{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:20:06Z","timestamp":1777555206487,"version":"3.51.4"},"reference-count":63,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T00:00:00Z","timestamp":1574294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.<\/jats:p>","DOI":"10.3390\/s19235079","type":"journal-article","created":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T02:49:27Z","timestamp":1574390967000},"page":"5079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3751-0453","authenticated-orcid":false,"given":"Rajesh N V P S","family":"Kandala","sequence":"first","affiliation":[{"name":"Department of ECE, GVPCE (A), Visakhapatnam 530048, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravindra","family":"Dhuli","sequence":"additional","affiliation":[{"name":"Department of ECE, VIT University, Andhra Pradesh 522237, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-2801","authenticated-orcid":false,"given":"Pawe\u0142","family":"P\u0142awiak","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warsaw 24 st., F-3, 31-155 Krakow, Poland"},{"name":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba\u0142tycka 5, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-9838","authenticated-orcid":false,"given":"Ganesh R.","family":"Naik","sequence":"additional","affiliation":[{"name":"The MARCS Institute, Western Sydney University, Milperra, NSW 2214, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2926-8014","authenticated-orcid":false,"given":"Hossein","family":"Moeinzadeh","sequence":"additional","affiliation":[{"name":"The MARCS Institute, Western Sydney University, Milperra, NSW 2214, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2616-2804","authenticated-orcid":false,"given":"Gaetano D.","family":"Gargiulo","sequence":"additional","affiliation":[{"name":"The MARCS Institute, Western Sydney University, Milperra, NSW 2214, Australia"},{"name":"Department of Electrical Engineering and Information Technology (DIETI), \u201cFederico II\u201d The University of Naples, 80100 Naples, Italy"},{"name":"School of Engineering at Western Sydney University, Penrith, NSW 2747, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suryanarayana","family":"Gunnam","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,21]]},"reference":[{"key":"ref_1","unstructured":"Alwan, A. (2011). Global Status Report on Noncommunicable Diseases 2010, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1088\/0967-3334\/27\/7\/004","article-title":"Assessment of electrocardiogram visual interpretation strategy based on scanpath analysis","volume":"27","author":"Augustyniak","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH arrhythmia database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.eswa.2017.09.022","article-title":"Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system","volume":"92","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1007\/s13042-017-0677-5","article-title":"A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression","volume":"9","author":"Yang","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tuncer, T., Dogan, S., P\u0142awiak, P., and Acharya, U.R. (2019). Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl. Based Syst., 104923.","DOI":"10.1016\/j.knosys.2019.104923"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.compbiomed.2017.06.006","article-title":"Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine","volume":"87","author":"Rajesh","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.swevo.2017.10.002","article-title":"Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals","volume":"39","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","article-title":"Arrhythmia detection using deep convolutional neural network with long duration ECG signals","volume":"102","author":"Tan","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"P\u0142awiak, P., and Acharya, U.R. (2019). Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput. Appl., 1\u201325.","DOI":"10.1007\/s00521-018-03980-2"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"P\u0142awiak, P., and Abdar, M. (2020). Novel Methodology for Cardiac Arrhythmias Classification Based on Long-Duration ECG Signal Fragments Analysis. Biomedical Signal Processing, Springer.","DOI":"10.1007\/978-981-13-9097-5_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1016\/j.eswa.2015.06.046","article-title":"A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines","volume":"42","author":"Khalaf","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1088\/0967-3334\/37\/4\/530","article-title":"ECG feature extraction based on the bandwidth properties of variational mode decomposition","volume":"37","author":"Mert","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s00034-015-0108-3","article-title":"Novel ECG signal classification based on KICA nonlinear feature extraction","volume":"35","author":"Li","year":"2016","journal-title":"Circ. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s10916-016-0467-8","article-title":"Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier","volume":"40","author":"Alickovic","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1350014","DOI":"10.1142\/S0129065713500147","article-title":"Application of higher order cumulant features for cardiac health diagnosis using ECG signals","volume":"23","author":"Martis","year":"2013","journal-title":"Int. J. Neural Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1049\/iet-spr.2015.0274","article-title":"Efficient methodology for electrocardiogram beat classification","volume":"10","author":"Sharma","year":"2016","journal-title":"IET Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.bspc.2010.01.002","article-title":"Local fractal dimension based ECG arrhythmia classification","volume":"5","author":"Mishra","year":"2010","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TBME.2004.824138","article-title":"Support vector machine-based expert system for reliable heartbeat recognition","volume":"51","author":"Osowski","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1109\/TBME.2012.2213253","article-title":"Heartbeat classification using morphological and dynamic features of ECG signals","volume":"59","author":"Ye","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s11517-006-0027-3","article-title":"Classification enhancible grey relational analysis for cardiac arrhythmias discrimination","volume":"44","author":"Lin","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s11517-006-0118-1","article-title":"Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching","volume":"45","author":"Biagetti","year":"2007","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1515\/bams-2015-0021","article-title":"Neural networks as a tool for modeling of biological systems","volume":"11","author":"Tadeusiewicz","year":"2015","journal-title":"Bio-Algorithms Med.-Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.compbiomed.2010.11.003","article-title":"A multi-stage automatic arrhythmia recognition and classification system","volume":"41","author":"Kutlu","year":"2011","journal-title":"Comput. Biol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","article-title":"ECG beat classification using PCA, LDA, ICA and discrete wavelet transform","volume":"8","author":"Martis","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.knosys.2013.02.007","article-title":"Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework","volume":"45","author":"Martis","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.cmpb.2015.12.024","article-title":"Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals","volume":"127","author":"Elhaj","year":"2016","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/TBME.2016.2539421","article-title":"High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal","volume":"64","author":"Li","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Desai, U., Martis, R.J., Nayak, C.G., Sarika, K., and Seshikala, G. (2015, January 17\u201320). Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. Proceedings of the India Conference (INDICON), New Delhi, India.","DOI":"10.1109\/INDICON.2015.7443220"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1640012","DOI":"10.1142\/S0219519416400121","article-title":"Decision support system for arrhythmia beats using ECG signals with DCT, DWT and EMD methods: A comparative study","volume":"16","author":"Desai","year":"2016","journal-title":"J. Mech. Med. Biol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","article-title":"Automatic classification of heartbeats using ECG morphology and heartbeat interval features","volume":"51","author":"Reilly","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.jesit.2015.07.001","article-title":"FPGA-based electrocardiography (ECG) signal analysis system using least-square linear phase finite impulse response (FIR) filter","volume":"3","author":"Egila","year":"2016","journal-title":"J. Elec. Syst. Inf. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s13534-015-0196-9","article-title":"A knowledge-based real time embedded platform for arrhythmia beat classification","volume":"5","author":"Raj","year":"2015","journal-title":"Biomed. Eng. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zairi, H., Talha, M.K., Meddah, K., and Slimane, S.O. (2019). FPGA-based system for artificial neural network arrhythmia classification. Neural Comput. Appl., 1\u201316.","DOI":"10.1007\/s00521-019-04081-4"},{"key":"ref_35","unstructured":"Jewajinda, Y., and Chongstitvatana, P. (2010, January 19\u201321). FPGA-based online-learning using parallel genetic algorithm and neural network for ECG signal classification. Proceedings of the ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Chiang Mai, Thailand."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.bspc.2017.12.004","article-title":"Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier","volume":"41","author":"Rajesh","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/1475-925X-4-60","article-title":"Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators","volume":"4","author":"Amann","year":"2005","journal-title":"Biomed. Eng. Online"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. A Math. Phys. Eng. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the Acoustics, speech and signal processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.bspc.2014.06.009","article-title":"Improved complete ensemble EMD: A suitable tool for biomedical signal processing","volume":"14","author":"Colominas","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, T., and Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests. Entropy, 18.","DOI":"10.3390\/e18080285"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0165-0270(00)00356-3","article-title":"Wavelet entropy: A new tool for analysis of short duration brain electrical signals","volume":"105","author":"Rosso","year":"2001","journal-title":"J. Neurosci. Method."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/j.1538-7305.1948.tb00917.x","article-title":"A mathematical theory of communication, Part I, Part II","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/18.119732","article-title":"Entropy-based algorithms for best basis selection","volume":"38","author":"Coifman","year":"1992","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Martis, R.J., Acharya, U.R., Ray, A.K., and Chakraborty, C. (September, January 30). Application of higher order cumulants to ECG signals for the cardiac health diagnosis. Proceedings of the 2011 Annual International Conference on Engineering in Medicine and Biology Society (EMBC), Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6090487"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/79.221324","article-title":"Signal processing with higher-order spectra","volume":"10","author":"Nikias","year":"1993","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_48","unstructured":"Swami, A., Mendel, J.M., and Nikias, C.L.M. (1984). Higher-order spectral analysis toolbox. Tech. Support Product Enhanc. Suggest."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evolut. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/34.667881","article-title":"On combining classifiers","volume":"20","author":"Kittler","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","unstructured":"John, G.H., and Langley, P. (1995, January 18\u201320). Estimating continuous distributions in Bayesian classifiers. Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence, Montr\u00e9al, QC, Canada."},{"key":"ref_52","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2012). Pattern Classification, John Wiley & Sons."},{"key":"ref_53","unstructured":"Quinlan, J.R. (2014). C4. 5: Programs for Machine Learning, Elsevier."},{"key":"ref_54","unstructured":"Arbelaiz Gallego, O., Gurrutxaga, I., Lozano, F., Muguerza, J., and P\u00e9rez, J.M. (2019, November 17). J48Consolidated: An Implementation of CTC Algorithm for WEKA. Available online: https:\/\/addi.ehu.es\/handle\/10810\/17314."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10994-008-5083-5","article-title":"Discretization for naive-Bayes learning: managing discretization bias and variance","volume":"74","author":"Yang","year":"2009","journal-title":"Mach. Learn."},{"key":"ref_56","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_57","unstructured":"Powers, D.M. (2019, November 19). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Available online: https:\/\/bioinfopublication.org\/files\/articles\/2_1_1_JMLT.pdf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3561","DOI":"10.1016\/j.eswa.2012.12.063","article-title":"ECG arrhythmia classification based on optimum-path forest","volume":"40","author":"Luz","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Queiroz, V., Luz, E., Moreira, G., Guarda, \u00c1., and Menotti, D. (2015, January 25\u201329). Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks. Proceedings of the 2015 37th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), Ilan, Italy.","DOI":"10.1109\/EMBC.2015.7319564"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1504\/IJBRA.2017.083148","article-title":"Evaluating a hierarchical approach for heartbeat classification from ECG","volume":"13","author":"Luz","year":"2017","journal-title":"Int. J. Bioinf. Res. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Garcia, G., Moreira, G., Luz, E., and Menotti, D. (2016, January 24\u201329). Improving automatic cardiac arrhythmia classification: Joining temporal-VCG, complex networks and SVM classifier. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727704"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.bspc.2016.07.010","article-title":"Heartbeat classification using projected and dynamic features of ECG signal","volume":"31","author":"Chen","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"10543","DOI":"10.1038\/s41598-017-09837-3","article-title":"Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO","volume":"7","author":"Garcia","year":"2017","journal-title":"Sci. Rep."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:36:14Z","timestamp":1760189774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,21]]},"references-count":63,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235079"],"URL":"https:\/\/doi.org\/10.3390\/s19235079","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,21]]}}}