TY - CHAP
T1 - Online EEG channel weighting for detection of seizures in the neonate
AU - Temko, Andriy
AU - Lightbody, Gordon
AU - Boylan, Geraldine
AU - Marnane, William
PY - 2011
Y1 - 2011
N2 - A framework for online dynamic channel weighting is developed for the task of EEG-based neonatal seizure detection. The channel weights are computed on-the-fly by combining the up-to-now patient-specific history and the clinically-derived prior channel importance. These estimated time-varying weights are introduced within a Bayesian probabilistic framework to provide a channel-specific and thus patient-adaptive seizure classification scheme. Validation results on one of the largest clinical datasets of neonatal seizures confirm the utility of the proposed channel weighting for the SVM-based detector recently developed by this research group. Exploiting the channel weighting, the precision-recall area can be drastically increased (up to 25%) for the most difficult patients, with the average increase from 81.0% to 84.42%. It is also shown that the increase in performance with channel weighting is proportional to the time the patient is observed.
AB - A framework for online dynamic channel weighting is developed for the task of EEG-based neonatal seizure detection. The channel weights are computed on-the-fly by combining the up-to-now patient-specific history and the clinically-derived prior channel importance. These estimated time-varying weights are introduced within a Bayesian probabilistic framework to provide a channel-specific and thus patient-adaptive seizure classification scheme. Validation results on one of the largest clinical datasets of neonatal seizures confirm the utility of the proposed channel weighting for the SVM-based detector recently developed by this research group. Exploiting the channel weighting, the precision-recall area can be drastically increased (up to 25%) for the most difficult patients, with the average increase from 81.0% to 84.42%. It is also shown that the increase in performance with channel weighting is proportional to the time the patient is observed.
UR - https://www.scopus.com/pages/publications/84055217217
U2 - 10.1109/IEMBS.2011.6090358
DO - 10.1109/IEMBS.2011.6090358
M3 - Chapter
C2 - 22254591
AN - SCOPUS:84055217217
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1447
EP - 1450
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -