Abstract
Objective
Typical absence seizures are underreported. We aimed to improve patient care using a wearable electroencephalograph (wEEG) at home and assess a machine learning (ML) pipeline for absence detection.
Methods
Patients with typical absences used a wEEG device 12–24 h 1 week after antiseizure medication (ASM) adjustments. Three-hertz generalized spike–wave discharges (SWDs) ≥ 3 s were used as absence surrogates. After manual inspection, we used the results to guide medical treatment. The outcomes were seizure freedom, number of consecutive measurements without relapse, and side effects. Afterward, we ...
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