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Objective
The objective of this study was to evaluate the accuracy of a semiautomated classification of nocturnal seizures using a hybrid system consisting of an artificial intelligence‐based algorithm, which selects epochs with potential clinical relevance to be reviewed by human experts.
Methods
Consecutive patients with nocturnal motor seizures admitted for video‐electroencephalographic long‐term...
Objective
Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection.
Methods
We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The...
Objective
To evaluate the diagnostic performance of artificial intelligence (AI)–based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20‐min) electroencephalography (EEG) recordings.
Methods
We evaluated two approaches: a fully automated one and a hybrid approach, where three human raters applied an operational IED definition to assess the automated...
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