Identifying sleep spindles with multichannel EEG and classification optimization.

ABSTRACT: Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroencephalography (EEG). Manual classification is time consuming and is susceptible to low inter-rater agreement. This could be improved using an automated approach. This study presents an optimized filter based and thresholding (FBT) model to set up a baseline for comparison to evaluate machine learning models using naïve features, such as raw signals, peak frequency, and dominant power. The FBT model allows us to formally define sleep spindles using signal processing but may miss examples most human scorers would agree are spindles. Machine learning methods in theory should be able to approach performance of human raters but they require a large

SUBMITTER: Mei N 

PROVIDER: S-EPMC5650544 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature