Seizure detection with deep neural networks for review of two-channel electroencephalogram

Manfred Hartmann, Johannes Koren, Christoph Baumgartner, Jonas Duun-Henriksen, Gerhard Gritsch, Tilmann Kluge, Hannes Perko, Franz Fürbass

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung


Ultra-long-term electroencephalographic (EEG) registration using minimally invasive low-channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection. The model is trained using EEG recordings from 590 patients in a publicly available seizure database. These recordings are based on the full 10–20 electrode system and include seizure annotations created by reviews of the full set of EEG channels. Validation was performed using 48 scalp EEG recordings from an independent epilepsy center and consensus seizure annotations from three neurologists. For each patient, a three-electrode subgroup (two channels with a common reference) of the full montage was selected for validation of the two-channel model. Mean sensitivity across patients of 88.8% and false positive rate across patients of 12.9/day were achieved. The proposed training approach is of great practical relevance, because true recordings from low-channel devices are currently available only in small numbers, and the generation of gold standard seizure annotations in two EEG channels is often difficult. The study demonstrates that automatic seizure detection based on two-channel EEG data is feasible and review of ultra-long-term recordings can be made efficient and effective.
Seiten (von - bis)S34–S39
Volume64 (2023)
IssueSuppl 4
Frühes Online-Datum24 Apr. 2022
PublikationsstatusVeröffentlicht - 27 Dez. 2023
Veranstaltung3rd International Congress on Mobile Health Devices and Seizure Detection in Epilepsy - Scandic Copenhagen, Copenhagen, Dänemark
Dauer: 28 Okt. 202130 Okt. 2021

Research Field

  • Medical Signal Analysis


  • Seizure detection
  • Deep learning


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