Respiration amplitude analysis for REM and NREM sleep classification
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 e...
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| creator | Xi Long Foussier, Jerome Fonseca, Pedro Haakma, Reinder Aarts, Ronald M. |
| description | 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. |
| doi_str_mv | 10.1109/EMBC.2013.6610675 |
| format | Conference Proceeding |
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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%). 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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%). 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Foussier, Jerome ; Fonseca, Pedro ; Haakma, Reinder ; Aarts, Ronald M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i344t-e6f0fd443b32148e4913e78cf208266359d76e9b7726bade99cd4d5c5115045f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Actigraphy</topic><topic>Adult</topic><topic>Algorithms</topic><topic>Automatic Data Processing</topic><topic>Discriminant Analysis</topic><topic>Electrocardiography</topic><topic>Feature extraction</topic><topic>Female</topic><topic>Healthy Volunteers</topic><topic>Heart rate variability</topic><topic>Humans</topic><topic>Linear Models</topic><topic>Male</topic><topic>Member and Geographic Activities Board committees</topic><topic>Polysomnography - methods</topic><topic>Reproducibility of Results</topic><topic>Respiration</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Sleep - physiology</topic><topic>Sleep apnea</topic><topic>Sleep, REM - physiology</topic><topic>Young Adult</topic><toplevel>online_resources</toplevel><creatorcontrib>Xi Long</creatorcontrib><creatorcontrib>Foussier, Jerome</creatorcontrib><creatorcontrib>Fonseca, Pedro</creatorcontrib><creatorcontrib>Haakma, Reinder</creatorcontrib><creatorcontrib>Aarts, Ronald M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xi Long</au><au>Foussier, Jerome</au><au>Fonseca, Pedro</au><au>Haakma, Reinder</au><au>Aarts, Ronald M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Respiration amplitude analysis for REM and NREM sleep classification</atitle><btitle>2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</btitle><stitle>EMBC</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2013-01-01</date><risdate>2013</risdate><volume>2013</volume><spage>5017</spage><epage>5020</epage><pages>5017-5020</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><eisbn>1457702169</eisbn><eisbn>9781457702167</eisbn><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%). 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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Actigraphy Adult Algorithms Automatic Data Processing Discriminant Analysis Electrocardiography Feature extraction Female Healthy Volunteers Heart rate variability Humans Linear Models Male Member and Geographic Activities Board committees Polysomnography - methods Reproducibility of Results Respiration Signal Processing, Computer-Assisted Sleep - physiology Sleep apnea Sleep, REM - physiology Young Adult |
| title | Respiration amplitude analysis for REM and NREM sleep classification |
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