{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T17:34:42Z","timestamp":1762018482843,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T00:00:00Z","timestamp":1678060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"],"award-info":[{"award-number":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"]}]},{"DOI":"10.13039\/501100006111","name":"FCT\/Minist\u00e9rio da Ci\u00eancia, Tecnologia e Ensino Superior (MCTES)","doi-asserted-by":"publisher","award":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"],"award-info":[{"award-number":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"]}],"id":[{"id":"10.13039\/501100006111","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT)","award":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"],"award-info":[{"award-number":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"]}]},{"DOI":"10.13039\/501100001871","name":"National Funds (OE)","doi-asserted-by":"publisher","award":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"],"award-info":[{"award-number":["PCIF\/S-SO\/0163\/2019","UIDB\/50008\/2020","LISBOA-01-0247-FEDER-069918"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device.<\/jats:p>","DOI":"10.3390\/s23052854","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T06:29:06Z","timestamp":1678084146000},"page":"2854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6254-7843","authenticated-orcid":false,"given":"Rafael","family":"Silva","sequence":"first","affiliation":[{"name":"Department of Bioengineering (DBE), Instituto Superior T\u00e9cnico (IST), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais 1, Torre Norte\u2014Piso 10, 1049-001 Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1320-5024","authenticated-orcid":false,"given":"Ana","family":"Fred","sequence":"additional","affiliation":[{"name":"Department of Bioengineering (DBE), Instituto Superior T\u00e9cnico (IST), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais 1, Torre Norte\u2014Piso 10, 1049-001 Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6764-8432","authenticated-orcid":false,"given":"Hugo","family":"Pl\u00e1cido da Silva","sequence":"additional","affiliation":[{"name":"Department of Bioengineering (DBE), Instituto Superior T\u00e9cnico (IST), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), Av. Rovisco Pais 1, Torre Norte\u2014Piso 10, 1049-001 Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","article-title":"Heart Disease and Stroke Statistics\u20142019 Update: A Report From the American Heart Association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1161\/CIRCRESAHA.120.316340","article-title":"Epidemiology of Atrial Fibrillation in the 21st Century","volume":"127","author":"Kornej","year":"2020","journal-title":"Circ. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1056\/NEJMoa1105575","article-title":"Subclinical Atrial Fibrillation and the Risk of Stroke","volume":"366","author":"Healey","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1177\/1747493019897870","article-title":"Global Epidemiology of Atrial Fibrillation: An Increasing Epidemic and Public Health Challenge","volume":"16","author":"Lippi","year":"2021","journal-title":"Int. J. Stroke"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ccep.2020.10.010","article-title":"Epidemiology of Atrial Fibrillation: Geographic and Ecological Risk Factors, Age, Sex, Genetics","volume":"13","author":"Zhang","year":"2021","journal-title":"Card. Electrophysiol. Clin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1161\/CIRCEP.107.754564","article-title":"Atrial Remodeling and Atrial Fibrillation","volume":"1","author":"Nattel","year":"2008","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1016\/j.jacc.2014.02.555","article-title":"Atrial Remodeling and Atrial Fibrillation: Recent Advances and Translational Perspectives","volume":"63","author":"Nattel","year":"2014","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1253\/circj.68.568","article-title":"Progressive Nature of Paroxysmal Atrial Fibrillation","volume":"68","author":"Kato","year":"2004","journal-title":"Circ. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12959-021-00276-9","article-title":"Persistent or Permanent Atrial Fibrillation is Associated with Severe Cardioembolic Stroke in Patients with Non-valvular Atrial Fibrillation","volume":"19","author":"Hagii","year":"2021","journal-title":"Thromb. J."},{"key":"ref_10","unstructured":"Podrid, P., Malhotra, R., Kakkar, R., and Noseworthy, P. (2015). Podrid\u2019s Real-World ECGs: Volume 4, Arrhythmias: A Master\u2019s Approach to the Art and Practice of Clinical ECG Interpretation, Cardiotext Publishing."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1097\/01.NPR.0000530214.17031.45","article-title":"Detection and Management of Atrial Fibrillation Using Remote Monitoring","volume":"43","author":"Hickey","year":"2018","journal-title":"Nurse Pract."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation","volume":"381","author":"Perez","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e010063","DOI":"10.1161\/CIRCEP.121.010063","article-title":"Arrhythmias Other Than Atrial Fibrillation in Those With an Irregular Pulse Detected With a Smartwatch: Findings From the Apple Heart Study","volume":"14","author":"Perino","year":"2021","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Serhani, M.A., El Kassabi, H., Ismail, H., and Nujum Navaz, A. (2020). ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges. Sensors, 20.","DOI":"10.3390\/s20061796"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ib\u00e1\u00f1ez, J., Gonz\u00e1lez-Vargas, J., Azor\u00edn, J.M., Akay, M., and Pons, J.L. (2017). Proceedings of the Converging Clinical and Engineering Research on Neurorehabilitation II, Springer. Biosystems & Biorobotics.","DOI":"10.1007\/978-3-319-46669-9"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s12553-015-0098-y","article-title":"Off-the-person Electrocardiography: Performance Assessment and Clinical Correlation","volume":"4","author":"Carreiras","year":"2015","journal-title":"Health Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Silva, A.S., Correia, M.V., and Silva, H.P. (2021). Invisible ECG for High Throughput Screening in eSports. Sensors, 21.","DOI":"10.3390\/s21227601"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6222","DOI":"10.1038\/s41598-021-85697-2","article-title":"Design and Evaluation of a Novel Approach to Invisible Electrocardiography (ECG) in Sanitary Facilities Using Polymeric Electrodes","volume":"11","author":"Almeida","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104404","DOI":"10.1016\/j.compbiomed.2021.104404","article-title":"Digital Biomarkers and Algorithms for Detection of Atrial Fibrillation Using Surface Electrocardiograms: A Systematic Review","volume":"133","author":"Wesselius","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"545","DOI":"10.3389\/fneur.2019.00545","article-title":"Spectral Analysis of Heart Rate Variability: Time Window Matters","volume":"10","author":"Li","year":"2019","journal-title":"Front. Neurol."},{"key":"ref_21","first-page":"E215","article-title":"Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"PhysioNet"},{"key":"ref_22","first-page":"227","article-title":"A New Method for Detecting Atrial Fibrillation using R-R Intervals","volume":"10","author":"Moody","year":"1983","journal-title":"Comput. Cardiol."},{"key":"ref_23","first-page":"1","article-title":"AF Classification from a Short Single Lead ECG Recording: The PhysioNet\/Computing in Cardiology Challenge 2017","volume":"44","author":"Clifford","year":"2017","journal-title":"Comput. Cardiol."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ijcard.2020.04.046","article-title":"A Morphology Based Deep Learning Model for Atrial Fibrillation Detection Using Single Cycle Electrocardiographic Samples","volume":"316","author":"Baalman","year":"2020","journal-title":"Int. J. Cardiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/JBHI.2018.2858789","article-title":"Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation from Single Lead Short ECG Recordings","volume":"22","author":"Fan","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/JBHI.2022.3221464","article-title":"Detection of Atrial Fibrillation from Variable-Duration ECG Signal Based on Time-Adaptive Densely Network and Feature Enhancement Strategy","volume":"27","author":"Zhang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yuan, C., Yan, Y., Zhou, L., Bai, J., and Wang, L. (2016, January 1\u20133). Automated Atrial Fibrillation Detection Based on Deep Learning Network. Proceedings of the 2016 IEEE International Conference on Information and Automation (ICIA), Ningbo, China.","DOI":"10.1109\/ICInfA.2016.7831994"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1166\/jmihi.2019.2626","article-title":"Identification of Atrial Fibrillation from Electrocardiogram Signals Based on Deep Neural Network","volume":"9","author":"Chen","year":"2019","journal-title":"J. Med Imaging Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5772","DOI":"10.1109\/JBHI.2022.3171918","article-title":"Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection","volume":"26","author":"Chen","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","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_32","unstructured":"Saleh, H. (2018). Machine Learning Fundamentals, Packt Publishing."},{"key":"ref_33","unstructured":"Le, L., Patterson, A., and White, M. (2018, January 3\u20138). Supervised Autoencoders: Improving Generalization Performance with Unsupervised Regularizers. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, Canada."},{"key":"ref_34","unstructured":"Silva, R. (2021). An Artificial Intelligence Approach to Atrial Fibrillation Detection in Single-lead Invisible ECG. [Master\u2019s Thesis, Instituto Superior T\u00e9cnico]."},{"key":"ref_35","unstructured":"Carreiras, C., Alves, A.P., Louren\u00e7o, A., Canento, F., Silva, H., and Fred, A. (2015). BioSPPy: Biosignal Processing in Python."},{"key":"ref_36","unstructured":"Hamilton, P. (1993, January 5\u20138). Open source ECG analysis. Proceedings of the Computers in Cardiology, London, UK."},{"key":"ref_37","unstructured":"Lourenco, A., Pl\u00e1cido da Silva, H., Leite, P., Louren\u00e7o, R., and Fred, A. (2012, January 1\u201314). Real Time Electrocardiogram Segmentation for Finger Based ECG Biometrics. Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing (BIOSTEC), Vilamoura, Portugal."},{"key":"ref_38","unstructured":"Kamel, M., and Campilho, A. Outlier Detection in Non-Intrusive ECG Biometric System. Proceedings of the Image Analysis and Recognition, Lecture Notes in Computer Science."},{"key":"ref_39","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:48:57Z","timestamp":1760122137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,6]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052854"],"URL":"https:\/\/doi.org\/10.3390\/s23052854","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,3,6]]}}}