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Level-Set Segmentation-Based Respiratory Volume Estimation Using a Depth Camera

Authors
 KyeongTaek Oh  ;  Cheung Soo Shin  ;  Jeongmin Kim  ;  Sun K. Yoo 
Citation
 IEEE Journal of Biomedical and Health Informatics, Vol.23(4) : 1674-1682, 2019 
Journal Title
 IEEE Journal of Biomedical and Health Informatics 
ISSN
 2168-2194 
Issue Date
2019
Abstract
In this paper, a method is proposed to measure human respiratory volume using a depth camera. The level-set segmentation method, combined with spatial and temporal information, was used to measure respiratory volume accurately. The shape of the human chest wall was used as spatial information. As temporal information, the segmentation result from the previous frame in the time-aligned depth image was used. The results of the proposed method were verified using a ventilator. The proposed method was also compared with other level-set methods. The result showed that the mean tidal volume error of the proposed method was 8.41% compared to the actual tidal volume. This was calculated to have less error than with two other methods: the level-set method with spatial information (14.34%) and the level-set method with temporal information (10.93%). The difference between these methods of tidal volume error was statistically significant [Formula: see text]. The intra-class correlation coefficient (ICC) of the respiratory volume waveform measured by a ventilator and by the proposed method was 0.893 on an average, while the ICC between the ventilator and the other methods were 0.837 and 0.879 on an average.
Full Text
https://ieeexplore.ieee.org/document/8466963
DOI
10.1109/JBHI.2018.2870859
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jeongmin(김정민) ORCID logo https://orcid.org/0000-0002-0468-8012
Shin, Cheung Soo(신증수) ORCID logo https://orcid.org/0000-0001-7829-8458
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/171309
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