EpilepsyNCT05384782
Computational Decision Support in Epilepsy Using Retrospective EEG
An observational study of Epilepsy, run by Cornwall Partnership NHS Foundation Trust at 1 site in United Kingdom.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Epilepsy
- How many people
- 825 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Dec 2019to 31 Mar 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Data will be collected across multiple sites within the NHS. At each site the local direct care team within the Neurology clinics will be performing the participant identification
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Cornwall Partnership NHS Foundation Trust
Bodmin, Cornwall, PL31 2QN
About this study
Official title: Retrospective Analysis of Resting-State EEG in the Diagnosis of Epilepsy to Validate a Computational Biomarker for Seizure Susceptibility
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.
Read the detailed description
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.
- Conditions
- Epilepsy
- Keywords
- Epilepsy
- Medical subject headings
- Epilepsy
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 31/12/2022
Secondary outcomes (1)
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.
Measured: 31/12/2022
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 May 2022
- Study start1 Dec 2019
- Primary completion31 Dec 2021
- Study completion31 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for EpilepsyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 825 participants (actual)
Sponsor and investigators
- Lead sponsor
- Cornwall Partnership NHS Foundation Trust
- Sponsor type
- Research network
- Collaborators
- Neuronostics Ltd
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05384782
- Sponsor's protocol ID
- Version 10
- Other IDs
- 260729
- Status last verified
- May 2022
- First submitted
- 4 May 2022
- Last updated
- 20 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Retrospective Analysis of Resting-State EEG in the Diagnosis of Epilepsy to Validate a Computational Biomarker for Seizure Susceptibility
- ClinicalTrials.gov ID
- NCT05384782
- Acronym / sponsor ID
- Version 10
- Conditions
- Epilepsy
- Keywords
- Epilepsy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 825 participants (actual)
- Lead sponsor
- Cornwall Partnership NHS Foundation TrustResearch network
- Collaborators
- Neuronostics Ltd
- Study start
- 1 Dec 2019 (actual)
- Primary completion
- 31 Dec 2021 (actual)
- Study completion
- 31 Mar 2022 (actual)
- First posted
- 20 May 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Data will be collected across multiple sites within the NHS. At each site the local direct care team within the Neurology clinics will be performing the participant identification
Key inclusion criteria (8 listed)
- 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:
- 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)
- 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
- 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)
Key exclusion criteria (1 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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
Time frame: 31/12/2022
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: 1. A global marker that quantifies how easy it is for the entire network to make the transition to seizure activity in the model 2. 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.
1 secondary outcome
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.
Time frame: 31/12/2022
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 May 2022.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Epilepsy, run by Cornwall Partnership NHS Foundation Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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 (time frame: 31/12/2022). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 May 2022; AzTrials last refreshed its copy on 2 Nov 2026.