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/library/oar/handle/123456789/18639| Title: | Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models |
| Authors: | Camilleri, Tracey A. Camilleri, Kenneth P. Fabri, Simon G. |
| Keywords: | Electroencephalography Sleep -- Stages Sleep -- Stages -- Measurement -- Data processing |
| Issue Date: | 2014 |
| Publisher: | Elsevier Ltd. |
| Citation: | Camilleri, T. A., Camilleri, K. P., & Fabri, S. G. (2014). Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models. Biomedical Signal Processing and Control, 10, 117-127. |
| Abstract: | 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 advan- tage 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. |
| URI: | https://www.um.edu.mt/library/oar//handle/123456789/18639 |
| Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models.pdf Restricted Access | 1.4 MB | Adobe PDF | View/Open Request a copy |
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