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Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data
OBJECTIVE: Electrical impedance tomography (EIT) is a bedside tool for lung ventilation and perfusion assessment. However, the ability for long-term monitoring diminished due to interferences from clinical interventions and motion artifacts. The purpose of this study is to investigate the feasibilit...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841770/ https://www.ncbi.nlm.nih.gov/pubmed/35174192 http://dx.doi.org/10.3389/fmed.2022.817590 |
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author | Yang, Lin Qu, Shuoyao Zhang, Yanwei Zhang, Ge Wang, Hang Yang, Bin Xu, Canhua Dai, Meng Cao, Xinsheng |
author_facet | Yang, Lin Qu, Shuoyao Zhang, Yanwei Zhang, Ge Wang, Hang Yang, Bin Xu, Canhua Dai, Meng Cao, Xinsheng |
author_sort | Yang, Lin |
collection | PubMed |
description | OBJECTIVE: Electrical impedance tomography (EIT) is a bedside tool for lung ventilation and perfusion assessment. However, the ability for long-term monitoring diminished due to interferences from clinical interventions and motion artifacts. The purpose of this study is to investigate the feasibility of the discrete wavelet transform (DWT) to detect and remove the common types of motion artifacts in thoracic EIT. METHODS: Baseline drifting, step-like and spike-like interferences were simulated to mimic three common types of motion artifacts. The discrete wavelet decomposition was employed to characterize those motion artifacts in different frequency levels with different wavelet coefficients, and those motion artifacts were then attenuated by suppressing the relevant wavelet coefficients. Further validation was conducted in two patients when motion artifacts were introduced through pulsating mattress and deliberate body movements. The db8 wavelet was used to decompose the contaminated signals into several sublevels. RESULTS: In the simulation study, it was shown that, after being processed by DWT, the signal consistency improved by 92.98% for baseline drifting, 97.83% for the step-like artifact, and 62.83% for the spike-like artifact; the signal similarity improved by 77.49% for baseline drifting, 73.47% for the step-like artifact, and 2.35% for the spike-like artifact. Results from patient data demonstrated the EIT image errors decreased by 89.24% (baseline drifting), 88.45% (step-like artifact), and 97.80% (spike-like artifact), respectively; the data correlations between EIT images without artifacts and the processed were all > 0.95. CONCLUSION: This study found that DWT is a universal and effective tool to detect and remove these motion artifacts. |
format | Online Article Text |
id | pubmed-8841770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88417702022-02-15 Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data Yang, Lin Qu, Shuoyao Zhang, Yanwei Zhang, Ge Wang, Hang Yang, Bin Xu, Canhua Dai, Meng Cao, Xinsheng Front Med (Lausanne) Medicine OBJECTIVE: Electrical impedance tomography (EIT) is a bedside tool for lung ventilation and perfusion assessment. However, the ability for long-term monitoring diminished due to interferences from clinical interventions and motion artifacts. The purpose of this study is to investigate the feasibility of the discrete wavelet transform (DWT) to detect and remove the common types of motion artifacts in thoracic EIT. METHODS: Baseline drifting, step-like and spike-like interferences were simulated to mimic three common types of motion artifacts. The discrete wavelet decomposition was employed to characterize those motion artifacts in different frequency levels with different wavelet coefficients, and those motion artifacts were then attenuated by suppressing the relevant wavelet coefficients. Further validation was conducted in two patients when motion artifacts were introduced through pulsating mattress and deliberate body movements. The db8 wavelet was used to decompose the contaminated signals into several sublevels. RESULTS: In the simulation study, it was shown that, after being processed by DWT, the signal consistency improved by 92.98% for baseline drifting, 97.83% for the step-like artifact, and 62.83% for the spike-like artifact; the signal similarity improved by 77.49% for baseline drifting, 73.47% for the step-like artifact, and 2.35% for the spike-like artifact. Results from patient data demonstrated the EIT image errors decreased by 89.24% (baseline drifting), 88.45% (step-like artifact), and 97.80% (spike-like artifact), respectively; the data correlations between EIT images without artifacts and the processed were all > 0.95. CONCLUSION: This study found that DWT is a universal and effective tool to detect and remove these motion artifacts. Frontiers Media S.A. 2022-01-31 /pmc/articles/PMC8841770/ /pubmed/35174192 http://dx.doi.org/10.3389/fmed.2022.817590 Text en Copyright © 2022 Yang, Qu, Zhang, Zhang, Wang, Yang, Xu, Dai and Cao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Yang, Lin Qu, Shuoyao Zhang, Yanwei Zhang, Ge Wang, Hang Yang, Bin Xu, Canhua Dai, Meng Cao, Xinsheng Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data |
title | Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data |
title_full | Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data |
title_fullStr | Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data |
title_full_unstemmed | Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data |
title_short | Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data |
title_sort | removing clinical motion artifacts during ventilation monitoring with electrical impedance tomography: introduction of methodology and validation with simulation and patient data |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841770/ https://www.ncbi.nlm.nih.gov/pubmed/35174192 http://dx.doi.org/10.3389/fmed.2022.817590 |
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