Comparing Methodological Variations in Seizure Onset Localisation Algorithms using intracranial EEG

TL;DR

This study compares different algorithmic approaches for localizing seizure onset in intracranial EEG, revealing that methodological choices significantly affect the identified onset locations and emphasizing the need for careful decision point consideration.

Contribution

It systematically analyzes how methodological variations impact seizure onset localization algorithms, highlighting the importance of decision points in algorithm design and application.

Findings

Low agreement (27-60%) between different algorithms on seizure onset channels.

Methodological differences significantly influence localization results (r>0.3).

Key decision points include baseline definition, frequency component consideration, and electrodecrement analysis.

Abstract

During clinical treatment for epilepsy, the area of the brain thought to be responsible for pathological activity is identified. This identification is typically performed through visual assessment of EEG recordings; however, this is time consuming and prone to subjective inconsistency. Automated onset localisation algorithms provide objective identification of the onset location 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 onset locations being identified. We analysed ictal intracranial EEG (icEEG) recordings in 16 subjects (100 seizures) with drug-resistant epilepsy from the SWEZ-ETHZ public database. We identified a series of key methodological differences that must be considered when designing or selecting an onset localisation algorithm. These differences were demonstrated using three distinct algorithms that capture different, but complementary, seizure onset features: Imprint, Epileptogenicity Index, and Low Entropy Map. We assessed methodological differences (or Decision Points), and their impact on the identified onset locations. Our independent application of all three algorithms to the same ictal icEEG dataset revealed low agreement between them: 27-60% of onset channels showed minimal or no overlap. Therefore, we investigated the effect of three key differences: (i) how to define a baseline, (ii) whether low-frequency components are considered, and finally (iii) whether electrodecrement is considered. Changes at each Decision Point were found to substantially influence resultant onset channels (r>0.3). Our results demonstrate how seemingly small methodological changes can result in large differences in onset locations. We propose that key Decision Points must be considered when using or designing an onset localisation algorithm.