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Automatic Analysis of Neonatal Video Data to Evaluate Resuscitation Performance
Univ North Carolina Chapel Hill, Chapel Hill, NC 27514 USA..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH).
Univ North Carolina Chapel Hill, Chapel Hill, NC 27514 USA..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH). UNICEF Nepal, Kathmandu, Nepal..ORCID iD: 0000-0002-0541-4486
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2016 (English)In: 2016 IEEE 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL ADVANCES IN BIO AND MEDICAL SCIENCES (ICCABS), 2016Conference paper, Published paper (Refereed)
Abstract [en]

Approximately 3% of births require neonatal resuscitation, which has a direct impact on the immediate survival of these infants. This report proposes an automatic video analysis method for neonatal resuscitation performance evaluation, which helps improve the quality of this procedure. More specifically, we design a deep learning based action model which incorporates motion and spatial information in order to classify neonatal resuscitation actions in videos. First, we use a Convolutional Neural Network to select regions containing infants and only keep those that are motion salient. Second, we extract deep spatial-temporal features to train a linear SVM classifier. Finally, we propose a pair-wise model to ensure consistent classification in consecutive frames. We evaluate the proposed method on a dataset consisting of 17 videos and compare the result against the state-of-the-art method for action classification in videos. To our best knowledge, this work is the first to attempt automatic evaluation of neonatal resuscitation videos and identifies several issues that require further work.

Place, publisher, year, edition, pages
2016.
Series
International Conference on Computational Advances in Bio and Medical Sciences, ISSN 2164-229X, E-ISSN 2473-4659
National Category
Pediatrics
Identifiers
URN: urn:nbn:se:uu:diva-316341DOI: 10.1109/ICCABS.2016.7802775ISI: 000392416700011ISBN: 978-1-5090-4199-2 (electronic)OAI: oai:DiVA.org:uu-316341DiVA, id: diva2:1090990
Conference
6th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), OCT 13-15, 2016, Georgia Inst Technol, Atlanta, GA
Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2017-04-25Bibliographically approved

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Wrammert, JohanKC, Ashish

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