Patients suspected of epilepsy or other conditions with impaired consciousness or cognition Nervous System Diseases Epilepsy
Conditions
Interventions
EEGs will be automatically assessed by the previously developed autoSCORE algorithm using pre-defined detection thresholds. The algorithm first distinguishes between normal and abnormal recordings. Th
Sponsors
Holberg EEG AS
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: The EEGs to be selected for this study have not been part of the training dataset to develop the autoSCORE. The datasets are distributed between the EEGs arriving from Haukeland University Hospital, Danish Epilepsy Centre Filadelfia and Mayo Clinic. Although there is no scientific reason to consider that ethnicities or geographical origin of the EEG, nor the software used for acquisition influences the results, in order to avoid any such potential bias, the study design has addressed this by using EEGs from different geographies. Age range: 35% under 16 years (pediatric population), 65% over 16 years (adult population).
Exclusion criteria
Exclusion criteria: 1. Neonatal 2. EEGs reported with rhythmic and periodic patterns in critically ill patients
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Sensitivity, specificity, accuracy, positive predictive value, negative predictive value of autoSCORE compared with the majority-consensus scoring of the human experts, calculated using a balanced sample of 100 randomly selected EEGs at a single timepoint | — |
Secondary
| Measure | Time frame |
|---|---|
| Calculated using a balanced sample of 100 randomly selected EEGs at a single timepoint: 1. Inter-test agreement (autoSCORE vs human experts) in the large, independent dataset 2. Performance of autoSCORE at identifying recordings with epileptiform abnormalities (both focal and generalized) compared with the commercially available spike-detector software packages | — |
Countries
Denmark, Norway, United States of America
Outcome results
None listed