TY - JOUR
T1 - A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video
AU - Awais, Muhammad
AU - Long, Xi
AU - Yin, Bin
AU - Farooq Abbasi, Saadullah
AU - Akhbarzadeh, Saeed
AU - Lu, Chunmei
AU - Wang, Xinhua
AU - Wang, Laishuan
AU - Zhang, Jiong
AU - Dudink, Jeroen
AU - Chen, Wei
N1 - Funding Information:
dateofcurrentversionMay11,2021.ThisworkwassupportedinpartApril2,2021;acceptedApril6,2021.DateofpublicationApril15,2021; Swhichisassociatedwithreducedresponsivenesstoexternal bytheShanghaiMunicipalScienceandTechnologyMajorProjectunder stimuli [1], [2]. According to research on human development Grant 2017SHZDZX01, and in part by the National Key R&D Program in early life, sleep is an essential factor for the development of ofChinaunderGrant2017YFE0112000.(Correspondingauthors:Wei the nervous system in infants [3], [4]. Newborn babies usually MuhammadAwais,SaadullahFarooqAbbasi,andSaeedAkbarzadehChen;XiLong;andChunmeiLu.) sleep between 16 and 18 hours per day in equispaced periods. are with the Center for Intelligent Medical Electronics, Department of As age increases, sleep changes from an ultradian rhythm to a ElectronicEngineering,SchoolofInformationScienceandTechnology, circadian rhythm [5]. Consistent evidence indicates that sleep fudan.edu.cn;[email protected];[email protected]).Fudan University, Shanghai 200433, China(e-mail: 17110720061@ is vital for the brain development of neonates (in particular for Xi Long is with the Philips Research, 5656 AE Eindhoven, The Nether-preterm infants) and help them in recovering from illness [2], [6]. lands, and also with the Department of Electrical Engineering, Eind- Further, the reliable measures for the tracking and assessment (e-mail:[email protected]).hovenUniversityofTechnology,5612AZEindhoven,TheNetherlands of wake-sleep patterns, over multiple nights could potentially Bin Yin is with the Connected Care and Personal provide an indication of neonatal development over time [7], Department, Philips Research, Shanghai 200032, China [8], [9], [10].
Publisher Copyright:
© 2013 IEEE.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - Brain modeling
KW - deep convolutional neural network
KW - Electroencephalography
KW - facial expression
KW - Hospitals
KW - Monitoring
KW - Neonatal sleep monitoring
KW - Pediatrics
KW - Sleep
KW - support vector machine
KW - Support vector machines
KW - video and image analysis
UR - http://www.scopus.com/inward/record.url?scp=85104605582&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2021.3073632
DO - 10.1109/JBHI.2021.3073632
M3 - Article
C2 - 33857007
SN - 2168-2194
VL - 25
SP - 1441
EP - 1449
JO - IEEE journal of biomedical and health informatics
JF - IEEE journal of biomedical and health informatics
IS - 5
M1 - 9405399
ER -