Investigation of automated sleep staging from cardiorespiratory signals regarding clinical applicability and robustness
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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In: Biomedical Signal Processing and Control, 71, S. 103047-
- Event
-
Veröffentlichung
- (where)
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Augsburg
- (who)
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Universität Augsburg
- (when)
-
2022
- Event
-
Veröffentlichung
- (who)
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Elsevier BV
- (when)
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2022
- Creator
- DOI
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10.1016/j.bspc.2021.103047
- URN
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urn:nbn:de:bvb:384-opus4-996082
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:49 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Goldammer, Miriam
- Zaunseder, Sebastian
- Brandt, Moritz D.
- Malberg, Hagen
- Gräßer, Felix
- Universität Augsburg
- Elsevier BV
Time of origin
- 2022
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Specializing CNN models for sleep staging based on heart rate
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Nocturnal ventricular repolarization lability predicts cardiovascular mortality in the Sleep Heart Health Study
Individualized sleep stage classification from cardiorespiratory features
Feature selection for sleep analysis using heart rate variability and fMRI functional connectivity
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Mess- und Analyseverfahren für das medizinische Monitoring
Specializing CNN models for sleep staging based on heart rate
Unobtrusive acquisition of cardiorespiratory signals: available techniques and perspectives for sleep medicine
Nocturnal ventricular repolarization lability predicts cardiovascular mortality in the Sleep Heart Health Study
Individualized sleep stage classification from cardiorespiratory features
Feature selection for sleep analysis using heart rate variability and fMRI functional connectivity
Data-based Therapy Recommender Systems
Adaptive Gaussian mixture model driven level set segmentation for remote pulse rate detection
CinC challenge - assessing the usability of ECG by ensemble decision trees
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