Article

Multi-channel EEG based neonatal seizure detection.

Sch. of Electr., Electron. & Mech. Eng., Univ. Coll. Dublin, Ireland.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2006; 1:4679-84. DOI:10.1109/IEMBS.2006.260461
Source: PubMed

ABSTRACT A multi-channel method for patient specific and patient independent, EEG based neonatal seizure detection is presented. Two classifier configurations are proposed and tested, along with a number of classifier models. Existing methods for neonatal seizure detection have been empirical threshold based or based on a single EEG channel. The optimum patient specific classifier for EEG based neonatal seizure detection was found to be an Early Integration configuration employing a linear discriminant classifier model. This yielded a mean classification accuracy of 74.66% for 11 neonatal records. The optimum patient independent classifier was an Early Integration configuration with a linear discriminant classifier model giving a mean accuracy of 72.81%

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Keywords

11 neonatal records
 
classifier configurations
 
classifier models
 
Existing methods
 
Integration configuration
 
linear discriminant classifier model
 
mean classification accuracy
 
multi-channel method
 
neonatal seizure detection
 
optimum patient independent classifier
 
optimum patient specific classifier
 
single EEG channel