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.
- Exploration of Digital Health
- Artificial Intelligence; EEG; Epilepsy; Interictal Epileptiform Discharges; Automated Detection; Sleep