{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:33:40Z","timestamp":1760060020328,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia (FCT), I.P.","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0117\/2020","UIDB\/00297\/2020","UIDP\/00297\/2020","UID\/04516\/NOVA"],"award-info":[{"award-number":["DSAIPA\/DS\/0117\/2020","UIDB\/00297\/2020","UIDP\/00297\/2020","UID\/04516\/NOVA"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Laboratory for Computer Science and Informatics (NOVA LINCS)","award":["DSAIPA\/DS\/0117\/2020","UIDB\/00297\/2020","UIDP\/00297\/2020","UID\/04516\/NOVA"],"award-info":[{"award-number":["DSAIPA\/DS\/0117\/2020","UIDB\/00297\/2020","UIDP\/00297\/2020","UID\/04516\/NOVA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of 0.82\u00b10.13 was obtained with Method D versus 0.63\u00b10.09 with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality.<\/jats:p>","DOI":"10.3390\/jcm14155312","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T09:53:02Z","timestamp":1753696382000},"page":"5312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analyzing Heart Rate Variability for COVID-19 ICU Mortality Prediction Using Continuous Signal Processing Techniques"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3540-9636","authenticated-orcid":false,"given":"Guilherme","family":"David","sequence":"first","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8935-9578","authenticated-orcid":false,"given":"Andr\u00e9","family":"Louren\u00e7o","sequence":"additional","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"},{"name":"NOVA LINCS\u2014NOVA Laboratory for Computer Science and Informatics and CardioID Technologies, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6843-1935","authenticated-orcid":false,"given":"Cristiana P.","family":"Von Rekowski","sequence":"additional","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"},{"name":"NMS\u2014NOVA Medical School, FCM\u2014Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, Campo M\u00e1rtires da P\u00e1tria 130, 1169-056 Lisbon, Portugal"},{"name":"CHRC\u2014Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2945-1441","authenticated-orcid":false,"given":"Iola","family":"Pinto","sequence":"additional","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"},{"name":"NOVA Math\u2014Center for Mathematics and Applications, NOVA FCT\u2014NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-9755","authenticated-orcid":false,"given":"Cec\u00edlia R. C.","family":"Calado","sequence":"additional","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"},{"name":"iBB\u2014Institute for Bioengineering and Biosciences, i4HB\u2014The Associate Laboratory Institute for Health and Bioeconomy, IST\u2014Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal"}]},{"given":"Lu\u00eds","family":"Bento","sequence":"additional","affiliation":[{"name":"Intensive Care Department, ULSSJ\u2014Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, Rua Jos\u00e9 Ant\u00f3nio Serrano, 1150-199 Lisbon, Portugal"},{"name":"Integrated Pathophysiological Mechanisms, CHRC\u2014Comprehensive Health Research Centre, NMS\u2014NOVA Medical School, FCM\u2014Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, Campo M\u00e1rtires da P\u00e1tria 130, 1169-056 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"unstructured":"Huhtaniemi, I., and Martini, L. (2019). Antiadrenergic Agents. Encyclopedia of Endocrine Diseases, Academic Press. [2nd ed.].","key":"ref_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.pcad.2008.01.003","article-title":"The role of the autonomic nervous system in sudden cardiac death","volume":"50","author":"Vaseghi","year":"2008","journal-title":"Prog. Cardiovasc. Dis."},{"doi-asserted-by":"crossref","unstructured":"Shaffer, F., and Ginsberg, J.P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Front. Public Health, 5.","key":"ref_3","DOI":"10.3389\/fpubh.2017.00258"},{"doi-asserted-by":"crossref","unstructured":"Jarczok, M.N., Weimer, K., Braun, C., Williams, D.P., Thayer, J.F., G\u00fcndel, H.O., and Balint, E.M. (2022). Heart rate variability in the prediction of mortality: A systematic review and meta-analysis of healthy and patient populations. Neurosci. Biobehav. Rev., 143.","key":"ref_4","DOI":"10.1016\/j.neubiorev.2022.104907"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e160721189770","DOI":"10.2174\/1573403X16999201231203854","article-title":"Analysis of Heart Rate Variability and Implication of Different Factors on Heart Rate Variability","volume":"17","author":"Tiwari","year":"2021","journal-title":"Curr. Cardiol. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1556\/APhysiol.88.2001.3-4.4","article-title":"Heart rate variability analysis","volume":"88","author":"Hejjel","year":"2001","journal-title":"Acta Physiol. Hung."},{"doi-asserted-by":"crossref","unstructured":"Costa, M.D., Davis, R.B., and Goldberger, A.L. (2017). Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics. Front. Physiol., 8.","key":"ref_7","DOI":"10.3389\/fphys.2017.00255"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1007\/s11517-011-0834-z","article-title":"Asymmetric properties of long-term and total heart rate variability","volume":"49","author":"Piskorski","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/22201181.2016.1202605","article-title":"Heart rate variability predicts 30-day all-cause mortality in intensive care units","volume":"22","author":"Bishop","year":"2016","journal-title":"S. Afr. J. Anaesth. Analg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.bbe.2015.05.004","article-title":"Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit","volume":"35","author":"Moridani","year":"2015","journal-title":"Biocybern. Biomed. Eng."},{"doi-asserted-by":"crossref","unstructured":"Liu, N., Chee, M.L., Foo, M.Z.Q., Pong, J.Z., Guo, D., Koh, Z.X., Ho, A.F.W., Niu, C., Chong, S.L., and Ong, M.E.H. (2021). Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department. PLoS ONE, 16.","key":"ref_12","DOI":"10.1101\/2020.12.26.20248866"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e34842","DOI":"10.1016\/j.heliyon.2024.e34842","article-title":"Heart rate variability and mortality in critically ill COVID-19 pneumonia patients","volume":"10","author":"Komaenthammasophon","year":"2024","journal-title":"Heliyon"},{"doi-asserted-by":"crossref","unstructured":"Mol, M.B.A., Strous, M.T.A., van Osch, F.H.M., Vogelaar, F.J., Barten, D.G., Farchi, M., Foudraine, N.A., and Gidron, Y. (2021). Heart-rate-variability (HRV), predicts outcomes in COVID-19. PLoS ONE, 16.","key":"ref_14","DOI":"10.1371\/journal.pone.0258841"},{"key":"ref_15","first-page":"74","article-title":"Exploration of COVID-19 associated bradycardia using heart rate variability analysis in a case-control study of ARDS patients","volume":"68","author":"Dumargne","year":"2024","journal-title":"Heart Lung J. Cardiopulm. Acute Care"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"481","DOI":"10.5935\/0103-507X.20170072","article-title":"Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review","volume":"29","author":"Bento","year":"2017","journal-title":"Rev. Bras. Ter. Intensiv."},{"key":"ref_17","first-page":"2399","article-title":"Analysis of six consecutive waves of ICU-admitted COVID-19 patients: Key findings and insights from a Portuguese population","volume":"47","author":"Pinto","year":"2025","journal-title":"GeroScience"},{"key":"ref_18","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","first-page":"42","article-title":"HDF: The hierarchical data format","volume":"23","author":"Fortner","year":"1998","journal-title":"Dr. Dobb\u2019s J. Softw. Tools Prof. Program."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1038\/s41746-023-00960-2","article-title":"Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU","volume":"6","author":"Lee","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1109\/TBME.1986.325695","article-title":"Quantitative investigation of QRS detection rules using the MIT\/BIH arrhythmia database","volume":"33","author":"Hamilton","year":"1986","journal-title":"IEEE Trans. Biomed. Eng."},{"unstructured":"Louppe, G. (2015). Understanding Random Forests: From Theory to Practice. arXiv.","key":"ref_22"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","key":"ref_24","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1002\/ehf2.15062","article-title":"Explainable machine learning and online calculators to predict heart failure mortality in intensive care units","volume":"12","author":"Chen","year":"2025","journal-title":"ESC Heart Fail."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e14197","DOI":"10.1097\/MD.0000000000014197","article-title":"Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department","volume":"98","author":"Chiew","year":"2019","journal-title":"Medicine"},{"key":"ref_27","first-page":"1341","article-title":"Advances in heart rate variability signal analysis: Joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society","volume":"17","author":"Sassi","year":"2015","journal-title":"EP Eur."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/15\/5312\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:17:08Z","timestamp":1760033828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/15\/5312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,28]]},"references-count":27,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jcm14155312"],"URL":"https:\/\/doi.org\/10.3390\/jcm14155312","relation":{},"ISSN":["2077-0383"],"issn-type":[{"type":"electronic","value":"2077-0383"}],"subject":[],"published":{"date-parts":[[2025,7,28]]}}}