Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG

Wang, Yiping; Yang, Yanfeng; Li, Si; Su, Zichen; Guo, Jinglin; Wei, Penghu; Huang, Jinguo; Kang, Guixia; Zhao, Guoguang

doi:10.3390/bioengineering9120769

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…We also tested Support Vector Machine and Linear Discriminant Analysis classifiers, but both provided poorer classification. Our approach of using a range of simple-to-complex features is different from older localisation methods which used one main feature such as high-to-low frequency energy ratio 13,16 , and aligns with later studies which have combined several features for EZ localisation 17,31,61,62 , temporal detection of seizures 63 and quantification of seizure severity 64 .…”

Section: Discussionmentioning

confidence: 99%

Generalisability of epileptiform patterns across time and patients

Karimi-Rouzbahani,

McGonigal

2023

Preprint

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Complexity of epileptogenic zone (EZ) localisation contributes to failure of surgical resection to produce seizure freedom. This is to some extent a result of distinct patterns of epileptiform activity between (i.e., interictal) and during seizures (i.e., ictal) and their diversity across patients. This often leads to suboptimal localisation based on inspection of electroencephalography (EEG) features. We asked two under-investigated questions. First, whether neural signal reflecting epileptogenicity would be generalisable from interictal to ictal time window within each patient. This would be critical for patients who are monitored in hospital without having a seizure to help with EZ localisation, and more generally for understanding the predictive power of resting state (interictal) EEG data in determining EZ. Second, whether epileptiform patterns would generalise across patients, and if so, which aspects of those patterns are the most generalisable. We used an intracranial EEG dataset that included fifty-five patients with lesional and non-lesional pathology, who had subsequently undergone cortical resection in frontal or temporal lobe with different levels of seizure freedom. We extracted a large set of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals recorded during interictal and ictal time windows. We fed those features to decision-tree classifiers for EZ localisation and to quantify the diversity of ictal and interictal epileptiform patterns through a cross-time and cross-patient generalisation procedure. We observed significant evidence (Bayes factor >> 10) for generalisability of patterns from interictal to ictal time windows across patients, which were dominantly reflected in signal power and high-frequency network-based features. Majority of patients showed consistent patterns of epileptogenicity across interictal and ictal time windows, reflected in above-chance area-under-curve (mean AUC = 0.6). We observed significant evidence (Bayes factor >> 10) that signal features of epileptogenic regions could generalise across patients in both interictal and ictal time windows with significant evidence for higher generalisability in ictal than interictal time window (mean AUC 0.75 vs. 0.59; Bayes factor >> 10). While signal power and moment features were the most contributory to the cross-patient generalisation in the interictal window, signal complexity features were the most contributory in the ictal window. These results provide new insights about features of epileptic neural activity that generalise across interictal-ictal time windows and patients, which can have implications for both qualitative and quantitative EZ localisation. The explainable machine-learning pipeline developed here can guide future developments in epilepsy investigations.

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Section: Discussionmentioning

confidence: 99%

Generalisability of epileptiform patterns across time and patients

Karimi-Rouzbahani,

McGonigal

2023

Preprint

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Generalisability of epileptiform patterns across time and patients

Karimi-Rouzbahani,

McGonigal

2024

Sci Rep

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The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.

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Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG

Cited by 2 publications

References 35 publications

Generalisability of epileptiform patterns across time and patients

Generalisability of epileptiform patterns across time and patients

Generalisability of epileptiform patterns across time and patients

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