A non-contact camera-based method for respiratory rhythm extraction
The aim of this work is to present a non-contact video-based method for respiratory rhythm extraction. The method makes use of a consumer-grade RGB camera, and it is based on computer vision algorithms to detect and track a custom pattern placed on the thorax of the subject. The respiratory signal i...
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| Published in | Biomedical signal processing and control Vol. 66; p. 102443 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.04.2021
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| ISSN | 1746-8094 1746-8108 |
| DOI | 10.1016/j.bspc.2021.102443 |
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| Abstract | The aim of this work is to present a non-contact video-based method for respiratory rhythm extraction. The method makes use of a consumer-grade RGB camera, and it is based on computer vision algorithms to detect and track a custom pattern placed on the thorax of the subject. The respiratory signal is extracted by computing the changes in the position of the detected pattern through time. The method has been validated by comparing the extracted respiratory signal versus the one obtained with a reference method in adult population. The reference method was an inductive thorax plethysmography system (Respiband system from BioSignalsPlux™).
21 healthy subjects were measured and four tests were performed for each subject. The respiratory signals and its respiratory cycles were extracted. To characterise the error, the respiratory cycles were assessed with: the Fisher intra-class correlation (ICC), mean absolute error (MAE), the mean absolute percentage error (MAPE) and four Bland-Altman plots were obtained. The results show a >0.9 correlation for controlled respiration and >0.85 for unconstrained respiration between the proposed method and the reference method, with low error results (MAPE <4% for constrained respiration and <6% for unconstrained respiration) and with a high sensitivity when detecting the respiratory cycles (>94% in all cases).
From the obtained results we can conclude that the proposed algorithm is adequate to acquire the respiratory signal for rhythm extraction, in real-time with a high performance when compared with the reference method, and that it could be applied to real-life situations. |
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| AbstractList | The aim of this work is to present a non-contact video-based method for respiratory rhythm extraction. The method makes use of a consumer-grade RGB camera, and it is based on computer vision algorithms to detect and track a custom pattern placed on the thorax of the subject. The respiratory signal is extracted by computing the changes in the position of the detected pattern through time. The method has been validated by comparing the extracted respiratory signal versus the one obtained with a reference method in adult population. The reference method was an inductive thorax plethysmography system (Respiband system from BioSignalsPlux™).
21 healthy subjects were measured and four tests were performed for each subject. The respiratory signals and its respiratory cycles were extracted. To characterise the error, the respiratory cycles were assessed with: the Fisher intra-class correlation (ICC), mean absolute error (MAE), the mean absolute percentage error (MAPE) and four Bland-Altman plots were obtained. The results show a >0.9 correlation for controlled respiration and >0.85 for unconstrained respiration between the proposed method and the reference method, with low error results (MAPE <4% for constrained respiration and <6% for unconstrained respiration) and with a high sensitivity when detecting the respiratory cycles (>94% in all cases).
From the obtained results we can conclude that the proposed algorithm is adequate to acquire the respiratory signal for rhythm extraction, in real-time with a high performance when compared with the reference method, and that it could be applied to real-life situations. |
| ArticleNumber | 102443 |
| Author | Rodriguez-Ibáñez, N. García-González, M.A. Ramos-Castro, J. Fernández-Chimeno, M. Guede-Fernández, F. Mateu-Mateus, M. |
| Author_xml | – sequence: 1 givenname: M. surname: Mateu-Mateus fullname: Mateu-Mateus, M. email: marc.mateu.mateus@upc.edu organization: Department of Electronic Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain – sequence: 2 givenname: F. surname: Guede-Fernández fullname: Guede-Fernández, F. organization: Department of Electronic Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain – sequence: 3 givenname: N. surname: Rodriguez-Ibáñez fullname: Rodriguez-Ibáñez, N. organization: Biomedical engineer, Technical Project Leader Driver Monitoring at Ficosa International S.A, P.I. Can Mitjans s/n, Viladecavalls (Barcelona), 08232, Spain – sequence: 4 givenname: M.A. surname: García-González fullname: García-González, M.A. organization: Department of Electronic Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain – sequence: 5 givenname: J. surname: Ramos-Castro fullname: Ramos-Castro, J. organization: Department of Electronic Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain – sequence: 6 givenname: M. surname: Fernández-Chimeno fullname: Fernández-Chimeno, M. organization: Department of Electronic Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain |
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| Keywords | Camera-based Computer vision Non-contact Respiration |
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| SubjectTerms | Camera-based Computer vision Non-contact Respiration |
| Title | A non-contact camera-based method for respiratory rhythm extraction |
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