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Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States
The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670397/ https://www.ncbi.nlm.nih.gov/pubmed/38002883 http://dx.doi.org/10.3390/children10111792 |
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author | Zhang, Dandan Peng, Zheng Van Pul, Carola Overeem, Sebastiaan Chen, Wei Dudink, Jeroen Andriessen, Peter Aarts, Ronald M. Long, Xi |
author_facet | Zhang, Dandan Peng, Zheng Van Pul, Carola Overeem, Sebastiaan Chen, Wei Dudink, Jeroen Andriessen, Peter Aarts, Ronald M. Long, Xi |
author_sort | Zhang, Dandan |
collection | PubMed |
description | The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states. |
format | Online Article Text |
id | pubmed-10670397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106703972023-11-07 Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States Zhang, Dandan Peng, Zheng Van Pul, Carola Overeem, Sebastiaan Chen, Wei Dudink, Jeroen Andriessen, Peter Aarts, Ronald M. Long, Xi Children (Basel) Article The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states. MDPI 2023-11-07 /pmc/articles/PMC10670397/ /pubmed/38002883 http://dx.doi.org/10.3390/children10111792 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Dandan Peng, Zheng Van Pul, Carola Overeem, Sebastiaan Chen, Wei Dudink, Jeroen Andriessen, Peter Aarts, Ronald M. Long, Xi Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States |
title | Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States |
title_full | Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States |
title_fullStr | Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States |
title_full_unstemmed | Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States |
title_short | Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States |
title_sort | combining cardiorespiratory signals and video-based actigraphy for classifying preterm infant sleep states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670397/ https://www.ncbi.nlm.nih.gov/pubmed/38002883 http://dx.doi.org/10.3390/children10111792 |
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