Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis. Issue 7 (July 2021) - Fürbass, Franz; Koren, Johannes; Hartmann, Manfred; Brandmayr, Georg; Hafner, Sebastian; Baumgartner, Christoph
Highlights: Data-driven classification of patients with epilepsy based on their temporal activation patterns of interictal discharges. AI-based detection and clustering resulted in five distinct activation patterns showing interrelations with sleep and seizures. Clinical applicable assignment rules for taxonomy of patients with epilepsy open new pathways in diagnosis and research. Abstract: Objective: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns. Methods: We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence. Results: Five sub-cohorts with similar IED activation patterns were found: "Sporadic " (14%, n = 10) without or few IEDs, "Continuous " (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation " (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep " (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal. Conclusions: Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures. Significance: This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.