电阻抗断层成像技术的心肺信号降维集合经验模态分解方法研究  

Study on Extraction Method of Reduced-Dimensionality Ensemble Empirical Mode Decomposition for Cardiopulmonary Signals Based on Electrical Impedance Tomography Technology

作　　者：李坤[1,2] 李蔚琛 郭奕彤 王伟策 王煜 闫孝姮 史学涛[2] Li Kun;Li Weichen;Guo Yitong;Wang Weice;Wang Yu;Yan Xiaoheng;Shi Xuetao(Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,Liaoning,China;Department of Biomedical Engineering,Air Force Medical University,Xi'an 710032,China;School of Life Sciences,Northwestern University,Xi'an 710127,China;Department of Ultrasound Diagnosis,Tangdu Hospital,Air Force Medical University,Xi'an 710038,China)

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125105 [2]空军军医大学生物医学工程系,西安710032 [3]西北大学生命科学学院,西安710127 [4]空军军医大学唐都医院超声诊断科,西安710038

出　　处：《中国生物医学工程学报》2024年第5期539-549,共11页Chinese Journal of Biomedical Engineering

基　　金：国家重点研发计划课题(2022YFC2404803);科技委基础加强计划重点基础研究项目(2019-JCJQ-ZD-115-00-02);国家自然科学基金青年基金(52207008)。

摘　　要：心脏射血与肺通气活动信息的实时获取具有重要临床意义。本研究提出了一种基于胸部电阻抗断层成像(EIT)的心肺信号降维集合经验模态分解方法,以同时分离胸部EIT数据中的心脏射血和肺通气活动信号。招募9名志愿者进行了EIT胸部数据采集。首先,根据屏息状态下胸部EIT数据中心脏活动信号的强弱对测量通道分类;随后,使用集合经验模态分解方法对自主呼吸状态下的EIT数据进行分解,并根据频谱特性对分解出的各分量归类,以得到肺通气EIT信号;然后,结合带通滤波方法,同时依据前述通道分类对心脏活动信号降维,得到心脏活动EIT信号;最后,重构得到通气相和心搏相EIT图像序列。结果表明,该方法可在通气相图像的肺区能够获得最高的肺通气功率谱峰(52.71±1.39)dB,在心搏相图像的心脏区域能够获得最高的心脏活动功率谱峰(43.05±3.26)dB,表明保留的通气信息和心脏活动信息非常丰富,同时在通气相图像心脏区域获得了最低心脏活动相关功率谱峰(10.02±2.65)dB,表明心脏活动的抑制效果更佳,相较于参考方法均有显著性差异(P<0.05)。研究表明,该方法可以有效分离肺通气与心脏活动相关信号,分别保留各自活动信息并抑制心脏对肺区成像的影响,同时实现对干扰信号的有效抑制,为临床上提供更加准确的治疗策略指导奠定基础。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 info

关 键 词：电阻抗断层成像 集合经验模态分解 心脏活动相关信号 肺通气 

分 类 号：R318[医药卫生—生物医学工程]