Deep Learning for Detection of Focal Epileptiform Discharges from Scalp EEG Recordings

Since the first recording of the human EEG by Hans Berger in 1924 (Berger, 1929), visual assessment by trained experts has remained the gold standard, despite the digital nature of EEG recordings. While visual analysis has proven to be invaluable (Schomer and Lopes da Silva, 2011), it has various limitations, including time-consuming review times, long learning curves, inter-observer variability and the need for specialized personnel (Lodder and van Putten, 2014; Faught, 1993). Visual analysis limits widespread use of long-term ambulatory recordings, although it has been established that this may improve diagnostic sensitivity for detecting interictal discharges (Askamp and van Putten, 2014; Geut et al., 2017).

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