{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T02:17:07Z","timestamp":1788229027865,"version":"build-2803163510"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Paroxysmal atrial fibrillation (PAF) may be missed on intermittent electrocardiographic (ECG) recordings. We evaluated MK545Pat, a deterministic one-dimensional descriptor derived from the first-appearance order of the notes in Mozart\u2019s Piano Sonata K.545. The CPSC 2021 dataset contains 1436 records from 105 patients, from which 116,042 15 s segments were analyzed. Each segment produced 2304 histogram features from 2942 unit-stride 59-sample blocks. Segment-level 10-fold cross-validation achieved 97.49 \u00b1 0.18% accuracy (range, 97.16\u201397.71%), and a separate 90:10 segment-level holdout achieved 97.49% accuracy and 95.85% macro-F1. A separate patient-wise leave-one-subject-out (LOSO) analysis held out all records and segments from one patient at a time. The unweighted mean of the 105 held-out patient-specific segment accuracies was 71.29% (median, 78.17%; SD, 24.22%; range, 0.00\u201396.00%). Pattern controls were lower for identity, reverse, and interleaved assignments, while a within-group permutation retained 97.49%, as predicted by the invariance analysis. The results support MK545Pat as a reproducible feature-engineering approach and show the expected performance gap between segment-level and cross-patient evaluation.<\/jats:p>","DOI":"10.3390\/bioengineering13091016","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:05:26Z","timestamp":1788224726000},"page":"1016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Music-Inspired MK545Pat for AF\/PAF\/Non-AF Classification Using ECG Signals"],"prefix":"10.3390","volume":"13","author":[{"given":"Irem Ece","family":"Gulensoy","sequence":"first","affiliation":[{"name":"Department of Music, Faculty of Fine Arts, Kocaeli University, 41001 Kocaeli, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Ilknur","family":"Tuncer","sequence":"additional","affiliation":[{"name":"Software and Informatics Research Institute, Firat University, 23119 Elazig, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5257-4810","authenticated-orcid":false,"given":"Tarik","family":"Kivrak","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Firat University Hospital, Firat University, 23119 Elazig, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2021-4052","authenticated-orcid":false,"given":"Omer Faruk","family":"Goktas","sequence":"additional","affiliation":[{"name":"Department of Electronics and Automation, Technical Sciences Vocational School, Ankara Yildirim Beyazit University, 06760 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6449-8950","authenticated-orcid":false,"given":"Mehmet","family":"Baygin","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, 25050 Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5117-8333","authenticated-orcid":false,"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[{"name":"School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2217-2925","authenticated-orcid":false,"given":"Mehmet Ali","family":"Kobat","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Firat University Hospital, Firat University, 23119 Elazig, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5126-6445","authenticated-orcid":false,"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16016","DOI":"10.1038\/nrdp.2016.16","article-title":"Atrial fibrillation","volume":"2","author":"Lip","year":"2016","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1161\/CIRCEP.107.754564","article-title":"Atrial remodeling and atrial fibrillation: Mechanisms and implications","volume":"1","author":"Nattel","year":"2008","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0140-6736(09)60040-4","article-title":"Mechanisms of thrombogenesis in atrial fibrillation: Virchow\u2019s triad revisited","volume":"373","author":"Watson","year":"2009","journal-title":"Lancet"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"399","DOI":"10.2147\/VHRM.S187656","article-title":"When to withhold oral anticoagulation in atrial fibrillation: An overview of frequent clinical discussion topics","volume":"15","author":"Seelig","year":"2019","journal-title":"Vasc. Health Risk Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Stiles, M.K., and Zhao, J. (2017). Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. 2017 Computing in Cardiology (CinC), IEEE.","DOI":"10.22489\/CinC.2017.066-138"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zabihi, M., Rad, A.B., Katsaggelos, A.K., Kiranyaz, S., Narkilahti, S., and Gabbouj, M. (2017). Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier. 2017 Computing in Cardiology (CinC), IEEE.","DOI":"10.22489\/CinC.2017.069-336"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1016\/j.jacc.2014.03.022","article-title":"2014 AHA\/ACC\/HRS guideline for the management of patients with atrial fibrillation: A report of the american college of cardiology\/american heart association task force on practice guidelines and the heart rhythm society","volume":"64","author":"January","year":"2014","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1161\/01.CIR.89.1.224","article-title":"Asymptomatic arrhythmias in patients with symptomatic paroxysmal atrial fibrillation and paroxysmal supraventricular tachycardia","volume":"89","author":"Page","year":"1994","journal-title":"Circulation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/0167-5273(96)02631-9","article-title":"The incidence of asymptomatic paroxysmal atrial fibrillation in patients treated with propranolol or propafenone","volume":"54","author":"Wolk","year":"1996","journal-title":"Int. J. Cardiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1001\/jamacardio.2018.1176","article-title":"Association of burden of atrial fibrillation with risk of ischemic stroke in adults with paroxysmal atrial fibrillation: The KP-RHYTHM study","volume":"3","author":"Go","year":"2018","journal-title":"JAMA Cardiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1161\/STROKEAHA.118.021396","article-title":"Progression from paroxysmal to sustained atrial fibrillation is associated with increased adverse events","volume":"49","author":"Ogawa","year":"2018","journal-title":"Stroke"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.1161\/CIR.0000000000000568","article-title":"Atrial fibrillation burden: Moving beyond atrial fibrillation as a binary entity: A scientific statement from the American Heart Association","volume":"137","author":"Chen","year":"2018","journal-title":"Circulation"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1177\/0003319720958555","article-title":"The \u201cwhen\u201d and \u201cwhere\u201d of using anticoagulants in paroxysmal atrial fibrillation: Do all patients need long-term anticoagulation?","volume":"72","author":"Kounis","year":"2021","journal-title":"Angiology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.5603\/KP.2016.0172","article-title":"2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS","volume":"74","author":"Kirchhof","year":"2016","journal-title":"Kardiol. Pol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2746","DOI":"10.1038\/s41598-019-38928-6","article-title":"Early differentiation of long-standing persistent atrial fibrillation using the characteristics of fibrillatory waves in surface ECG multi-leads","volume":"9","author":"Park","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Faust, O., Kareem, M., Ali, A., Ciaccio, E.J., and Acharya, U.R. (2021). Automated arrhythmia detection based on RR intervals. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081446"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ins.2018.07.063","article-title":"Computer-aided diagnosis of atrial fibrillation based on ECG signals: A review","volume":"467","author":"Hagiwara","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kikillus, N., Hammer, G., Wieland, S., and Bolz, A. (2007, January 22\u201326). Algorithm for identifying patients with paroxysmal atrial fibrillation without appearance on the ECG. Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352277"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1111\/anec.12321","article-title":"Atrial fibrillation predictors: Importance of the electrocardiogram","volume":"21","author":"German","year":"2016","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cmpb.2018.07.014","article-title":"Prediction of paroxysmal atrial fibrillation: A machine learning-based approach using a combined feature vector and mixture-of-experts classification on HRV signals","volume":"165","author":"Ebrahimzadeh","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.physa.2018.06.022","article-title":"Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability","volume":"509","author":"Narin","year":"2018","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5704963","DOI":"10.1155\/2016\/5704963","article-title":"Optimal duration of monitoring for atrial fibrillation in cryptogenic stroke: A nonsystematic review","volume":"2016","author":"Hariri","year":"2016","journal-title":"BioMed Res. Int."},{"key":"ref_25","unstructured":"Wang, X., Ma, C., Zhang, X., Gao, H., Clifford, G.D., and Liu, C. (2021). Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021, PhysioNet."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Teijeiro, T., Garcia, C.A., Castro, D., and Felix, P. (2017). Arrhythmia classification from the abductive interpretation of short single-lead ECG records. 2017 Computing in Cardiology (CinC), IEEE.","DOI":"10.22489\/CinC.2017.166-054"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1109\/JBHI.2016.2631247","article-title":"Heartbeat classification using abstract features from the abductive interpretation of the ECG","volume":"22","author":"Teijeiro","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113911","DOI":"10.1016\/j.eswa.2020.113911","article-title":"DENS-ECG: A deep learning approach for ECG signal delineation","volume":"165","author":"Peimankar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sorayaie Azar, A., Mohasefi, J.B., Teichmann, D., Puthusserypady, S., Wiil, U.K., and Peimankar, A. (2025, January 14\u201317). SViT-ECG: Spectrogram vision transformer for detection of short-term atrial fibrillation from ECG signals. Proceedings of the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Copenhagen, Denmark.","DOI":"10.1109\/EMBC58623.2025.11252617"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Peimankar, A., and Puthusserypady, S. (2019, January 12\u201317). An ensemble of deep recurrent neural networks for P-wave detection in electrocardiogram. Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682307"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.knosys.2013.09.016","article-title":"Automated detection of atrial fibrillation using Bayesian paradigm","volume":"54","author":"Martis","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.eswa.2018.08.011","article-title":"A deep learning approach for real-time detection of atrial fibrillation","volume":"115","author":"Andersen","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Faust, O., Ciaccio, E.J., and Acharya, U.R. (2020). A review of atrial fibrillation detection methods as a service. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17093093"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104790","DOI":"10.1016\/j.ijmedinf.2022.104790","article-title":"Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches","volume":"163","author":"Jahan","year":"2022","journal-title":"Int. J. Med. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"75577","DOI":"10.1109\/ACCESS.2019.2918792","article-title":"A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals","volume":"7","author":"Dang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"Moody, G.B., and Mark, R.G. (2000). MIT-BIH Atrial Fibrillation Database, PhysioNet. Version 1.0.0."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/CIC.2001.977604","article-title":"Predicting the onset of paroxysmal atrial fibrillation: The Computers in Cardiology Challenge 2001","volume":"Volume 28","author":"Moody","year":"2001","journal-title":"Computers in Cardiology 2001. Vol.28 (Cat. No.01CH37287)"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Wang, L.-H., Yan, Z.-H., Yang, Y.-T., Chen, J.-Y., Yang, T., Kuo, I.-C., Abu, P.A.R., Huang, P.-C., Chen, C.-A., and Chen, S.-L. (2021). A classification and prediction hybrid model construction with the IQPSO-SVM algorithm for atrial fibrillation arrhythmia. Sensors, 21.","DOI":"10.3390\/s21155222"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liaqat, S., Dashtipour, K., Zahid, A., Assaleh, K., Arshad, K., and Ramzan, N. (2020). Detection of atrial fibrillation using a machine learning approach. Information, 11.","DOI":"10.3390\/info11120549"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yue, Y., Chen, C., Liu, P., Xing, Y., and Zhou, X. (2021). Automatic detection of short-term atrial fibrillation segments based on frequency slice wavelet transform and machine learning techniques. Sensors, 21.","DOI":"10.3390\/s21165302"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8057820","DOI":"10.1155\/2019\/8057820","article-title":"Atrial fibrillation detection by the combination of recurrence complex network and convolution neural network","volume":"2019","author":"Wei","year":"2019","journal-title":"J. Probab. Stat."},{"key":"ref_46","first-page":"513","article-title":"Neighbourhood components analysis","volume":"17","author":"Goldberger","year":"2004","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1016\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:26:43Z","timestamp":1788226003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":47,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bioengineering13091016"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13091016","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}