Automatic analysis of neonatal video data to evaluate resuscitation performance


ja-17-236

Author(s): Guo Y, Wrammert J, Singh K, KC A, Bradford K, Krishnamurthy A

Year: 2016

Automatic analysis of neonatal video data to evaluate resuscitation performance Abstract:

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.

This document is not available in print from MEASURE Evaluation.

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