Comparison of video-based methods for respiration rhythm measurement

被引:9
作者
Mateu-Mateus, M. [1 ]
Guede-Fernandez, F. [1 ]
Ferrer-Mileo, V. [1 ]
Garcia-Gonzalez, M. A. [1 ]
Ramos-Castro, J. [1 ]
Fernandez-Chimeno, M. [1 ]
机构
[1] Univ Politecn Cataluna, Dept Elect Engn, ES-08034 Barcelona, Spain
关键词
Respiration rhythm; Respiration variability; Video-based; Camera; Thermal; Depth; ALGORITHM; SENSORS; SIGNAL; ECG;
D O I
10.1016/j.bspc.2019.02.004
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The aim of this work is to characterize the differences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography system (Respiband system from BioSignals Plux). 21 healthy subjects where measured, performing 4 tests for each subject. The respiratory rhythm and its variability was obtained from the four respiratory signals (3 video methods and gold standard). The signal acquisition was performed with custom and proprietary algorithms. To characterize the respiratory rhythm and its variability obtained with the different video sources and gold standard, the instantaneous frequency, Bland-Altman plots and standard deviation of the error between video methods and the gold standard have been computed. The depth and RGB camera present high agreement with no statistical differences between them, with errors when comparing with the gold standard in the range of mHz. The thermal camera performs poorly if compared with the two other methods, nevertheless it cannot be discarded directly because some errors produced by the subjects head movement could not be corrected. From these results we conclude that the depth and RGB camera, and their respective acquisition algorithms, can be used in controlled conditions to measure respiration rhythm and its variability. The thermal camera on the other hand, although it cannot be discarded directly, performed poorly if compared with the other two methods. Further studies are needed to confirm that these methods can be used in real life conditions. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:138 / 147
页数:10
相关论文
共 36 条
[1]   Respiration Rate Monitoring Methods: A Review [J].
AL-Khalidi, F. Q. ;
Saatchi, R. ;
Burke, D. ;
Elphick, H. ;
Tan, S. .
PEDIATRIC PULMONOLOGY, 2011, 46 (06) :523-529
[2]   Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition [J].
Balocchi, R ;
Menicucci, D ;
Santarcangelo, E ;
Sebastiani, L ;
Gemignani, A ;
Ghelarducci, B ;
Varanini, M .
CHAOS SOLITONS & FRACTALS, 2004, 20 (01) :171-177
[3]  
Bartula M, 2013, IEEE ENG MED BIO, P2672, DOI 10.1109/EMBC.2013.6610090
[4]   Breathing pattern in humans: diversity and individuality [J].
Benchetrit, G .
RESPIRATION PHYSIOLOGY, 2000, 122 (2-3) :123-129
[5]   Respiratory rate detection algorithm based on RGB-D camera: theoretical background and experimental results [J].
Benetazzo, Flavia ;
Freddi, Alessandro ;
Monteriu, Andrea ;
Longhi, Sauro .
HEALTHCARE TECHNOLOGY LETTERS, 2014, 1 (03) :81-86
[6]   A new QRS detection algorithm based on the Hilbert transform [J].
Benitez, DS ;
Gaydecki, PA ;
Zaidi, A ;
Fitzpatrick, AP .
COMPUTERS IN CARDIOLOGY 2000, VOL 27, 2000, 27 :379-382
[7]  
Bernal EA, 2014, 2014 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI), P101, DOI 10.1109/BHI.2014.6864314
[8]   Ambient and unobtrusive cardiorespiratory monitoring techniques [J].
Brüser, Christoph ;
Antink, Christoph Hoog ;
Wartzek, Tobias ;
Walter, Marian ;
Leonhardt, Steffen .
IEEE Reviews in Biomedical Engineering, 2015, 8 :30-43
[9]  
Chatterjee A, 2016, IEEE ENG MED BIO, P2708, DOI 10.1109/EMBC.2016.7591289
[10]  
Chimeno M. Fernandez, 2018, Respiratory Signal Extraction, Patent No. [WO/2018/121861, WO2018121861]