SozRank: A new approach for localizing the epileptic seizure onset zone

Yonathan Murin, Jeremy Kim, Josef Parvizi and Andrea Goldsmith

PLOS Computational Biology, 2018, vol. 14, issue 1, 1-26

Abstract: Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. For patients with focal seizures that cannot be treated with antiepileptic drugs, the common treatment is a surgical procedure for removal of the seizure onset zone (SOZ). In this work we introduce an algorithm for automatic localization of the seizure onset zone (SOZ) in epileptic patients based on electrocorticography (ECoG) recordings. The proposed algorithm builds upon the hypothesis that the abnormal excessive (or synchronous) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain. Thus, when this abnormal activity starts, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. The SOZ localization is executed in two steps. First, the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals. Then, the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase. Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists (success rate of 17 out of 19).Author summary: Epilepsy is a common neurological disorder characterized by abnormal electrical disturbances in the brain that result in transient occurrence of signs and/or symptoms, also known as seizures. In focal epilepsy, this electrical activity originates from a limited area in the brain, commonly referred to as the seizure onset zone (SOZ). For patients with focal epilepsy that cannot be treated with medications, the common treatment is a resective surgery to remove the SOZ. This work presents an algorithm for SOZ localization based on electrocorticography recordings. Such an automatic solution has the potential to increase the localization accuracy, to provide a validation of the neurologist’s SOZ region, and to ultimately reduce or eliminate the analysis time of the neurologist. Inference results for 19 patients show a close match between the SOZ inferred by the proposed algorithm and the SOZ estimated by expert neurologists.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005953

DOI: 10.1371/journal.pcbi.1005953

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