A Novel Mixed Stimulation Pattern for Balanced Pulmonary EIT Imaging Performance.

Zhibo Zhao, Zhijun Gao, Heyao Zhu, Zhanqi Zhao, Meng Dai, Zilong Liu, Feng Fu, Lin Yang

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Zhibo ZhaoShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, The Fourth Military Medical University, Xi'an 710032, China.

Zhijun GaoDepartment of Aerospace Medicine, The Fourth Military Medical University, Xi'an 710032, China.

Heyao ZhuShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, The Fourth Military Medical University, Xi'an 710032, China.ORCID 0009-0004-0974-4484

Zhanqi ZhaoSchool of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China.

Meng DaiShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, The Fourth Military Medical University, Xi'an 710032, China.ORCID 0000-0003-4389-8888

Zilong LiuDepartment of Aerospace Medicine, The Fourth Military Medical University, Xi'an 710032, China.

Feng FuShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, The Fourth Military Medical University, Xi'an 710032, China.

Lin YangDepartment of Aerospace Medicine, The Fourth Military Medical University, Xi'an 710032, China.ORCID 0000-0003-1641-2926

Funding

Joint Founding Project of Innovation Research Institution Xijing Hospital LHJJ24YG03Key Research and Development Program of Shaanxi Province 2024SF-YBXM-074National Natural Science Foundation of China 52277235National Natural Science Foundation of China 62401373National Natural Science Foundation of China 82470088

6 · The paper itself

Abstract

Pulmonary electrical impedance tomography (EIT) offers non-invasive and real-time imaging in a compact device size, making it valuable for pulmonary ventilation monitoring. However, conventional EIT stimulation patterns face a trade-off dilemma between anti-noise performance and image interpretability. To address this challenge, we propose a novel mixed stimulation pattern that integrates opposite and adjacent stimulation patterns with a tunable weight ratio. The results of simulations and human experiments (involving 30 subjects) demonstrated that the mixed stimulation pattern uses 200 stimulation-measurement channels, preserves a high signal-to-noise ratio, improves lung separation, and reduces artifacts compared with the opposite and adjacent stimulation patterns. It maintained stable imaging at 600 μA of stimulation current amplitude (equivalent to 1 mA) and preserved most imaging and clinical indicators' stability at 200 μA (except GI/RVD

Indexed as

anti-noise performanceelectrical impedance tomographyimage interpretabilitystimulation pattern

Identifiers

PMID41596004

DOI10.3390/bioengineering13010072

PMCPMC12837143

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.