{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T13:53:29Z","timestamp":1793109209624,"version":"4.5.0"},"reference-count":82,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Council for the discipline of Automatic Control, Electronics and Electrical Engineering of Warsaw University of Technology, Poland"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2\/SD1), and coefficient of variation\u2014standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective.<\/jats:p>","DOI":"10.3390\/jcm11144004","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T21:57:53Z","timestamp":1657576673000},"page":"4004","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2412-0739","authenticated-orcid":false,"given":"Szymon","family":"Bu\u015b","sequence":"first","affiliation":[{"name":"Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4303-9450","authenticated-orcid":false,"given":"Konrad","family":"J\u0119drzejewski","sequence":"additional","affiliation":[{"name":"Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9052-5027","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Guzik","sequence":"additional","affiliation":[{"name":"Department of Cardiology-Intensive Therapy and Internal Disease, Poznan University of Medical Sciences, 60-355 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1093\/ejcts\/ezw313","article-title":"2016 ESC Guidelines for the Management of Atrial Fibrillation Developed in Collaboration with EACTS","volume":"50","author":"Kirchhoff","year":"2016","journal-title":"Eur. J. Cardio-Thorac. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/eurheartj\/ehaa612","article-title":"2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation Developed in Collaboration with the European Association for Cardio-Thoracic Surgery (EACTS) The Task Force for the Diagnosis and Management of Atrial Fibrillation of the European Society of Cardiology (ESC) Developed with the Special Contribution of the European Heart Rhythm Association (EHRA) of the ESC","volume":"42","author":"Hindricks","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tonko, J.B., and Wright, M.J. (2021). Review of the 2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation\u2014What Has Changed and How Does This Affect Daily Practice. J. Clin. Med., 10.","DOI":"10.3390\/jcm10173922"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1161\/CIRCULATIONAHA.117.028981","article-title":"Sex Differences and Similarities in Atrial Fibrillation Epidemiology, Risk Factors, and Mortality in Community Cohorts: Results from the BiomarCaRE Consortium (Biomarker for Cardiovascular Risk Assessment in Europe)","volume":"136","author":"Magnussen","year":"2017","journal-title":"Circulation"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sagnard, A., Guenancia, C., Mouhat, B., Maza, M., Fichot, M., Moreau, D., Garnier, F., Lorgis, L., Cottin, Y., and Zeller, M. (2020). Involvement of Autonomic Nervous System in New-Onset Atrial Fibrillation during Acute Myocardial Infarction. J. Clin. Med., 9.","DOI":"10.3390\/jcm9051481"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ble, M., Benito, B., Cuadrado-Godia, E., P\u00e9rez-Fern\u00e1ndez, S., G\u00f3mez, M., Mas-Stachurska, A., Tiz\u00f3n-Marcos, H., Molina, L., Mart\u00ed-Almor, J., and Cladellas, M. (2021). Left Atrium Assessment by Speckle Tracking Echocardiography in Cryptogenic Stroke: Seeking Silent Atrial Fibrillation. J. Clin. Med., 10.","DOI":"10.3390\/jcm10163501"},{"key":"ref_7","first-page":"235","article-title":"Analysis of Atrial Fibrillation: From Electrocardiogram Signal Processing to Clinical Management","volume":"367","author":"Stridh","year":"2009","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_8","unstructured":"Langley, P., Bourke, J.P., and Murray, A. (2000, January 24\u201327). Frequency Analysis of Atrial Fibrillation. Proceedings of the Computers in Cardiology 2000 (Cat. 00CH37163), Cambridge, MA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/TBME.2004.827272","article-title":"Atrial Activity Extraction for Atrial Fibrillation Analysis Using Blind Source Separation","volume":"51","author":"Rieta","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1088\/0967-3334\/29\/12\/001","article-title":"Adaptive Singular Value Cancelation of Ventricular Activity in Single-Lead Atrial Fibrillation Electrocardiograms","volume":"29","author":"Alcaraz","year":"2008","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bu\u015b, S., and J\u0119drzejewski, K. (2019, January 17\u201319). Two Stage SVD-Based Method for QRST Waves Cancellation in Atrial Fibrillation Detection. Proceedings of the 2019 Signal Processing Symposium (SPSympo), Krakow, Poland.","DOI":"10.1109\/SPS.2019.8882032"},{"key":"ref_12","unstructured":"(1996). Heart Rate Variability: Standards of Measurement, Physiological Interpretation and Clinical Use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93, 1043\u20131065."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.hrthm.2016.12.015","article-title":"Reference Values of Heart Rate Variability","volume":"14","author":"Bauer","year":"2017","journal-title":"Heart Rhythm"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jelectrocard.2011.08.006","article-title":"Heart Rate Deceleration Runs for Postinfarction Risk Prediction","volume":"45","author":"Guzik","year":"2012","journal-title":"J. Electrocardiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ijcard.2018.01.056","article-title":"Classic Electrocardiogram-Based and Mobile Technology Derived Approaches to Heart Rate Variability Are Not Equivalent","volume":"258","author":"Guzik","year":"2018","journal-title":"Int. J. Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Michel, P., Ngo, N., Pons, J.-F., Delliaux, S., and Giorgi, R. (2021). A Filter Approach for Feature Selection in Classification: Application to Automatic Atrial Fibrillation Detection in Electrocardiogram Recordings. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01427-8"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bu\u015b, S., J\u0119drzejewski, K., and Guzik, P. (2021, January 20\u201323). A Study on Selection of HRV-based Features for Different Classifiers in Atrial Fibrillation Detection. Proceedings of the 2021 Signal Processing Symposium (SPSympo), Lodz, Poland.","DOI":"10.1109\/SPSympo51155.2020.9593769"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2016.07.016","article-title":"Paroxysmal Atrial Fibrillation Prediction Method with Shorter HRV Sequences","volume":"134","author":"Boon","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mustaqeem, A., Anwar, S.M., Majid, M., and Khan, A.R. (2017, January 11\u201315). Wrapper Method for Feature Selection to Classify Cardiac Arrhythmia. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037650"},{"key":"ref_20","unstructured":"de Chazal, P., and Heneghan, C. (2001, January 23\u201326). Automated Assessment of Atrial Fibrillation. Proceedings of the Computers in Cardiology 2001 (Cat. No.01CH37287), Rotterdam, The Netherlands."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Clifford, G.D., Liu, C., Moody, B., Li-wei, H.L., Silva, I., Li, Q., Johnson, A.E., and Mark, R.G. (2017, January 24\u201327). AF Classification from a Short Single Lead ECG Recording: The PhysioNet\/Computing in Cardiology Challenge 2017. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.065-469"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Datta, S., Puri, C., Mukherjee, A., Banerjee, R., Dutta Choudhury, A., Singh, R., Ukil, A., Bandyopadhyay, S., Pal, A., and Khandelwal, S. (2017, January 24\u201327). Identifying Normal, AF and Other Abnormal ECG Rhythms Using a Cascaded Binary Classifier. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.173-154"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez Serrano, S., Yag\u00fce Mayans, J., Simarro-Mond\u00e9jar, E., Calvo, C., Castells Ramon, F., and Roig, J. (2017, January 24\u201327). Atrial Fibrillation Detection Using Feedforward Neural Networks and Automatically Extracted Signal Features. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.341-131"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zabihi, M., Rad, A.B., Katsaggelos, A.K., Kiranyaz, S., Narkilahti, S., and Gabbouj, M. (2017, January 24\u201327). Detection of Atrial Fibrillation in ECG Hand-Held Devices Using a Random Forest Classifier. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.069-336"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Krasteva, V., Christov, I., Naydenov, S., Stoyanov, T., and Jekova, I. (2021). Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set. Sensors, 21.","DOI":"10.3390\/s21206848"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"094005","DOI":"10.1088\/1361-6579\/aad9f0","article-title":"Ranking of the Most Reliable Beat Morphology and Heart Rate Variability Features for the Detection of Atrial Fibrillation in Short Single-Lead ECG","volume":"39","author":"Christov","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shao, M., Zhou, Z., Bin, G., Bai, Y., and Wu, S. (2020). A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection. Sensors, 20.","DOI":"10.3390\/s20030606"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104367","DOI":"10.1016\/j.compbiomed.2021.104367","article-title":"Prediction of Paroxysmal Atrial Fibrillation Using New Heart Rate Variability Features","volume":"133","author":"Parsi","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1093\/ehjdh\/ztab071","article-title":"Atrial Fibrillation Risk Prediction from the 12-Lead Electrocardiogram Using Digital Biomarkers and Deep Representation Learning","volume":"2","author":"Biton","year":"2021","journal-title":"Eur. Heart J.-Digit. Health"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2140013","DOI":"10.1142\/S0219519421400133","article-title":"A Feature Selection-Based Algorithm for Detection of Atrial Fibrillation Using Short-Term ECG","volume":"21","author":"Zhu","year":"2021","journal-title":"J. Mech. Med. Biol."},{"key":"ref_31","unstructured":"Kotynia, M.M. (2021). Application for Atrial Fibrillation Classification Based on ECG Recordings. [Bachelor\u2019s Thesis, Warsaw University of Technology]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6920","DOI":"10.1109\/JSEN.2022.3153647","article-title":"Topological Data Analysis Approach to Extract the Persistent Homology Features of Ballistocardiogram Signal in Unobstructive Atrial Fibrillation Detection","volume":"22","author":"Jiang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102310","DOI":"10.1016\/j.bspc.2020.102310","article-title":"Heart Rate Variability Feature Selection Method for Automated Prediction of Sudden Cardiac Death","volume":"65","author":"Parsi","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_34","unstructured":"Oster, J., Behar, J., Colloca, R., Li, Q., Li, Q., and Clifford, G.D. (2013, January 22\u201325). Open Source Java-Based ECG Analysis Software and Android App for Atrial Fibrillation Screening. Proceedings of the Computing in Cardiology 2013, Zaragoza, Spain."},{"key":"ref_35","first-page":"177","article-title":"Detection of Atrial Fibrillation Episodes Using SVM","volume":"2008","author":"Mohebbi","year":"2008","journal-title":"IEEE Eng. Med. Biol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sepulveda-Suescun, J.P., Murillo-Escobar, J., Urda-Benitez, R.D., Orrego-Metaute, D.A., and Orozco-Duque, A. (2016, January 26\u201328). Atrial Fibrillation Detection through Heart Rate Variability Using a Machine Learning Approach and Poincare Plot Features. Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Colombia.","DOI":"10.1007\/978-981-10-4086-3_142"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Ansari, S., Hooshmand, M., Lin, K., Ghanbari, H., Gryak, J., and Najarian, K. (2018, January 18\u201321). Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512345"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"53566","DOI":"10.1109\/ACCESS.2018.2871220","article-title":"Automatic Atrial Fibrillation Detection Based on Heart Rate Variability and Spectral Features","volume":"6","author":"Mei","year":"2018","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/j.patcog.2007.03.008","article-title":"Atrial Fibrillation Classification with Artificial Neural Networks","volume":"40","author":"Kara","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1109\/TSMC.2017.2705582","article-title":"Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients","volume":"48","author":"Pourbabaee","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.compbiomed.2018.07.001","article-title":"Automated Detection of Atrial Fibrillation Using Long Short-Term Memory Network with RR Interval Signals","volume":"102","author":"Faust","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s10527-013-9324-y","article-title":"A Combined Algorithm for Identification and Differentiation of Atrial Flutter and Atrial Fibrillation Based on ECG Analysis","volume":"47","author":"Malakhov","year":"2013","journal-title":"Biomed. Eng."},{"key":"ref_43","first-page":"9159158","article-title":"Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices","volume":"2020","author":"Ma","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Marsili, I.A., Mase, M., Pisetta, V., Ricciardi, E., Andrighetti, A.O., Ravelli, F., and Nollo, G. (2016, January 12\u201315). Optimized Algorithms for Atrial Fibrillation Detection by Wearable Tele-Holter Devices. Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy.","DOI":"10.1109\/ISC2.2016.7580863"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e64","DOI":"10.3346\/jkms.2019.34.e64","article-title":"Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-Term Normal Electrocardiogram Signal","volume":"34","author":"Erdenebayar","year":"2019","journal-title":"J. Korean Med. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104057","DOI":"10.1016\/j.compbiomed.2020.104057","article-title":"HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks","volume":"127","author":"Mousavi","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.patrec.2020.02.005","article-title":"Validating the Robustness of an Internet of Things Based Atrial Fibrillation Detection System","volume":"133","author":"Faust","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1142\/S0219720005001004","article-title":"Minimum redundancy feature selection from microarray gene expression data","volume":"3","author":"Ding","year":"2005","journal-title":"J. Bioinform. Comput. Biol."},{"key":"ref_50","first-page":"227","article-title":"A New Method for Detecting Atrial Fibrillation Using RR Intervals","volume":"10","author":"Moody","year":"1983","journal-title":"Comput. Cardiol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1093\/europace\/eum096","article-title":"Abrupt Changes in Fibrillatory Wave Characteristics at the Termination of Paroxysmal Atrial Fibrillation in Humans","volume":"9","author":"Petrutiu","year":"2007","journal-title":"Europace"},{"key":"ref_53","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine Learning Algorithm Validation with a Limited Sample Size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1093\/bib\/bbv082","article-title":"Correct Machine Learning on Protein Sequences: A Peer-Reviewing Perspective","volume":"17","author":"Walsh","year":"2015","journal-title":"Brief. Bioinform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","article-title":"An Overview of Heart Rate Variability Metrics and Norms","volume":"5","author":"Shaffer","year":"2017","journal-title":"Front. Public Health"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2170\/physiolsci.RP005506","article-title":"Correlations between the Poincar\u00e9 plot and conventional heart rate variability parameters assessed during paced breathing","volume":"57","author":"Guzik","year":"2007","journal-title":"J. Physiol. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1111\/j.1542-474X.2005.10101.x","article-title":"Heart Rate Variability: Measurement and Clinical Utility","volume":"10","author":"Kleiger","year":"2005","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/RBME.2020.2976507","article-title":"A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning","volume":"14","author":"Rizwan","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bolon-Candedo, V., and Alonso-Betanzos, A. (2018). Recent Advances in Ensembles for Feature Selection, Springer.","DOI":"10.1007\/978-3-319-90080-3"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","article-title":"Simplifying Decision Trees","volume":"27","author":"Quinlan","year":"1987","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_65","unstructured":"Ho, T.K. (1995, January 14\u201316). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/S0895-4356(03)00177-X","article-title":"The Diagnostic Odds Ratio: A Single Indicator of Test Performance","volume":"56","author":"Glas","year":"2003","journal-title":"J. Clin. Epidemiol."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Mitrega, K., Lip, G.Y., Sredniawa, B., Sokal, A., Streb, W., Przyludzki, K., Zdrojewski, T., Wierucki, L., Rutkowski, M., and Bandosz, P. (2021). Predicting Silent Atrial Fibrillation in the Elderly: A Report from the NOMED-AF Cross-Sectional Study. J. Clin. Med., 10.","DOI":"10.3390\/jcm10112321"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Boriani, G., Palmisano, P., Malavasi, V.L., Fantecchi, E., Vitolo, M., Bonini, N., Imberti, J.F., Valenti, A.C., Schnabel, R.B., and Freedman, B. (2021). Clinical Factors Associated with Atrial Fibrillation Detection on Single-Time Point Screening Using a Hand-Held Single-Lead ECG Device. J. Clin. Med., 10.","DOI":"10.3390\/jcm10040729"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.jelectrocard.2016.07.030","article-title":"ECG by Mobile Technologies","volume":"49","author":"Guzik","year":"2016","journal-title":"J. Electrocardiol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Roten, L., Goulouti, E., Lam, A., Elchinova, E., Nozica, N., Spirito, A., Wittmer, S., Branca, M., Servatius, H., and Noti, F. (2021). Age and Sex Specific Prevalence of Clinical and Screen-Detected Atrial Fibrillation in Hospitalized Patients. J. Clin. Med., 10.","DOI":"10.3390\/jcm10214871"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1700024","DOI":"10.1002\/adhm.201700024","article-title":"Advanced Materials for Health Monitoring with Skin-Based Wearable Devices","volume":"6","author":"Jin","year":"2017","journal-title":"Adv. Healthc. Mater."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jelectrocard.2019.05.011","article-title":"Clinical Stage of Acquired Immunodeficiency Syndrome in HIV-Positive Patients Impacts the Quality of the Touch ECG Recordings","volume":"55","author":"Kaminski","year":"2019","journal-title":"J. Electrocardiol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Tsukada, S., Nakashima, H., and Torimitsu, K. (2012). Conductive Polymer Combined Silk Fiber Bundle for Bioelectrical Signal Recording. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0033689"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yapici, M.K., and Alkhidir, T.E. (2017). Intelligent Medical Garments with Graphene-Functionalized Smart-Cloth ECG Sensors. Sensors, 17.","DOI":"10.3390\/s17040875"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1097\/NCQ.0b013e31827993bc","article-title":"Daily Electrode Change and Effect on Cardiac Monitor Alarms: An Evidence-Based Practice Approach","volume":"28","author":"Cvach","year":"2013","journal-title":"J. Nurs. Care Qual."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Wang, M., Li, Y., Sun, Z., Liu, Z., He, L., and Liu, R. (2021). High-Adhesive Flexible Electrodes and Their Manufacture: A Review. Micromachines, 12.","DOI":"10.3390\/mi12121505"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An Artificial Intelligence-Enabled ECG Algorithm for the Identification of Patients with Atrial Fibrillation during Sinus Rhythm: A Retrospective Analysis of Outcome Prediction","volume":"394","author":"Attia","year":"2019","journal-title":"Lancet"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1161\/CIRCULATIONAHA.121.057480","article-title":"ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation","volume":"145","author":"Khurshid","year":"2022","journal-title":"Circulation"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"4717","DOI":"10.1093\/eurheartj\/ehab649","article-title":"Application of Artificial Intelligence to the Electrocardiogram","volume":"42","author":"Attia","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1016\/j.jacc.2008.07.041","article-title":"Heart Rate Turbulence: Standards of Measurement, Physiological Interpretation, and Clinical Use: International Society for Holter and Noninvasive Electrophysiology Consensus","volume":"52","author":"Bauer","year":"2008","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1093\/europace\/euv015","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":"Europace"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1016\/j.amjcard.2008.11.061","article-title":"Risk Stratification of Mortality in Patients with Heart Failure and Left Ventricular Ejection Fraction > 35%","volume":"103","author":"Cygankiewicz","year":"2009","journal-title":"Am. J. Cardiol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/14\/4004\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:48:00Z","timestamp":1760140080000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/14\/4004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,11]]},"references-count":82,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["jcm11144004"],"URL":"https:\/\/doi.org\/10.3390\/jcm11144004","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,11]]}}}