Analyzing respiratory effort amplitude for automated sleep stage classification

Long, Xi; Foussier, Jérôme; Fonseca, Pedro; Haakma, Reinder; Aarts, RM Ronald

doi:10.1016/j.bspc.2014.08.001

Cited by 69 publications

(53 citation statements)

References 28 publications

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“…The corresponding cardiac and cardiorespiratory features were only extracted for a given epoch if the sum of the length of all detected R-R intervals in the window used to extract each feature was equal or larger than 50% of the length of that window. Regarding the RIP signal, peaks and troughs were first detected based on the sign change of the respiratory effort signal slope, and then marked as false detections if (1) the sum of two successive peak-to-trough intervals was less than the median of all intervals or if (2) the peak-totrough distance was less than 15% of the median of all intervals [24]. Corresponding respiratory features were only extracted for epochs where the window used to extract a feature did not have false peak/trough detections.…”

Section: B Feature Extractionmentioning

confidence: 99%

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Fonseca

Teuling

Long

et al. 2017

IEEE J. Biomed. Health Inform.

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• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. for regular subjects) for REM, and 85.69% and 0.51 (90.42% and 0.52 for regular subjects) for wake. In comparison with the stateof-the-art, and having been tested on a much larger data set, the classifier was found to outperform most of the work reported in the literature for some of the tasks, in particular for subjects with regular sleep architecture. It achieves a comparable accuracy for N3, higher accuracy and kappa for REM, and higher accuracy and comparable kappa for NREM than the best performing classifiers described in literature.

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Section: B Feature Extractionmentioning

confidence: 99%

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Fonseca

Teuling

Long

et al. 2017

IEEE J. Biomed. Health Inform.

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“…Since this study aimed at examining whether our boundary adaptation method can help improve the classification results, we only used the respiratory spectral features. In addition to them, many other existing respiratory (non-spectral) features have been used for sleep stage classification such as time domain features [5], amplitude features [6], and non-linear features based on self-similarity and sample entropy [7], [14]. Including those features for sleep/wake detection merits further investigation.…”

Section: Resultsmentioning

confidence: 99%

“…It has been increasingly used for sleep stage classification in recent years [5], [6], [7] as long as it can be unobtrusively obtained with e.g., textile bed sensors [8], Doppler radar [9], and camera [10]. For that purpose, many respiratory features have been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Improving sleep/wake detection via boundary adaptation for respiratory spectral features

Long

Haakma

Rolink

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Citation for published version (APA):Long, X., Haakma, R., Rolink, J., Fonseca, P., & Aarts, R. M. (2015). Improving sleep/wake detection via boundary adaptation for respiratory spectral features. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'15), 25-29 August 2015, Milan, Italy (pp. 374-377 Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publicationGeneral rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at: openaccess@tue.nl providing details and we will investigate your claim. Abstract-In previous work, respiratory spectral features have been successfully used for sleep/wake detection. They are usually extracted from several frequency bands. However, these traditional bands with fixed frequency boundaries might not be the most appropriate to optimize the sleep and wake separation. This is caused by the between-subject variability in physiology, or more specifically, in respiration during sleep. Since the optimal boundaries may relate to mean respiratory frequency over the entire night. Therefore, we propose to adapt these boundaries for each subject in terms of his/her mean respiratory frequency. The adaptive boundaries were considered as those being able to maximize the separation between sleep and wake states by means of their mean power spectral density (PSD) curves overnight. Linear regression models were used to address the association between the adaptive boundaries and mean respiratory frequency based on training data. This was then in turn used to estimate the adaptive boundaries of each test subject. Experiments were c...

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“…Alternatives need to be established to explore sleep in more convenient environments, for example at home. Several studies show that it is possible to perform sleep staging to a certain extent using cardio-respiratory signals and body movements [4,6,25,30,31,41]. For example frequency and time-frequency analysis are applied on cardiac data to determine sleep or sleep-related disorders, such as sleep disordered breathing or sleep apnea [17,23].…”

Section: Introductionmentioning

confidence: 99%