Contactless Camera-Based Sleep Staging: The HealthBed Study Article Swipe
Fokke van Meulen
,
Angela Grassi
,
Leonie van den Heuvel
,
Sebastiaan Overeem
,
Merel M. van Gilst
,
Johannes van Dijk
,
Henning Maass
,
Mark van Gastel
,
Pedro Fonseca
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering10010109
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering10010109
Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake–N1/N2/N3–REM) and 4 class (Wake–N1/N2–N3–REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.
Related Topics
Concepts
Polysomnography
Photoplethysmogram
Sleep (system call)
Sleep Stages
Computer science
Heart rate variability
Artificial intelligence
Wearable computer
Computer vision
Medicine
Heart rate
Embedded system
Electroencephalography
Psychiatry
Radiology
Blood pressure
Operating system
Filter (signal processing)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering10010109
- https://www.mdpi.com/2306-5354/10/1/109/pdf?version=1674034630
- OA Status
- gold
- Cited By
- 33
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4316039052
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4316039052Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/bioengineering10010109Digital Object Identifier
- Title
-
Contactless Camera-Based Sleep Staging: The HealthBed StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-12Full publication date if available
- Authors
-
Fokke van Meulen, Angela Grassi, Leonie van den Heuvel, Sebastiaan Overeem, Merel M. van Gilst, Johannes van Dijk, Henning Maass, Mark van Gastel, Pedro FonsecaList of authors in order
- Landing page
-
https://doi.org/10.3390/bioengineering10010109Publisher landing page
- PDF URL
-
https://www.mdpi.com/2306-5354/10/1/109/pdf?version=1674034630Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2306-5354/10/1/109/pdf?version=1674034630Direct OA link when available
- Concepts
-
Polysomnography, Photoplethysmogram, Sleep (system call), Sleep Stages, Computer science, Heart rate variability, Artificial intelligence, Wearable computer, Computer vision, Medicine, Heart rate, Embedded system, Electroencephalography, Psychiatry, Radiology, Blood pressure, Operating system, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
33Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 8, 2023: 10Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.stage | 91 |
| abstract_inverted_index.turn, | 32 |
| abstract_inverted_index.(HRV). | 30 |
| abstract_inverted_index.camera | 16 |
| abstract_inverted_index.enable | 23 |
| abstract_inverted_index.remote | 36, 56, 111, 142 |
| abstract_inverted_index.scored | 139 |
| abstract_inverted_index.sensor | 17 |
| abstract_inverted_index.stages | 66, 129 |
| abstract_inverted_index.4-class | 89 |
| abstract_inverted_index.average | 159 |
| abstract_inverted_index.healthy | 100 |
| abstract_inverted_index.metrics | 44, 179 |
| abstract_inverted_index.nature. | 13 |
| abstract_inverted_index.perform | 61 |
| abstract_inverted_index.reflect | 46 |
| abstract_inverted_index.remains | 2 |
| abstract_inverted_index.results | 116 |
| abstract_inverted_index.sensing | 182 |
| abstract_inverted_index.showing | 188 |
| abstract_inverted_index.stages, | 40 |
| abstract_inverted_index.stages. | 51 |
| abstract_inverted_index.Advances | 14 |
| abstract_inverted_index.Compared | 136 |
| abstract_inverted_index.accuracy | 166 |
| abstract_inverted_index.achieved | 145 |
| abstract_inverted_index.analysis | 21 |
| abstract_inverted_index.classify | 127 |
| abstract_inverted_index.manually | 138 |
| abstract_inverted_index.moderate | 146 |
| abstract_inverted_index.obtained | 102 |
| abstract_inverted_index.reported | 180 |
| abstract_inverted_index.staging, | 187 |
| abstract_inverted_index.staging. | 195 |
| abstract_inverted_index.standard | 5 |
| abstract_inverted_index.validate | 114 |
| abstract_inverted_index.wearable | 185 |
| abstract_inverted_index.HRV-based | 84 |
| abstract_inverted_index.PPG-based | 143 |
| abstract_inverted_index.agreement | 147 |
| abstract_inverted_index.algorithm | 85, 123, 144 |
| abstract_inverted_index.automated | 62 |
| abstract_inverted_index.darkness. | 69 |
| abstract_inverted_index.developed | 83 |
| abstract_inverted_index.different | 42 |
| abstract_inverted_index.evaluated | 53, 95 |
| abstract_inverted_index.obtrusive | 11 |
| abstract_inverted_index.overnight | 105 |
| abstract_inverted_index.potential | 190 |
| abstract_inverted_index.purposes, | 120 |
| abstract_inverted_index.recording | 106 |
| abstract_inverted_index.assessment | 37 |
| abstract_inverted_index.expression | 48 |
| abstract_inverted_index.indirectly | 45 |
| abstract_inverted_index.monitoring | 8, 25 |
| abstract_inverted_index.previously | 82 |
| abstract_inverted_index.techniques | 22 |
| abstract_inverted_index.technology | 18 |
| abstract_inverted_index.Performance | 93 |
| abstract_inverted_index.contactless | 24, 73 |
| abstract_inverted_index.measurement | 74 |
| abstract_inverted_index.non-contact | 193 |
| abstract_inverted_index.performance | 178 |
| abstract_inverted_index.variability | 29 |
| abstract_inverted_index.video-based | 192 |
| abstract_inverted_index.benchmarking | 119 |
| abstract_inverted_index.camera-based | 55, 110 |
| abstract_inverted_index.participants | 101 |
| abstract_inverted_index.simultaneous | 104 |
| abstract_inverted_index.technologies | 183 |
| abstract_inverted_index.variability, | 78 |
| abstract_inverted_index.corresponding | 133 |
| abstract_inverted_index.respectively. | 171 |
| abstract_inverted_index.classification | 63 |
| abstract_inverted_index.Polysomnography | 0 |
| abstract_inverted_index.classification, | 157 |
| abstract_inverted_index.classification. | 92 |
| abstract_inverted_index.photoplethysmography | 57 |
| abstract_inverted_index.(Wake–N1/N2/N3–REM) | 152 |
| abstract_inverted_index.(Wake–N1/N2–N3–REM) | 156 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5067304019 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I4210132398, https://openalex.org/I83019370 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.94989037 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |