TY  - CONF
  - Greene BR, Boylan GB, Marnane WP, Lightbody G, Connolly S
  - 30th Annual International IEEE EMBS Conference
  - Automated single channel seizure detection in the neonate.
  - 2008
  - August
  - Published
  - 1
  - ()
  - EEG; neonatal seizure; seizure detection
  - 915
  - 918
  - Vancouver, Canada
  - 20-AUG-08
  - 24-AUG-09
  - Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with poor long-term outcome. EEG is considered the gold standard for identification of all neonatal seizures, reducing the number of EEG electrodes required would reduce patient handling and allow faster acquisition of data. A method for automated neonatal seizure detection based on two carefully chosen cerebral scalp electrodes but trained using multi-channel EEG is presented. The algorithm was developed and tested using a multi-channel EEG dataset containing 411 seizures from 251.9 hours of EEG recorded from 17 full-term neonates. Automated seizure detection using a variety of bipolar channel derivations was investigated. Channel C3-C4 yielded correct detection of 90.77% of seizures with a false detection rate of 9.43%. This compares favourably with a multi-channel seizure detection method which detected 81.03% of seizures with a false detection rate of 3.82%.
  - 10.1109/IEMBS.2008.4649303
  - Science Foundation Ireland
  - 05/PICA/1836
DA  - 2008/08
ER  - 

@inproceedings{V58462180,
   = {Greene BR,  Boylan GB and  Marnane WP,  Lightbody G and  Connolly S },
   = {30th Annual International IEEE EMBS Conference},
   = {{Automated single channel seizure detection in the neonate.}},
   = {2008},
   = {August},
   = {Published},
   = {1},
   = {()},
   = {EEG; neonatal seizure; seizure detection},
  pages = {915--918},
   = {Vancouver, Canada},
  month = {Aug},
   = {24-AUG-09},
   = {{Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with poor long-term outcome. EEG is considered the gold standard for identification of all neonatal seizures, reducing the number of EEG electrodes required would reduce patient handling and allow faster acquisition of data. A method for automated neonatal seizure detection based on two carefully chosen cerebral scalp electrodes but trained using multi-channel EEG is presented. The algorithm was developed and tested using a multi-channel EEG dataset containing 411 seizures from 251.9 hours of EEG recorded from 17 full-term neonates. Automated seizure detection using a variety of bipolar channel derivations was investigated. Channel C3-C4 yielded correct detection of 90.77% of seizures with a false detection rate of 9.43%. This compares favourably with a multi-channel seizure detection method which detected 81.03% of seizures with a false detection rate of 3.82%.}},
   = {10.1109/IEMBS.2008.4649303},
   = {Science Foundation Ireland},
   = {05/PICA/1836},
  source = {IRIS}
}