NDLI: Respiration amplitude analysis for REM and NREM sleep classification

Content Provider	IEEE Xplore Digital Library
Author	Xi Long Foussier, J. Fonseca, P. Haakma, R. Aarts, R.M.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Eindhoven Univ. of Technol., Eindhoven, Netherlands (Xi Long; Fonseca, P.; Aarts, R.M.) \|\| Philips Res., Eindhoven, Netherlands (Haakma, R.) \|\| Med. Inf. Technol., RWTH Aachen Univ., Aachen, Germany (Foussier, J.)
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 κ = 0.59 (overall accuracy of 87.6%) compared to that obtained without using these features (κ 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.
Starting Page	5017
Ending Page	5020
File Size	182719
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Member and Geographic Activities Board committees Sleep apnea Feature extraction Electrocardiography Heart rate variability Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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