| Literature DB >> 22255730 |
Manfred M Hartmann1, Franz Fürbass, Hannes Perko, Ana Skupch, Katharina Lackmayer, Christoph Baumgartner, Tilmann Kluge.
Abstract
An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.Entities:
Mesh:
Year: 2011 PMID: 22255730 DOI: 10.1109/IEMBS.2011.6091506
Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN: 1557-170X