期刊
NEUROCOMPUTING
卷 390, 期 -, 页码 226-238出版社
ELSEVIER
DOI: 10.1016/j.neucom.2019.05.106
关键词
Autism spectrum disorder; Tracking-learning-detection (TLD); Accumulative histogram; Deep learning; Long Short-Term Memory (LSTM)
资金
- National Key Research and Development Program of China [2017YFC0820205]
- National Natural Science Foundation of China [61703198, 61773070, 51575338, 61733011]
- Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province [2018ACB21014]
- Open Fund of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences [20180109]
- Open Fund of Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology [2017B02]
Autism spectrum disorder (ASD) is a serious neurodevelopmental disorder that impairs a child's ability to communicate and interact with others. Usually, recognizing a child with ASD needs the diagnosis by professional doctors. However, it is not only expensive and time-consuming, but also the results are influenced by subjective factors, such as the experience of a doctor. Recently, some methods which identify ASD based on biomarkers have been developed, but there are rarely works specific to raw video data. This paper is the first attempt to help diagnose the children with ASD in raw video data using a deep learning technique. Firstly, in order to investigate different gaze patterns between ASD children and typically developing (TD) children, we track the eye movement in each video by the tracking-learning-detection method. Secondly, we divide these tracking trajectories into two components: (i) the length; and (ii) the angle. Afterwards, we calculate an accumulative histogram for each component. Finally, we adopt three-layer Long Short-Term Memory (LSTM) network for classification. Experimental results on our extended dataset (Ext-Dataset) containing 272 videos captured from 136 ASD children and 136 TD children show the LSTM network outperforms the traditional machine learning methods, e.g., Support Vector Machine, with the improvement of accuracy by 6.2% (from 86.4% to 92.6%). Especially, for ASD, we obtain the sensitivity (the true positive rate, TPR) of 91.9% and the specificity (the true negative rate, TNR) of 93.4%, which demonstrates the effectiveness of our method. (C) 2019 Elsevier B.V. All rights reserved.
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