Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

ABSTRACT: Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. I

SUBMITTER: Bogaarts JG 

PROVIDER: S-EPMC5104774 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature