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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.

We evaluated our approach on >5500 h of long‐term EEG recordings using two public datasets: the PhysioNet.org Children’s Hospital Boston–Massachusetts Institute of Technology (CHB‐MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually identified a set of similarly occurring morphological patterns (seizure signature) seen simultaneously over two different EEG channels, and within two randomly …