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Automatic classification of neonatal sleep-wake states based on facial video analysis

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dc.contributor.author Mukai, Y
dc.contributor.author Morita, K
dc.contributor.author Shirai, NC
dc.contributor.author Wakabayashi, T
dc.contributor.author Shinkoda, H
dc.contributor.author Matsumoto, A
dc.contributor.author Noguchi, Y
dc.contributor.author Shiramizu, M
dc.contributor.editor Sudantha, BH
dc.date.accessioned 2022-11-18T04:24:08Z
dc.date.available 2022-11-18T04:24:08Z
dc.date.issued 2019-12
dc.identifier.citation Y. Mukai et al., "Automatic Classification of Neonatal Sleep-Wake States Based on Facial Video Analysis," 2019 4th International Conference on Information Technology Research (ICITR), 2019, pp. 1-6, doi: 10.1109/ICITR49409.2019.9407788. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19561
dc.description.abstract Premature babies are admitted to the NICU (Neonatal Intensive Care Unit) for several weeks and generally placed under high medical supervision. To provide a better environment to them, some researchers investigate the affection of light and noise in the NICU on the formation of the sleep-wake cycle of the newborn called Circadian rhythm. These researches require the optimal evaluation method of the sleep-wake state. The visual assessment by nurses do not guarantee enough inter-tester reliability, and the measurement puts an additional burden on them. The conventional sleep-wake states discrimination method requires attachment devices on the subject's body. This paper proposes the automatic classification method of the sleep-wake states of neonates by using only facial information. In this research, we extract gradient features and spatio-temporal HOGV features from 3,600 face image frames (1 minute). According to Blazelton's method, this study classifies the sleep-wake states into six classes by using machine learning techniques. Support Vector Machine and Random Forest were used in the experiment. The spatio-temporal HOGV feature is an extension of the HOG feature to the time domain. The experiments using two kinds of feature quantities and classifiers showed that the highest accuracy rate (54.4%) was obtained by the gradient feature and Random Forest. This result suggested the possibility of improving accuracy by combining facial information with body movement and other conventional features. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9407788 en_US
dc.subject Sleep-Wake States en_US
dc.subject Neonates en_US
dc.subject Neonatal Behavioral Assessment Scale (NBAS) en_US
dc.subject Classifying en_US
dc.subject Neonatal Intensive Care Unit (NICU) en_US
dc.subject Facial Video Analysis en_US
dc.title Automatic classification of neonatal sleep-wake states based on facial video analysis en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2019 en_US
dc.identifier.conference 4th International Conference in Information Technology Research 2019 en_US
dc.identifier.place Colombo,Sri Lanka en_US
dc.identifier.proceeding Proceedings of the 4th International Conference in Information Technology Research 2019 en_US
dc.identifier.doi doi: 10.1109/ICITR49409.2019.9407788 en_US


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    International Conference on Information Technology Research (ICITR)

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