Development of an Epileptic Seizure Detection Algorithm by Continuous Analysis of the Electrocardiogram
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Enrollment
- 40
- Locations
- 4
- Primary Endpoint
- ROC curve for seizure detection
Study Overview
Brief Summary
The aim of our project is to develop an epileptic seizure detection algorithm based on the the continuous analysis of the Electrocardiogram
Detailed Description
In a first step, the team will build a multi-parametric algorithm on existing records issued from the Epilepsy Monitoring Units. Each selected record will be analyzed by EMU medical team in order to detect seizure on the EEG. The algorithm will adapt in order to obtain the best sensitivity and specificity regarding seizure detection.
in a second step,algorithm will validate on a cohort of other recordings in patients with epilepsy
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •patients with epilepsy
- •with a long-term video-EEG recording
- •1 (or more) seizure (s) recorded
- •onset seizure clearly defined on the basis of the EEG
Exclusion Criteria
- •bad quality of the Electrocardiogram recording
- •pace-maker
- •non sinus rhythm
Outcomes
Primary Outcomes
ROC curve for seizure detection
Time Frame: 24 months
ROC curve gives the relation between detection probability and false alarm probability of the algorithm that will be developped
Secondary Outcomes
- Time of detection of seizures(24 months)
- ROC curve for Psychogenic seizure detection(24 months)