Study on the separation method of lung ventilation and lung perfusion signals in electrical impedance tomography based on rime algorithm optimized variational mode decomposition_Journal of Biomedical Engineering

Authors：

GAO Guobin ¹ , LI Kun ¹ , LI Junyao ² , ZHU Mingxu ² , WANG Yu ³ , YAN Xiaoheng ¹ ,  SHI Xuetao ²

1. Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning 125105, P. R. China;
2. Department of Biomedical Engineering, Air Force Medical University, Xi'an 710032, P. R. China;
3. Institute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, P. R. China;

Corresponding author：

SHI Xuetao, Email: shixuetao@fmmu.edu.cn

Keywords：

Electrical impedance tomography; Blood perfusion; Pulmonary ventilation; Variational mode decomposition; Rime algorithm

DOI：

10.7507/1001-5515.202410012

Video：

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Real-time acquisition of pulmonary ventilation and perfusion information through thoracic electrical impedance tomography (EIT) holds significant clinical value. This study proposes a novel method based on the rime (RIME) algorithm-optimized variational mode decomposition (VMD) to separate lung ventilation and perfusion signals directly from raw voltage data prior to EIT image reconstruction, enabling independent imaging of both parameters. To validate this approach, EIT data were collected from 16 healthy volunteers under normal breathing and inspiratory breath-holding conditions. The RIME algorithm was employed to optimize VMD parameters by minimizing envelope entropy as the fitness function. The optimized VMD was then applied to separate raw data across all measurement channels in EIT, with spectral analysis identifying relevant components to reconstruct ventilation and perfusion signals. Results demonstrated that the structural similarity index (SSIM) between perfusion images derived from normal breathing and breath-holding states averaged approximately 84% across all 16 subjects, significantly outperforming traditional frequency-domain filtering methods in perfusion imaging accuracy. This method offers a promising technical advancement for real-time monitoring of pulmonary ventilation and perfusion, holding significant value for advancing the clinical application of EIT in the diagnosis and treatment of respiratory diseases.

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7.	Jiang L, Deng Y, Xu F, et al. Individualized PEEP guided by EIT in patients undergoing general anesthesia: A systematic review and meta-analysis. Journal of Clinical Anesthesia, 2024, 94: 111397.
8.	Putensen C, Hentze B, Muenster S, et al. Electrical impedance tomography for cardio-pulmonary monitoring. Journal of Clinical Medicine, 2019, 8(8): 1176.
9.	任英杰. 通过电阻抗断层扫描评价俯卧位通气对不同肺通气灌注比的急性呼吸窘迫综合征患者影响的研究. 南充市: 川北医学院, 2023.
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11.	Krueger-Ziolek S, Gong B, Laufer B, et al. Impact of lung volume changes on perfusion estimates derived by electrical impedance tomography. Current Directions in Biomedical Engineering, 2019, 5(1): 199-202.
12.	Frerichs I, Hinz J, Herrmann P, et al. Regional lung perfusion as determined by electrical impedance tomography in comparison with electron beam CT imaging. IEEE Transactions on Medical Imaging, 2002, 21(6): 646-652.
13.	McArdle F J, Suggett A J, Brown B H, et al. An assessment of dynamic images by applied potential tomography for monitoring pulmonary perfusion. Clinical Physics and Physiological Measurement, 1988, 9(Suppl A): 87-91.
14.	Rahman T, Hasan M M, Farooq A, et al. Extraction of cardiac and respiration signals in electrical impedance tomography based on independent component analysis. Journal of Electrical Bioimpedance, 2013, 4(1). DOI: 10.5617/jeb.553.
15.	Zadehkoochak M, Blott B H, Hames T K, et al. Pulmonary perfusion and ventricular ejection imaging by frequency domain filtering of EIT images. Clinical Physics and Physiological Measurement, 1992, 13(Suppl A): 191.
16.	Stein E, Chen R, Battistel A, et al. Voltage-based separation of respiration and cardiac activity by harmonic analysis in electrical impedance tomography. IFAC Journal of Systems and Control, 2024, 27. DOI: 10.1016/j.ifacsc.2024.100248.
17.	Cheng K S, Su P L, Ko Y F. Separation of heart and lung-related signals in electrical impedance tomography using empirical mode decomposition. Current Medical Imaging, 2022, 18(13): 1396-1415.
18.	Djemili R, Djemili I. Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection. Computer Methods in Biomechanics and Biomedical Engineering, 2024, 27(15): 2091-2110.
19.	Shi X, Li W, You F, et al. High-precision electrical impedance tomography data acquisition system for brain imaging. IEEE Sensors Journal, 2018, 18(14): 5974-5984.
20.	Su H, Zhao D, Heidari A, et al. RIME: a physics-based optimization. Neurocomputing, 2023, 532: 183-214.
21.	Nguyen D T, Jin C, Thiagalingam A, et al. A review on electrical impedance tomography for pulmonary perfusion imaging. Physiological Measurement, 2012, 33(5): 695-706.
22.	Spinelli E, Kircher M, Stender B, et al. Unmatched ventilation and perfusion measured by electrical impedance tomography predicts the outcome of ARDS. Critical Care, 2021, 25(1): 192.

1. World Health Organization. World health statistics 2016: monitoring health for the SDGs, sustainable development goals. World Health Organization, 2016.
2. Moal B, Orieux A, Ferté T, et al. Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium. PloS One, 2023, 18(1): e0266985.
3. Ashbaugh D G, Bigelow D B, Petty T L, et al. Acute respiratory distress in adults[J]. Lancet, 1967, 2(7511): 319-323.
4. Máca J, Jor O, Holub M, et al. Past and present ARDS mortality rates: a systematic review. Respir Care. 2017, 62(1): 113~122.
5. 中华医学会心血管病学分会心力衰竭学组,中国医师协会心力衰竭专业委员会,中华心血管病杂志编辑委员会. 中国心力衰竭诊断和治疗指南2018. 中华心力衰竭和心肌病杂志, 2018, 2(4): 30.
6. Adler A, Boyle A. Electrical impedance tomography: tissue properties to image measures. IEEE Transactions on Biomedical Engineering, 2017, 64(11): 2494-2504.
7. Jiang L, Deng Y, Xu F, et al. Individualized PEEP guided by EIT in patients undergoing general anesthesia: A systematic review and meta-analysis. Journal of Clinical Anesthesia, 2024, 94: 111397.
8. Putensen C, Hentze B, Muenster S, et al. Electrical impedance tomography for cardio-pulmonary monitoring. Journal of Clinical Medicine, 2019, 8(8): 1176.
9. 任英杰. 通过电阻抗断层扫描评价俯卧位通气对不同肺通气灌注比的急性呼吸窘迫综合征患者影响的研究. 南充市: 川北医学院, 2023.
10. 曲志华, 代萌, 吴佳铭, 等. 机械通气下肺血流灌注状况的电阻抗断层成像评估新方法研究. 中国医疗设备, 2019, 34(1): 5.
11. Krueger-Ziolek S, Gong B, Laufer B, et al. Impact of lung volume changes on perfusion estimates derived by electrical impedance tomography. Current Directions in Biomedical Engineering, 2019, 5(1): 199-202.
12. Frerichs I, Hinz J, Herrmann P, et al. Regional lung perfusion as determined by electrical impedance tomography in comparison with electron beam CT imaging. IEEE Transactions on Medical Imaging, 2002, 21(6): 646-652.
13. McArdle F J, Suggett A J, Brown B H, et al. An assessment of dynamic images by applied potential tomography for monitoring pulmonary perfusion. Clinical Physics and Physiological Measurement, 1988, 9(Suppl A): 87-91.
14. Rahman T, Hasan M M, Farooq A, et al. Extraction of cardiac and respiration signals in electrical impedance tomography based on independent component analysis. Journal of Electrical Bioimpedance, 2013, 4(1). DOI: 10.5617/jeb.553.
15. Zadehkoochak M, Blott B H, Hames T K, et al. Pulmonary perfusion and ventricular ejection imaging by frequency domain filtering of EIT images. Clinical Physics and Physiological Measurement, 1992, 13(Suppl A): 191.
16. Stein E, Chen R, Battistel A, et al. Voltage-based separation of respiration and cardiac activity by harmonic analysis in electrical impedance tomography. IFAC Journal of Systems and Control, 2024, 27. DOI: 10.1016/j.ifacsc.2024.100248.
17. Cheng K S, Su P L, Ko Y F. Separation of heart and lung-related signals in electrical impedance tomography using empirical mode decomposition. Current Medical Imaging, 2022, 18(13): 1396-1415.
18. Djemili R, Djemili I. Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection. Computer Methods in Biomechanics and Biomedical Engineering, 2024, 27(15): 2091-2110.
19. Shi X, Li W, You F, et al. High-precision electrical impedance tomography data acquisition system for brain imaging. IEEE Sensors Journal, 2018, 18(14): 5974-5984.
20. Su H, Zhao D, Heidari A, et al. RIME: a physics-based optimization. Neurocomputing, 2023, 532: 183-214.
21. Nguyen D T, Jin C, Thiagalingam A, et al. A review on electrical impedance tomography for pulmonary perfusion imaging. Physiological Measurement, 2012, 33(5): 695-706.
22. Spinelli E, Kircher M, Stender B, et al. Unmatched ventilation and perfusion measured by electrical impedance tomography predicts the outcome of ARDS. Critical Care, 2021, 25(1): 192.

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