Respiration amplitude analysis for REM and NREM sleep classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

14 Citations (Scopus)
316 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13), 2-7 September, Osaka, Japan
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages5017-5020
ISBN (Print)978-1-4577-0216-7
DOIs
Publication statusPublished - 2013
Event35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 3 Jul 20137 Jul 2013
Conference number: 35

Conference

Conference35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Abbreviated titleEMBC 2013
Country/TerritoryJapan
CityOsaka
Period3/07/137/07/13

Fingerprint

Dive into the research topics of 'Respiration amplitude analysis for REM and NREM sleep classification'. Together they form a unique fingerprint.

Cite this