Abstract
In previous work, single-night polysomnography recordings (PSG) of respiratory effort and electrocardiogram (ECG) signals combined with actigraphy were used to classify sleep and wake states. In this study, we aim at classifying rapid-eye-movement (REM) and non-REM (NREM) sleep states. Besides the existing features used for sleep and wake classification, we propose a set of new features based on respiration amplitude. This choice is motivated by the observation that the breathing pattern has a more regular amplitude during NREM sleep than during REM sleep. Experiments were conducted with a data set of 14 healthy subjects using a linear discriminant (LD) classifier. Leave-one-subject-out cross-validations show that adding the new features into the existing feature set results in an increase in Cohen’s Kappa coefficient to a value of kappa = 0.59 (overall accuracy of 87.6%) compared to that obtained without using these features (kappa of 0.54 and overall accuracy of 86.4%). In addition, we compared the results to those reported in some other studies with different features and signal modalities.
Original language | English |
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Title of host publication | Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13), 2-7 September, Osaka, Japan |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 5017-5020 |
ISBN (Print) | 978-1-4577-0216-7 |
DOIs | |
Publication status | Published - 2013 |
Event | 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan Duration: 3 Jul 2013 → 7 Jul 2013 Conference number: 35 |
Conference
Conference | 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 |
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Abbreviated title | EMBC 2013 |
Country/Territory | Japan |
City | Osaka |
Period | 3/07/13 → 7/07/13 |
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Dive into the research topics of 'Respiration amplitude analysis for REM and NREM sleep classification'. Together they form a unique fingerprint.Research areas
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Sleep Medicine
van Gilst, M. M. (Content manager) & van der Hout-van der Jagt, M. B. (Content manager)
Impact: Research Topic/Theme (at group level)