Objective Long‐term automatic detection of focal seizures remains one of the major
challenges in epilepsy due to the unacceptably high number of false alarms from state‐of‐
the‐art methods. Our aim was to investigate to what extent a new patient‐specific approach
based on similarly occurring morphological electroencephalographic (EEG) signal patterns
could be used to distinguish seizures from nonseizure events, as well as to estimate its
maximum performance. Methods We evaluated our approach on> 5500 h of long‐term EEG …

Objective: Long-term automatic detection of focal seizures remains one of the major
challenges in epilepsy due to the unacceptably high number of false alarms from state-o f-
the-a rt methods. Our aim was to investigate to what extent a new patient-s pecific approach
based on similarly occurring morphological electroencephalographic (EEG) signal patterns
could be used to distinguish seizures from nonseizure events, as well as to estimate its
maximum performance. Methods: We evaluated our approach on> 5500 h of long-term EEG …