG (icEEG) recordings in 16 subjects (100 seizures) with drug-resistant epilepsy from the SWEZ-ETHZ public database. Through our analysis, we identified a series of key methodological differences that must be considered when designing or selecting a SOZ localisation algorithm. These differences were demonstrated using three distinct algorithms that capture different, but complementary, seizure onset features: Imprint, Epileptogenicity Index (EI), and Low Entropy Map (LEM). We assessed methodological differences at each Decision Point, comparing the resultant SOZ localisations. Our independent application of all three algorithms to the same ictal icEEG dataset revealed low agreement between algorithms: 27-60% of seizure onsets were as minimally or non-overlapping (<25% overlap). Therefore, we investigated the effect of three key methodological differences (or Decision Points). Changes at each Decision Point were found to result in significantly different SOZ localisations (p<0.05). Our results demonstrate how seemingly small methodological changes can result in large differences in SOZ localisations. We propose that key Decision Points must be considered when using or designing a SOZ localisation algorithm.
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