Clinical utility of an ambulatory absence seizure detection system

Novel absence seizure detection systems have emerged in recent years, showing consistent results in ambulatory monitoring. This study aimed to provide real-world evidence of how these systems support clinical decision-making. At the Freiburg Epilepsy Center, 16 patients diagnosed with genetic generalized epilepsy were provided with a human-driven, AI-assisted electrographic absence seizure detection service utilizing a wearable EEG device during a three-day ambulatory monitoring period. We retrospectively analyzed and categorized therapeutic decisions made by neurologists based on their interpretation of the service's results in the context of routine clinical care. Electrographic seizure frequency and mean seizure duration were compared based on whether an intervention in treatment or care management was performed. Additionally, we assessed patients' self-reporting performance and compared agreement between seizure activity reported by patients and that recorded by the service. Electrographic absence seizure patterns were observed in 12 patients, with a total of 459 events recorded. Agreement with seizure diary data was low (Cohen's κ = 0.14). The data obtained influenced neurologists' decisions on confirming therapeutic success, adjusting treatment plans, recommending inpatient video-EEG monitoring, and performing differential diagnostic evaluations. Patients with an intervention exhibited higher electrographic seizure frequencies and longer mean seizure durations. The results underscore the technology's potential in supporting clinical decisions by enhancing the clinical picture. PLAIN LANGUAGE SUMMARY: We used a wearable EEG device to quantify absence seizures in people with epilepsy during everyday activities. The system helped neurologists to identify and assess seizure activity objectively. Our findings show that this can support treatment decisions, highlighting the potential to improve clinical care.