Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models

•We propose switching multiple models for automatic Stage 2 sleep EEG analysis.•This provides a unified framework for detection of multiple transient events.•This method is used to automatically segment EEG data and label multiple events.•Sleep spindles and K-complexes are successfully detected and labeled.•We extend the method to afford unsupervised learning of new models in real time.

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.