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| Study on Extraction Method of Reduced-Dimensionality Ensemble Empirical Mode Decomposition for Cardiopulmonary Signals Based on Electrical Impedance Tomography Technology |
| Li Kun1,2, Li Weichen3, Guo Yitong2,4, Wang Weice2, Wang Yu1, Yan Xiaoheng1, Shi Xuetao2#* |
1(Faculty of Electrical and Control Engineering,Liaoning Technical University, Huludao 125105,Liaoning, China)
2(Department of Biomedical Engineering,Air Force Medical University,Xi'an 710032,China)
3(School of Life Sciences,Northwestern University,Xi'an 710127,China)
4(Department of Ultrasound Diagnosis, Tangdu Hospital, Air Force Medical University, Xi'an 710038, China) |
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Abstract Real-time acquisition of cardiac ejection and pulmonary ventilation activity information is of great clinical significance. To separate both cardiac ejection and pulmonary ventilation activity signals simultaneously from chest electrical impedance tomography (EIT) data, this study proposed a novel signal extraction method named reduced-dimensionality ensemble empirical mode decomposition (RDEEMD) method. A total of 9 volunteers were recruited for EIT chest data collection. Firstly, this method classified the measurement channels based on the strength of the cardiac activity signal of the chest EIT data under breath holding state. Subsequently, the ensemble empirical mode decomposition method was used to decompose the EIT data under autonomous breathing state, and the decomposed components were categorized based on spectral characteristics to obtain the lung ventilation EIT signal. Combined with the band-pass filtering method and based on the aforementioned channel classification, the heart activity EIT signal was obtained by reduced-dimensionality of the heart activity signal. Finally, the EIT image sequences of the ventilation phase and cardiac phase were reconstructed. The results showed that the highest power spectral peak for lung ventilation (52.71 ± 1.39) dB in the lung area of the ventilation phase image can be obtained through RDEEMD, the highest power spectral peak for cardiac activity (43.05 ± 3.26) dB in the heart area of the cardiac phase image can be obtained through RDEEMD, indicating a fine reservation of ventilation and cardiac activity information. Meanwhile, the power spectral peak related to cardiac activity in the heart area of the ventilation phase image obtained by RDEEMD (10.02 ± 2.65) dB is the lowest among these methods, indicating that the effect of cardiac activity was well inhibited, compared to the reference method, there were significant differences (P<0.05). These results showed that the method RDEEMD could effectively separate signals related to lung ventilation and heart activity, preserving respective activity information and suppressing the influence of the heart in lung imaging. Finally, it can effectively suppress interference signal and lay the foundation for providing more accurate treatment strategy guidance in clinical practice.
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Received: 20 October 2023
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Corresponding Authors:
*E-mail: shixuetao@fmmu.edu.cn
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[1] Fazleen A, Wilkinson T. Early COPD: current evidence for diagnosis and management [J]. Therapeutic Advances in Respiratory Disease, 2020, 14: 1753466620942128.
[2] 邓青南,曹小飞. 老年慢性阻塞性肺疾病急性加重期的心脏保护 [J]. 中华临床医学杂志, 2004, 5(10): 15-17.
[3] 刘玉金,贾振华. 慢性阻塞性肺疾病合并冠心病的心肺相关机制浅析 [J]. 中国中医药信息杂志, 2017, 24(5): 112-115.
[4] Frerichs I, Amato MBP, Van Kaam AH, et al. Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the translational EIT development study group [J]. Thorax, 2017, 72(1): 83-93.
[5] Adler A, Boyle A. Electrical impedance tomography: tissue properties to image measures [J]. IEEE Transactions on Biomedical Engineering, 2017, 64(11): 2494-2504.
[6] 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 [J]. 中国医疗设备, 2019, 34(1): 6-9,17.
[7] McArdle FJ, Suggett AJ, Brown BH, et al. An assessment of dynamic images by applied potential tomography for monitoring pulmonary perfusion [J]. Clinical Physics and Physiological Measurement, 1988, 9(4A): 87.
[8] Zadehkoochak M, Blott BH, Hames TK, et al. Pulmonary perfusion and ventricular ejection imaging by frequency domain filtering of EIT images [J]. Clinical Physics and Physiological Measurement, 1992, 13(A): 191.
[9] Leathard AD, Brown BH, Campbell J, et al. A comparison of ventilatory and cardiac related changes in EIT images of normal human lungs and of lungs with pulmonary emboli [J]. Physiological Measurement, 1994, 15(2A): A137.
[10] Frerichs I, Pulletz S, Elke G, et al. Assessment of changes in distribution of lung perfusion by electrical impedance tomography [J]. Respiration, 2009;77(3):282-291.
[11] Rahman T, Hasan MM, Farooq A, et al. Extraction of cardiac and respiration signals in electrical impedance tomography based on independent component analysis [J]. Journal of Electrical Bioimpedance, 2013, 4(1): 38-44.
[12] Deibele JM, Luepschen H, Leonhardt S. Dynamic separation of pulmonary and cardiac changes in electrical impedance tomography [J]. Physiological Measurement, 2008, 29(6): S1.
[13] Jang GY, Jeong YJ, Zhang Tingting, et al. Noninvasive, simultaneous, and continuous measurements of stroke volume and tidal volume using EIT: feasibility study of animal experiments [J]. Scientific Reports, 2020, 10(1): 11242.
[14] Zhang Tingting, Jang YG, Oh IT, et al. Source consistency electrical impedance tomography [J]. SIAM Journal on Applied Mathematics,2020,80(1):499-520.
[15] Huang NE, Shen Zheng, Long SR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[16] Cheng KS, Su PL, Ko YF. Separation of heart and lung-related signals in electrical impedance tomography using empirical mode decomposition [J]. Current Medical Imaging, 2022, 18(13): 1396-1415.
[17] Wu Zhaohua, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(01): 1-41.
[18] Sun Xuxue, Jakob O, Malmivuo J, et al. Separation of cardiac-and ventilation-related signals within electrical impedance tomography data based on multi-dimensional ensemble empirical mode decomposition [J]. IFAC-PapersOnLine, 2017, 50(1): 4436-4441.
[19] Yeh JR, Shieh JS, Huang NE. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method [J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135-156.
[20] Adler A, Lionheart W, Polydorides N. Eidors: electrical impedance tomography and diffuse optical tomography reconstruction software [EB/OL]. http://www.sce.carleton.ca/faculty/adler/eidors/index.shtml. 2017-06-21/2023-10-20.
[21] Adler A, Arnold J H, Bayford R, et al. GREIT: a unified approach to 2D linear EIT reconstruction of lung images [J]. Physiol Meas, 2009, 30(6): S35-55.
[22] Zhang Ke, Li Maokun, Liang Haiqing, et al. Deep feature-domain matching for cardiac-related component separation from a chest electrical impedance tomography image series: proof-of-concept study [J]. Physiol Meas, 2022, 43(12): ac9c44.
[23] Frerichs I, Hinz J, Herrmann P, et al. Regional lung perfusion as determined by electrical impedance tomography in comparison with electron beam CT imaging [J]. IEEE Trans Med Imaging, 2002,21(6):646-652.
[24] Frerichs I, Pulletz S, Elke G, et al. Assessment of changes in distribution of lung perfusion by electrical impedance tomography[J]. Respiration, 2009, 77(3): 282-291.
[25] Borges JB, Suarez-Sipmann F, Bohm SH, et al. Regional lung perfusion estimated by electrical impedance tomography in a piglet model of lung collapse[J]. Journal of Applied Physiology, 2012, 112(1): 225-236.
[26] 刘备,董胡,钱盛友. 基于经验模态分解与小波分析的超声信号降噪方法 [J]. 测试技术学报, 2018,32(5):422-428. |
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