March 27, 2024 Dataset Open Access

Wimmert, Lukas; Madesta, Frederic; Gauer, Tobias; Werner, Rene

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{"@context":"https://schema.org/","@id":"http://doi.org/10.25592/uhhfdm.17691","@type":"Dataset","creator":[{"@type":"Person","affiliation":"Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf","name":"Wimmert, Lukas"},{"@id":"https://orcid.org/0000-0001-8987-7690","@type":"Person","affiliation":"Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf","name":"Madesta, Frederic"},{"@type":"Person","affiliation":"Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany","name":"Gauer, Tobias"},{"@id":"https://orcid.org/0000-0002-2296-3305","@type":"Person","affiliation":"Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf","name":"Werner, Rene"}],"datePublished":"2024-03-27","description":"<pre>This SQLite-based database contains 2,510 respiratory signals from 419 patients (total acquisition time &gt; 90 hours) with thoracic lesions treated between February 2013 and May 2022 at the Clinic of Radiotherapy and Radiation Oncology of the University Medical Center Hamburg-Eppendorf.\n\nWe believe this comprehensive dataset is of high value to the radiotherapy community as well as to researchers working on time-series analysis tasks such as forecasting and classification.\nOpen access to these retrospectively collected and anonymized respiratory signals was approved by the local ethics board, with the need for written informed consent waived [2023-300334-WF].</pre>\n\n<pre><strong>Usage</strong>\nPlease refer to the corresponding <a href=\"https://github.com/IPMI-ICNS-UKE/respiratory-signal-database\">GitHub repository</a> for data reading and preprocessing functionalities.  \nAdditionally, review the provided README to understand the database structure.\nWe recommend using <a href=\"https://sqlitebrowser.org/\">DB Browser for SQLite</a> as a convenient tool for browsing the database.\n\n<strong>Data</strong>\nAll respiratory signals were recorded in the course of radiotherapy treatment. \nSignals were acquired using the <em>Varian RPM System</em> during:  \n- 4D CT imaging (sampling rate: 25 Hz)  \n- 4D CBCT imaging (66 Hz)  \n- Dose delivery (66 Hz)  \n\nThe <em>Varian RPM System</em> monitors an external marker block placed on the patient&rsquo;s chest wall with an infrared camera.\nFrom the obtained marker block signal, only the one-dimensional signal component representing the vertical displacement (anterior-posterior) of the chest wall is considered, resulting in univariate time series.\nAll patients breathed freely during acquisition without visual guidance or coaching.\nPlease also refer to the provided acquisition and example images for additional context.\n</pre>\n\n<pre><strong>Citation</strong>\nIf you use this database, please also cite the underlying publication:\n@article{wimmert2024benchmarking,\n  doi={10.1002/mp.17038}\n  title={Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT},\n  author={Wimmert, Lukas and Nielsen, Maximilian and Madesta, Frederic and Gauer, Tobias and Hofmann, Christian and Werner, Rene},\n  journal={Medical Physics},\n  year={2024},\n  publisher={Wiley Online Library}\n}\n</pre>\n\n<p>&nbsp;</p>","distribution":[{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/acquisition_camera.png","encodingFormat":"png"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/acquisition_setup.png","encodingFormat":"png"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/entity-relationship-diagram.png","encodingFormat":"png"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/example_signal.png","encodingFormat":"png"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/marker_block.png","encodingFormat":"png"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/open_access_rpm_signals_master.db","encodingFormat":"db"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/patient_view.png","encodingFormat":"png"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/08963015-66ee-428d-aa88-beda0869e74b/README.md","encodingFormat":"md"}],"identifier":"http://doi.org/10.25592/uhhfdm.17691","isPartOf":[{"@id":"http://doi.org/10.1002/mp.17038","@type":"CreativeWork"}],"keywords":["univariate time-series, radiotherapy, free-breathing"],"license":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"RespDB - A Respiratory Signal Database","url":"https://www.fdr.uni-hamburg.de/record/17691","version":"1.0.1"}