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Add reference to existing EIT software packages
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paper/paper.bib

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@@ -285,3 +285,22 @@ @article{Wisse2024-sl
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language = {en},
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doi = {10.1186/s40635-024-00686-9}
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}
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@article{liu2018pyeit,
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title={pyEIT: A python based framework for Electrical Impedance Tomography},
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author={Liu, Benyuan and Yang, Bin and Xu, Canhua and Xia, Junying and Dai, Meng and Ji, Zhenyu and You, Fusheng and Dong, Xiuzhen and Shi, Xuetao and Fu, Feng},
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journal={SoftwareX},
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volume={7},
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pages={304--308},
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year={2018},
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publisher={Elsevier}
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}
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@article{EIDORS2005,
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author = {Adler, Andy and Lionheart, William},
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year = {2005},
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month = {01},
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pages = {},
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title = {EIDORS: Towards a community-based extensible software base for EIT}
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}

paper/paper.md

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@@ -89,8 +89,12 @@ findings and clinical implementation. The current limitations of EIT analysis st
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between physicians, clinical researchers and engineers in order to identify clinical needs, to
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develop and validate new algorithms, and to facilitate clinical implementation [@Scaramuzzo2024-ob].
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Currently, some open source EIT software packages are available [@liu2018pyeit;@EIDORS2005]. These, however, all focus on reconstruction of
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voltage data to images, bypassing the clinically used reconstruction algorithms implemented in CE-approved devices, and
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don't include tools for the analysis of reconstructed EIT image data.
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`eitprocessing` offers a standardized, open, and highly expandable library of tools for loading,
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filtering, segmentation and analysis of EIT data as well as related waveform or sparse data.
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filtering, segmentation and analysis of reconstructed EIT data as well as related waveform or sparse data.
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`eitprocessing` is compatible with data from the three most-used clinically available EIT devices, as well as
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from related data sources, such as mechanical ventilators and dedicated pressure devices. It includes commonly used methods for filtering and segmentation. The
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authors continuously develop and implement further algorithms for analysis. The

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