Computational Decision Support in Epilepsy Using Retrospective EEG

Study Overview
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Brief Summary
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The primary aim is to validate a set of computational biomarkers as potential decision support in epilepsy on a large cohort of study participants that were diagnosed with epilepsy and controls that ended up with another diagnosis (such as syncope or non-epileptic seizures). The goal is to examine if the methodology works robustly on this large cohort, and can theoretically contribute to the reduction of misdiagnosis rates. The secondary aim is to examine whether the computational biomarkers could contribute to reducing the waiting time and the number of clinical appointments needed before a final diagnosis is made.

Detailed Description
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Mathematical models provide a powerful and useful tool with which to identify and understand biological mechanisms that may lead to the risk of having seizures as well as how they generate, propagate and terminate (Wendling, 2005). Mathematical models that combine experimental and clinical detail at diverse scales have revealed the importance of many microscopic and macroscopic mechanisms in the generation of seizure-like activity, ranging from genetic and molecular mechanisms to changes in the excitability of neural populations leading to the generation of pathological oscillations (for review see Woldman & Terry (2015); Soltesz & Staley (2008)). Due to the increased availability of data recordings (EEG, MRI, MEG, CT, PET), there has been a significant increase in research studies that aim to identify novel biomarkers from these recordings with potential clinical value, using various different techniques (e.g. time-series analysis, computational modelling, machine learning). By combining mathematical and computational techniques, we have identified properties in the resting-state EEG (eyes closed, relaxed) of people with epilepsy that differ from those of controls as well as their first-degree relatives (Chowdhury et al., 2014). Developing these approaches and applying them to routine recordings from individuals with epilepsy against a control cohort (Schmidt et al., 2016) revealed levels of diagnostic accuracy similar to current general (i.e. non-specialist) neurology practices (60% sensitivity, 87% specificity, N=68). Crucially, our method correctly classified several subjects using their first EEG, whereas clinical diagnosis was confirmed only after prolonged telemetric recordings over many months. Since our methods and analysis depend on short segments of resting-state EEG only, its accuracy and efficacy do not rely on capturing epileptiform abnormalities, in contrast to the current use of EEG in diagnosing epilepsy. Since many EEGs return negative, clinicians are often faced with the problem of deciding on whether to opt for longer recordings of EEG or ambulatory or video EEG, which is currently the final method in the diagnostic cascade. This is time-consuming, expensive and relies on the availability and expertise of trained EEG-readers. By optimally interrogating short segments of background activity with mathematical and computational analysis, our methods, in the short term, provide additional evidence that could guide clinicians in future diagnostic steps.

Official Title
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Retrospective Analysis of Resting-State EEG in the Diagnosis of Epilepsy to Validate a Computational Biomarker for Seizure Susceptibility

Conditions
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Epilepsy

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subject was suspected of having had a seizure or epilepsy (fits, faints or funny turns), and as part of the diagnostic process one or more EEGs was recorded The subject ended up with a confirmed diagnosis of epilepsy or of the differential diagnosis such as syncope, or psychogenic seizures (diagnosis must have been at least 1 year ago, and not changed since) For each subject identified we would like to have all the available EEG files within the centre, with the following metadata: Primary meta-data (crucial): Age at the subject at time of each available EEG Treatment status at the time of each available EEG (including drug-load) Gender of the individual Ethnicity of the individual Confirmed diagnosis: details on the exact diagnosis made (syndrome and or condition) Secondary meta-data (optional): Aim of each available EEG at the time Information on whether any other conditions are present such as Alzheimer's disease, schizophrenia, Intellectual Disability If available: information on when the diagnosis was made If available: interpretation of each available EEG Specifics for the EEG recordings: Montage (10-20 preferred) Number of channels (minimum 19 channels) Referencing method (common average preferred) Format of the file (EDF preferred) Consistent channel labels for all EEGs provided from each centre Information concerning the time of day during the recording Information on the sampling frequency Faulty channels (not more than 2 preferred, all should be indicated though) Pre-processing details (information as to whether any filters were used, for example) Exclusion Criteria: Subject was not suspected of having had a seizure or epilepsy Unavailable information concerning the final diagnosis of the subject (epilepsy or other) Incomplete or unreliable meta-data, such as the age, gender and treatment-status at the time of the EEG recording (primary meta-data) Recordings which do not comply with inclusion criteria

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| To validate a set of computational biomarkers as potential decision support in epilepsy on a large cohort of study participants that were diagnosed with epilepsy and controls that ended up with another diagnosis | To each EEG recording, we apply an algorithm that automatically detects relevant segments to our analysis (free of artefacts). By combining the individually derived network structure with the mathematical model, we simulate a computer-generated EEG, which serves as a proxy for the original segment derived from the study participant. We then examine this computer-generated EEG by calculating two biomarkers: A global marker that quantifies how easy it is for the entire network to make the transition to seizure activity in the model A local marker that quantifies whether there are particular regions in the network that are particular prone to generating or participating in seizure activity in the model. | 31/12/2022 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| To examine whether the computational biomarkers could contribute to reducing the waiting time and the number of clinical appointments needed before a final diagnosis is made. | To each EEG recording, we apply an algorithm that automatically detects relevant segments to our analysis (free of artefacts). By combining the individually derived network structure with the mathematical model, we simulate a computer-generated EEG, which serves as a proxy for the original segment derived from the study participant. We then examine this computer-generated EEG by calculating two biomarkers: A global marker that quantifies how easy it is for the entire network to make the transition to seizure activity in the model A local marker that quantifies whether there are particular regions in the network that are particular prone to generating or participating in seizure activity in the model. | 31/12/2022 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Epilepsy