Abstract
Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 ± 2.2% and an F1-score 0.93 ± 0.3.
Original language | English |
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Article number | 9405399 |
Pages (from-to) | 1441-1449 |
Number of pages | 9 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 25 |
Issue number | 5 |
Early online date | 15 Apr 2021 |
DOIs | |
Publication status | Published - May 2021 |
Keywords
- Brain modeling
- Electroencephalography
- Hospitals
- Monitoring
- Neonatal sleep monitoring
- Pediatrics
- Sleep
- Support vector machines
- deep convolutional neural network
- facial expression
- support vector machine
- video and image analysis
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Dive into the research topics of 'A hybrid DCNN-SVM model for classifying neonatal sleep and wake states based on facial expressions in video'. Together they form a unique fingerprint.Impacts
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Perinatal Medicine
M.B. (Beatrijs) van der Hout-van der Jagt (Content manager) & Eugenie Delvaux (Content manager)
Impact: Research Topic/Theme (at group level)
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Sleep Medicine
Merel M. van Gilst (Content manager) & M.B. (Beatrijs) van der Hout-van der Jagt (Content manager)
Impact: Research Topic/Theme (at group level)