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Zhang D, Peng Z, Sun S, van Pul C, Shan C, Dudink J, Andriessen P, Aarts RM, Long X. Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state. Acta Paediatr 2024. [PMID: 38501583 DOI: 10.1111/apa.17211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/18/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024]
Abstract
AIM This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors. METHODS We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient. RESULTS A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11). CONCLUSION Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states.
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Affiliation(s)
- Dandan Zhang
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Zheng Peng
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Clinical Physics, Máxima Medical Center, Veldhoven, The Netherlands
| | - Shaoxiong Sun
- Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom
| | - Carola van Pul
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Clinical Physics, Máxima Medical Center, Veldhoven, The Netherlands
| | - Caifeng Shan
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
- School of Intelligence Science and Technology, Nanjing University, Nanjing, China
| | - Jeroen Dudink
- Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter Andriessen
- Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Neonatology, Máxima Medical Center, Veldhoven, The Netherlands
| | - Ronald M Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Zhang D, Peng Z, Van Pul C, Overeem S, Chen W, Dudink J, Andriessen P, Aarts RM, Long X. Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1792. [PMID: 38002883 PMCID: PMC10670397 DOI: 10.3390/children10111792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023]
Abstract
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.
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Affiliation(s)
- Dandan Zhang
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Department of Personal and Preventive Care, Philips Research, 5556 AE Eindhoven, The Netherlands
| | - Zheng Peng
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Department of Clinical Physics, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands
| | - Carola Van Pul
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Department of Clinical Physics, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Sleep Medicine Center, Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Wei Chen
- The Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China;
| | - Jeroen Dudink
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3584 EA Utrecht, The Netherlands;
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands;
| | - Ronald M. Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
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