Sleep Staging Based on Signals Acquired Through Bed Sensor

Kortelainen, Juha; Méndez, Martín O.; Bianchi, Anna Maria; Matteucci, Matteo; Cerutti, S.

doi:10.1109/titb.2010.2044797

Cited by 179 publications

(101 citation statements)

References 20 publications

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“…Classification is generally done using an extensive set of features. Many different classifiers have been tested over the years, such as linear discriminants (LD) [6], [8], [9], hidden Markov models (HMM) [10], and support vector machines (SVM) [3]. For many classification tasks, the LD classifier was found to be amongst the best performing.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Beattie et al described a system using force measurement cells under the bed feet to classify critical breathing events [6] in the form of central apnea and obstructive apnea / hypopnea. Other systems are used to recognize human body postures in nursing beds to detect motion [7] or to analyze different states during sleep [8]. Furthermore, it is possible to predict the risk of decubitus pressure sores [9] or fall out of nursing beds [10].…”

Section: Introductionmentioning

confidence: 99%

MoveHN-A database to support the development of motion based biosignal processing systems

Kitzig

Schroter

Naroska

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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-In the field of signal processing, pattern recognition and also modeling and simulation, it is often necessary to use large data sets. These allow reliable, independent and test case spanning development of algorithms or even of complete systems. The data is usually taken from existing data sets such as TIMIT for speech recognition and processing or EPILEPSIAE to develop algorithms for epileptic seizure prediction, to give just two examples. Apart from the fact that some of these databases imply a considerable cost factor and thus are not accessible to all research groups, even greater problems arise if no data is available at all. In the field of speech recognition, this problem was solved more or less by creating databases. In the area of biosignal processing with a focus on the functionalization of furniture for care and clinical facilities, there is still a need for large data sets. This was also the case with biomechanical modeling of functionalized furniture, since up to now none or few data on the human movement sequences were available. In order to overcome this deficiency, the following paper presents a new database of motion patterns, which is intended to support the development of algorithms for motion detection as well as modeling biosignal processing. The database can be used and downloaded by any interested researcher for free.

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“…Several automatic wake-REM-NREM classifiers have been proposed which use multiple physiological signals including actigraphy, electrocardiogram (ECG), and respiratory effort [2], [4]. These signals contain information from which different sleep states can be derived [2], [5].…”

Section: Introductionmentioning

confidence: 99%

Respiration amplitude analysis for REM and NREM sleep classification

Long

Foussier

Fonseca

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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 rapideye-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.

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Sleep Staging Based on Signals Acquired Through Bed Sensor

Cited by 179 publications

References 20 publications

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

MoveHN-A database to support the development of motion based biosignal processing systems

Respiration amplitude analysis for REM and NREM sleep classification

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