{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:11:55Z","timestamp":1783163515835,"version":"3.54.6"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of Innsbruck and Medical University of Innsbruck"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence offers great opportunities in critical care, particularly when a vast amount of continuously acquired physiological data is incorporated. High-quality, reliably labelled data are paramount for developing and training artificial intelligence methods. However, routinely recorded data in critical care are often noisy, and the sheer volume of high-resolution data is challenging to manage. Generalizable solutions for these problems are lacking, restricting progress. To address these barriers, we developed\n                    <jats:italic>Vitabel<\/jats:italic>\n                    , an open-source\n                    <jats:italic>Python<\/jats:italic>\n                    framework for post hoc loading, visualizing, aligning, and annotating medical time series. The framework provides sensible defaults and interactive components for efficient use in preconfigured workflows, while remaining flexible and extendable for custom analysis and annotation pipelines. It integrates seamlessly into\n                    <jats:italic>Jupyter Notebooks<\/jats:italic>\n                    , providing an interactive, customizable interface for visual interaction with the data. In this publication, we demonstrate its utility across three use cases. The code and exemplary data are provided as browser-based demos.\n                    <jats:italic>Vitabel<\/jats:italic>\n                    is freely available and published under the MIT license accompanying this publication.\n                  <\/jats:p>","DOI":"10.1007\/s10916-026-02417-x","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T08:18:02Z","timestamp":1780993082000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7799-4822","authenticated-orcid":false,"given":"Simon","family":"Orlob","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5080-382X","authenticated-orcid":false,"given":"Wolfgang J.","family":"Kern","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2998-9599","authenticated-orcid":false,"given":"Benjamin","family":"Hackl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8685-858X","authenticated-orcid":false,"given":"Jan","family":"Wnent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8143-0376","authenticated-orcid":false,"given":"Jan-Thorsten","family":"Gr\u00e4sner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-2375","authenticated-orcid":false,"given":"Martin","family":"Holler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"2417_CR1","doi-asserted-by":"publisher","unstructured":"Anthony\u00a0Celi, L., Mark, R. G., Stone, D. J., and Montgomery, R. A., \u201cBig Data\u201d in the intensive care unit. Closing the data loop. Am. J. Respir. Crit. Care Med. 187(11):1157\u20131160, 2013. https:\/\/doi.org\/10.1164\/rccm.201212-2311ED.","DOI":"10.1164\/rccm.201212-2311ED"},{"key":"2417_CR2","doi-asserted-by":"publisher","unstructured":"Cecconi, M., Greco, M., Shickel, B., Angus, D. C., Bailey, H., Bignami, E., et\u00a0al., Implementing artificial intelligence in critical care medicine: A consensus of 22. Crit. Care. 29(1):290, 2025. https:\/\/doi.org\/10.1186\/s13054-025-05532-2.","DOI":"10.1186\/s13054-025-05532-2"},{"key":"2417_CR3","doi-asserted-by":"crossref","unstructured":"Hildebrandt, M., Ground-Truthing in the European Health Data Space:. In: Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies. Lisbon, Portugal: SCITEPRESS - Science and Technology Publications; 2023. p. 15\u201322.","DOI":"10.5220\/0011955900003414"},{"key":"2417_CR4","doi-asserted-by":"publisher","unstructured":"Lee, H. C., and Jung, C. W., Vital recorder\u2014a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices. Sci. Rep. 8(1):1527, 2018. https:\/\/doi.org\/10.1038\/s41598-018-20062-4.","DOI":"10.1038\/s41598-018-20062-4"},{"key":"2417_CR5","doi-asserted-by":"publisher","unstructured":"Karippacheril, J. G., and Ho, T. Y., Data acquisition from S\/5 GE Datex anesthesia monitor using VSCapture: An Open Source.NET\/Mono Tool. J. Anaesthesiol. Clin. Pharmacol. 29(3):423\u2013424, 2013. https:\/\/doi.org\/10.4103\/0970-9185.117096.","DOI":"10.4103\/0970-9185.117096"},{"key":"2417_CR6","doi-asserted-by":"publisher","unstructured":"Xie, C., McCullum, L., Johnson, A., Pollard, T., Gow, B., and Moody, B., Waveform Database Software Package (WFDB) for Python. PhysioNet. Version 4.1.0, 2023. https:\/\/doi.org\/10.13026\/9njx-6322.","DOI":"10.13026\/9njx-6322"},{"key":"2417_CR7","unstructured":"Moody, G. B., LightWAVE: Waveform and annotation viewing and editing in aWeb browser. Comput. Cardiol. 2013:17\u201320, 2013."},{"key":"2417_CR8","unstructured":"ADInstruments., LabChart. version 8.1.30. Available from: https:\/\/www.adinstruments.com\/products\/labchart."},{"key":"2417_CR9","unstructured":"van Beelen, T., EDFBrowser. version 2.14-1. Available from: https:\/\/www.teuniz.net\/edfbrowser\/."},{"key":"2417_CR10","doi-asserted-by":"publisher","unstructured":"Silva, L. E. V., Fazan, R., and Marin-Neto, J. A., PyBioS: A freeware computer software for analysis of cardiovascular signals. Comput. Methods Progr. Biomed. 197:105718, 2020. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105718.","DOI":"10.1016\/j.cmpb.2020.105718"},{"key":"2417_CR11","doi-asserted-by":"publisher","unstructured":"Maheshwari, K., Khanna, S., Bajracharya, G. R., Makarova, N., Riter, Q., Raza, S., et\u00a0al., A randomized trial of continuous noninvasive blood pressure monitoring during noncardiac surgery. Anesthesia & Analgesia. 127(2):424\u2013431, 2018 . https:\/\/doi.org\/10.1213\/ANE.0000000000003482.","DOI":"10.1213\/ANE.0000000000003482"},{"key":"2417_CR12","doi-asserted-by":"publisher","unstructured":"McKinney, W., Data structures for statistical computing in Python. Scipy, 2010. https:\/\/doi.org\/10.25080\/Majora-92bf1922-00a.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"2417_CR13","doi-asserted-by":"publisher","unstructured":"Orlob, S., Kern, W. J., Alpers, B., Sch\u00f6rghuber, M., Bohn, A., Holler, M., et\u00a0al., Chest Compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data - tested in Zoll X series. Resuscitation. 172:162\u2013169, 2022. https:\/\/doi.org\/10.1016\/j.resuscitation.2021.12.028.","DOI":"10.1016\/j.resuscitation.2021.12.028"},{"key":"2417_CR14","doi-asserted-by":"publisher","unstructured":"Kern, W. J., Orlob, S., Alpers, B., Sch\u00f6rghuber, M., Bohn, A., Holler, M., et\u00a0al., A sliding-window based algorithm to determine the presence of chest compressions from acceleration data. Data Brief. 41:107973, 2022. https:\/\/doi.org\/10.1016\/j.dib.2022.107973.","DOI":"10.1016\/j.dib.2022.107973"},{"key":"2417_CR15","doi-asserted-by":"publisher","unstructured":"Kern, W. J., Orlob, S., Bohn, A., Toller, W., Wnent, J., Gr\u00e4sner, J. T., et\u00a0al., Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest. IEEE Trans. Biomed. Eng. 70(8):2310\u20132317, 2023. https:\/\/doi.org\/10.1109\/tbme.2023.3242717.","DOI":"10.1109\/tbme.2023.3242717"},{"key":"2417_CR16","doi-asserted-by":"publisher","unstructured":"Aramendi, E., Elola, A., Alonso, E., Irusta, U., Daya, M., Russell, J. K., et\u00a0al., Feasibility of the capnogram to monitor ventilation rate during cardiopulmonary resuscitation. Resuscitation. 110:162\u2013168, 2017. https:\/\/doi.org\/10.1016\/j.resuscitation.2016.08.033.","DOI":"10.1016\/j.resuscitation.2016.08.033"},{"key":"2417_CR17","doi-asserted-by":"publisher","unstructured":"Orlob, S., Purkarthofer, D., Grobbel, M., Holler, M., Furtm\u00fcller, M., Wittig, J., et\u00a0al., Multiplying flow and pressure: Detecting respiratory phases in intra-arrest ventilation. Resuscitation. 111050, 2026. https:\/\/doi.org\/10.1016\/j.resuscitation.2026.111050.","DOI":"10.1016\/j.resuscitation.2026.111050"},{"key":"2417_CR18","doi-asserted-by":"publisher","unstructured":"Segond, N., Wittig, J., Kern, W. J., Orlob, S., Towards a common terminology of ventilation during cardiopulmonary resuscitation. Resuscitation. 207:110511, 2025. https:\/\/doi.org\/10.1016\/j.resuscitation.2025.110511.","DOI":"10.1016\/j.resuscitation.2025.110511"},{"key":"2417_CR19","doi-asserted-by":"publisher","unstructured":"Hunter, J. D., Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9(3):90\u201395, 2007. https:\/\/doi.org\/10.1109\/MCSE.2007.55.","DOI":"10.1109\/MCSE.2007.55"},{"key":"2417_CR20","unstructured":"Orlob, S., Hackl, B., Kern, W. J., Vitabel: First use case. MyBinder. Available from: https:\/\/mybinder.org\/v2\/gh\/UniGrazMath\/vitabel\/v0.1.1?urlpath=%2Fdoc%2Ftree%2Fexamples%2F01_defibrillator.ipynb."},{"key":"2417_CR21","unstructured":"Orlob, S., Hackl, B., Kern, W. J., Vitabel: Second use case. MyBinder. Available from: https:\/\/mybinder.org\/v2\/gh\/UniGrazMath\/vitabel\/v0.1.1?urlpath=%2Fdoc%2Ftree%2Fexamples%2F02_animal_lab.ipynb."},{"key":"2417_CR22","unstructured":"Orlob, S., Hackl, B., Kern, W. J., Vitabel: Third use case. MyBinder. Available from: https:\/\/mybinder.org\/v2\/gh\/UniGrazMath\/vitabel\/v0.1.1?urlpath=%2Fdoc%2Ftree%2Fexamples%2F03_anaesthesia_chart.ipynb."},{"key":"2417_CR23","doi-asserted-by":"publisher","unstructured":"Kern, W. J., Orlob, S., Putzer, G., Martini, J., Holler, M. A., Parameter identification approach towards analyzing hemodynamics based on capnography. 2023 Comput. Cardiol. (CinC). 50:1\u20134, 2023. https:\/\/doi.org\/10.22489\/cinc.2023.086.","DOI":"10.22489\/cinc.2023.086"},{"key":"2417_CR24","doi-asserted-by":"publisher","unstructured":"Wnent, J., Gr\u00e4sner, J. T., Fischer, M., Ramshorn-Zimmer, A., Bohn, A., Bein, B., et\u00a0al., The German resuscitation registry \u2013 epidemiological data for out-of-hospital and in-hospital cardiac arrest. Resuscit. Plus. 18:100638, 2024. https:\/\/doi.org\/10.1016\/j.resplu.2024.100638.","DOI":"10.1016\/j.resplu.2024.100638"},{"issue":"3","key":"2417_CR25","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.resuscitation.2007.01.024","volume":"74","author":"J Kramer-Johansen","year":"2007","unstructured":"Kramer-Johansen J, Edelson DP, Losert H, K\u00f6hler K, Abella BS. Uniform Reporting of Measured Quality of Cardiopulmonary Resuscitation (CPR). Resuscitation. 2007;74(3):406\u2013417. https:\/\/doi.org\/10.1016\/j.resuscitation.2007.01.024.","journal-title":"Resuscitation."},{"key":"2417_CR26","doi-asserted-by":"publisher","unstructured":"Nordseth, T., Eftest\u00f8l, T., Aramendi, E., Kval\u00f8y, J. T., Skogvoll, E., Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest. Resuscit Plus. 18:100611, 2024. https:\/\/doi.org\/10.1016\/j.resplu.2024.100611.","DOI":"10.1016\/j.resplu.2024.100611"},{"key":"2417_CR27","doi-asserted-by":"publisher","unstructured":"Orlob, S., Kern, W., Hackl, B., Streifert, D., Bohn, A., Fischer, M., et\u00a0al., Integrating defibrillator data in utstein registries - an opportunity for big data analysis within EuReCa. Resuscitation. 215:S24, 2025. https:\/\/doi.org\/10.1016\/S0300-9572(25)00413-7.","DOI":"10.1016\/S0300-9572(25)00413-7"},{"key":"2417_CR28","unstructured":"Orlob, S., Kern, W. J., Hackl, B., Wnent, J., Gr\u00e4sner, J. T., Holler, M., vitabel. Zenodo. Available from: https:\/\/doi.org\/10.5281\/zenodo.15771826."}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02417-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-026-02417-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02417-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T08:18:14Z","timestamp":1780993094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-026-02417-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,9]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2417"],"URL":"https:\/\/doi.org\/10.1007\/s10916-026-02417-x","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,9]]},"assertion":[{"value":"27 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Human data from the resuscitation attempt was obtained in anonymous form from the\n                      German Resuscitation Registry\n                      under the ethical approval of the institutional review board of the\n                      Christian-Albrecht University of Kiel\n                      (D 540\/24) and the approval of the Scientific Advisory Board of the registry (AZ: 2024-03). The animal experiment contributing data was approved by the\n                      Austrian Federal Ministry of Education, Science, and Research\u2019s\n                      ethics committee vote (GZ: 2021-0.895.386).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests regarding the manuscript\u2019s content. MH and WK acknowledge funding from the University of Graz within the project \u201cA machine learning approach towards data-driven cardiopulmonary resuscitation\u201d. The current position of SO is sponsored by the DAMP Foundation within the Cardiac Arrest Research Fellowship program.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"92"}}