Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 10, Issue -, Pages 117-127Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2014.01.010
Keywords
EEG; Segmentation; Switching multiple models
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This work investigates the use of switching linear Gaussian state space models for the segmentation and automatic labelling of Stage 2 sleep EEG data characterised by spindles and K-complexes. The advantage of this approach is that it offers a unified framework of detecting multiple transient events within background EEG data. Specifically for the identification of background EEG, spindles and K-complexes, a true positive rate (false positive rate) of 76.04% (33.47%), 83.49% (47.26%) and 52.02% (7.73%) respectively was obtained on a sample by sample basis. A novel semi-supervised model allocation approach is also proposed, allowing new unknown modes to be learnt in real time. (C) 2014 Elsevier Ltd. All rights reserved.
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