{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T06:18:48Z","timestamp":1778653128496,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20\u201330% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.<\/jats:p>","DOI":"10.3390\/e23070847","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T02:44:39Z","timestamp":1625107479000},"page":"847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2855-1344","authenticated-orcid":false,"given":"Jon","family":"Urteaga","sequence":"first","affiliation":[{"name":"Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6409-1429","authenticated-orcid":false,"given":"Elisabete","family":"Aramendi","sequence":"additional","affiliation":[{"name":"Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain"},{"name":"Biocruces Bizkaia Health Research Institute, Cruces University Hospital, 48903 Baracaldo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4401-7376","authenticated-orcid":false,"given":"Andoni","family":"Elola","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of the Basque Country, 48013 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9521-1852","authenticated-orcid":false,"given":"Unai","family":"Irusta","sequence":"additional","affiliation":[{"name":"Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain"},{"name":"Biocruces Bizkaia Health Research Institute, Cruces University Hospital, 48903 Baracaldo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahamed","family":"Idris","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.resuscitation.2005.03.021","article-title":"Incidence of EMS-treated out-of-hospital cardiac arrest in Europe","volume":"67","author":"Atwood","year":"2005","journal-title":"Resuscitation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1016\/j.resuscitation.2010.08.006","article-title":"Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies","volume":"81","author":"Berdowski","year":"2010","journal-title":"Resuscitation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.resuscitation.2015.07.015","article-title":"European Resuscitation Council Guidelines for Resuscitation 2015: Section 2. Adult basic life support and automated external defibrillation","volume":"95","author":"Perkins","year":"2015","journal-title":"Resuscitation"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1109\/TBME.2017.2688380","article-title":"ECG-based classification of resuscitation cardiac rhythms for retrospective data analysis","volume":"64","author":"Rad","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.resuscitation.2019.12.042","article-title":"Survival after out-of-hospital cardiac arrest in Europe-Results of the EuReCa TWO study","volume":"148","author":"Wnent","year":"2020","journal-title":"Resuscitation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.resuscitation.2020.03.014","article-title":"Occurrence of shockable rhythm in out-of-hospital cardiac arrest over time: A report from the COSTA group","volume":"151","author":"Oving","year":"2020","journal-title":"Resuscitation"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1161\/CIRCULATIONAHA.113.004490","article-title":"Pulseless electric activity: Definition, causes, mechanisms, management, and research priorities for the next decade: Report from a National Heart, Lung, and Blood Institute workshop","volume":"128","author":"Myerburg","year":"2013","journal-title":"Circulation"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.resuscitation.2012.03.033","article-title":"Out-of-hospital cardiac arrest outcomes stratified by rhythm analysis","volume":"83","author":"Mader","year":"2012","journal-title":"Resuscitation"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ahj.2016.04.018","article-title":"Factors associated with out-of-hospital cardiac arrest with pulseless electric activity: A population-based study","volume":"177","author":"Ko","year":"2016","journal-title":"Am. Heart J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1097\/CCM.0b013e3181b43282","article-title":"Rhythms and outcomes of adult in-hospital cardiac arrest","volume":"38","author":"Meaney","year":"2010","journal-title":"Crit. Care Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.resuscitation.2019.01.006","article-title":"Rhythm characteristics and patterns of change during cardiopulmonary resuscitation for in-hospital paediatric cardiac arrest","volume":"135","author":"Nordseth","year":"2019","journal-title":"Resuscitation"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.resuscitation.2018.01.041","article-title":"Initial electrical frequency predicts survival and neurological outcome in out of hospital cardiac arrest patients with pulseless electrical activity","volume":"125","author":"Weiser","year":"2018","journal-title":"Resuscitation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.ajem.2015.03.050","article-title":"Cardiac arrest with initial arrest rhythm of pulseless electrical activity: Do rhythm characteristics correlate with outcome?","volume":"33","author":"Hauck","year":"2015","journal-title":"Am. J. Emerg. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.resuscitation.2018.03.039","article-title":"ECG changes during resuscitation of patients with initial pulseless electrical activity are associated with return of spontaneous circulation","volume":"127","author":"Skjeflo","year":"2018","journal-title":"Resuscitation"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.resuscitation.2016.03.029","article-title":"ECG patterns in early pulseless electrical activity-Associations with aetiology and survival of in-hospital cardiac arrest","volume":"104","author":"Bergum","year":"2016","journal-title":"Resuscitation"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.ajem.2010.08.017","article-title":"Pulseless electrical activity in cardiac arrest: Electrocardiographic presentations and management considerations based on the electrocardiogram","volume":"30","author":"Mehta","year":"2012","journal-title":"Am. J. Emerg. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1159\/000513431","article-title":"Pulseless Electrical Activity: Detection of Underlying Causes in a Prehospital Setting","volume":"30","author":"Wauters","year":"2021","journal-title":"Med. Princ. Pract."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.ajem.2019.158503","article-title":"Pseudo-pulseless electrical activity in the emergency department, an evidence based approach","volume":"38","author":"Rabjohns","year":"2020","journal-title":"Am. J. Emerg. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.resuscitation.2015.03.024","article-title":"Echocardiography for prognostication during the resuscitation of intensive care unit patients with non-shockable rhythm cardiac arrest","volume":"92","author":"Flato","year":"2015","journal-title":"Resuscitation"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1177\/147323001003800428","article-title":"Impact of modified treatment in echocardiographically confirmed pseudo-pulseless electrical activity in out-of-hospital cardiac arrest patients with constant end-tidal carbon dioxide pressure during compression pauses","volume":"38","author":"Prosen","year":"2010","journal-title":"J. Int. Med. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.resuscitation.2006.10.001","article-title":"Thoracic-impedance changes measured via defibrillator pads can monitor signs of circulation","volume":"73","author":"Losert","year":"2007","journal-title":"Resuscitation"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.resuscitation.2015.11.014","article-title":"Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads","volume":"99","author":"Alonso","year":"2016","journal-title":"Resuscitation"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1109\/TBME.2020.3030216","article-title":"Multimodal algorithms for the classification of circulation states during out-of-hospital cardiac arrest","volume":"68","author":"Elola","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1016\/j.resuscitation.2014.08.022","article-title":"Beyond ventricular fibrillation analysis: Comprehensive waveform analysis for all cardiac rhythms occurring during resuscitation","volume":"85","author":"Alonso","year":"2014","journal-title":"Resuscitation"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lasa, H., Irusta, U., Eftestol, T., Aramendi, E., Rad, A.B., Kramer-Johansen, J., and Wik, L. (2020, January 13\u201316). Multimodal Biosignal Analysis Algorithm for the Classification of Cardiac Rhythms During Resuscitation. Proceedings of the 2020 Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.347"},{"key":"ref_26","unstructured":"Urteaga Urizarbarrena, J., Aramendi Ecenarro, E., Elola Artano, A., Irusta Zarandona, U., and Idris, A. (2020, January 25\u201327). Modelo predictivo del retorno de circulaci\u00f3n espont\u00e1nea en la parada cardiorrespiratoria utilizando el ECG y la impedancia tor\u00e1cica. Proceedings of the Libro de Actas del XXXVIII Congreso Anual de la Sociedad Espa\u00f1ola de Ingenier\u00eda Biom\u00e9dica CASEIB, Madrid, Spain."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.resuscitation.2014.04.007","article-title":"Automatic detection of chest compressions for the assessment of CPR-quality parameters","volume":"85","author":"Ayala","year":"2014","journal-title":"Resuscitation"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.resuscitation.2008.08.007","article-title":"Transthoracic impedance used to evaluate performance of cardiopulmonary resuscitation during out of hospital cardiac arrest","volume":"79","author":"Stecher","year":"2008","journal-title":"Resuscitation"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/TBME.2018.2878910","article-title":"A machine learning shock decision algorithm for use during piston-driven chest compressions","volume":"66","author":"Isasi","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Isasi, I., Irusta, U., Elola, A., Aramendi, E., Eftest\u00f8l, T., Kramer-Johansen, J., and Wik, L. (2019, January 23\u201327). A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856784"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1016\/j.resuscitation.2013.05.020","article-title":"Reliable extraction of the circulation component in the thoracic impedance measured by defibrillation pads","volume":"84","author":"Ruiz","year":"2013","journal-title":"Resuscitation"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TBME.2007.910644","article-title":"Automatic identification of return of spontaneous circulation during cardiopulmonary resuscitation","volume":"55","author":"Risdal","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","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":"12","author":"Hamilton","year":"1986","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhao, Z., S\u00e4rkk\u00e4, S., and Rad, A.B. (2018, January 17\u201320). Spectro-temporal ECG analysis for atrial fibrillation detection. Proceedings of the 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark.","DOI":"10.1109\/MLSP.2018.8517085"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s11517-018-1892-2","article-title":"ECG-based pulse detection during cardiac arrest using random forest classifier","volume":"57","author":"Elola","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.resuscitation.2018.04.036","article-title":"Circulation assessment by automated external defibrillators during cardiopulmonary resuscitation","volume":"128","author":"Ruiz","year":"2018","journal-title":"Resuscitation"},{"key":"ref_37","unstructured":"Chicote, B., Irusta, U., Aramendi, E., Isasi, I., Alonso, D., Vicente, F., and Sanchez, M. (2016, January 11\u201314). Nonlinear energy operators for defibrillation shock outcome prediction. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1088\/0967-3334\/25\/5\/007","article-title":"Real time detection of ventricular fibrillation and tachycardia","volume":"25","author":"Jekova","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIM.2002.808031","article-title":"Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data","volume":"51","author":"Bos","year":"2002","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Elola, A., Aramendi, E., Rueda, E., Irusta, U., Wang, H., and Idris, A. (2020). Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest. Entropy, 22.","DOI":"10.3390\/e22070758"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"115147","DOI":"10.1109\/ACCESS.2019.2935096","article-title":"Automatic cardiac rhythm classification with concurrent manual chest compressions","volume":"7","author":"Isasi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TBME.2003.821031","article-title":"A wavelet-based ECG delineator: Evaluation on standard databases","volume":"51","author":"Almeida","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.resuscitation.2021.02.010","article-title":"European Resuscitation Council Guidelines 2021: Adult advanced life support","volume":"161","author":"Soar","year":"2021","journal-title":"Resuscitation"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.resuscitation.2018.08.018","article-title":"Capnography during cardiac arrest","volume":"132","author":"Sandroni","year":"2018","journal-title":"Resuscitation"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.resuscitation.2015.06.023","article-title":"Cerebral oximetry and return of spontaneous circulation after cardiac arrest: A systematic review and meta-analysis","volume":"94","author":"Sanfilippo","year":"2015","journal-title":"Resuscitation"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"736","DOI":"10.3389\/fmed.2020.587930","article-title":"Near-infrared spectroscopy assessments of regional cerebral oxygen saturation for the prediction of clinical outcomes in patients with cardiac arrest: A review of clinical impact, evolution, and future directions","volume":"7","author":"Takegawa","year":"2020","journal-title":"Front. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1016\/j.resuscitation.2010.07.013","article-title":"Focused echocardiographic evaluation in life support and peri-resuscitation of emergency patients: A prospective trial","volume":"81","author":"Breitkreutz","year":"2010","journal-title":"Resuscitation"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e3960","DOI":"10.1097\/MD.0000000000003960","article-title":"Role of blood gas analysis during cardiopulmonary resuscitation in out-of-hospital cardiac arrest patients","volume":"95","author":"Kim","year":"2016","journal-title":"Medicine"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Elola, A., Aramendi, E., Irusta, U., Pic\u00f3n, A., Alonso, E., Owens, P., and Idris, A. (2019). Deep neural networks for ECG-based pulse detection during out-of-hospital cardiac arrest. Entropy, 21.","DOI":"10.3390\/e21030305"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Isasi, I., Irusta, U., Aramendi, E., Eftest\u00f8l, T., Kramer-Johansen, J.K.J., and Wik, L. (2020). Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks. Entropy, 22.","DOI":"10.3390\/e22060595"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"154746","DOI":"10.1109\/ACCESS.2020.3018704","article-title":"Shock Decision Algorithms for Automated External Defibrillators Based on Convolutional Networks","volume":"8","author":"Jaureguibeitia","year":"2020","journal-title":"IEEE Access"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/7\/847\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:23Z","timestamp":1760163863000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/7\/847"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":52,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["e23070847"],"URL":"https:\/\/doi.org\/10.3390\/e23070847","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}