{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:44:25Z","timestamp":1780555465470,"version":"3.54.1"},"reference-count":151,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cardiocalm srl","award":["Cardiocalm srl"],"award-info":[{"award-number":["Cardiocalm srl"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study\u2019s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.<\/jats:p>","DOI":"10.3390\/s23115237","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T02:39:47Z","timestamp":1685587187000},"page":"5237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A Systematic Survey of Data Augmentation of ECG Signals for AI Applications"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7716-1318","authenticated-orcid":false,"given":"Md Moklesur","family":"Rahman","sequence":"first","affiliation":[{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8553-2414","authenticated-orcid":false,"given":"Massimo Walter","family":"Rivolta","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9195-4317","authenticated-orcid":false,"given":"Fabio","family":"Badilini","sequence":"additional","affiliation":[{"name":"School of Nursing, University of California, San Francisco, CA 94143, USA"},{"name":"AMPS-LLC, New York, NY 10025, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9729-2641","authenticated-orcid":false,"given":"Roberto","family":"Sassi","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/S1474-5151(11)00111-3","article-title":"The global burden of cardiovascular disease","volume":"10","author":"Deaton","year":"2011","journal-title":"Eur. J. Cardiovasc. Nurs."},{"key":"ref_2","unstructured":"Isais, R., Nguyen, K., Perez, G., Rubio, R., and Nazeran, H. (2003, January 17\u201321). A low-cost microcontroller-based wireless ECG-blood pressure telemonitor for home care. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.ccl.2006.03.005","article-title":"Status of computerized electrocardiography","volume":"24","author":"Hongo","year":"2006","journal-title":"Cardiol. Clin."},{"key":"ref_5","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1038\/s41467-020-15432-4","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101779","DOI":"10.1016\/j.artmed.2019.101779","article-title":"An enhanced deep learning approach for brain cancer MRI images classification using residual networks","volume":"102","author":"Ismael","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Habibzadeh, M., Jannesari, M., Rezaei, Z., Baharvand, H., and Totonchi, M. (2018, January 13). Automatic white blood cell classification using pre-trained deep learning models: Resnet and inception. Proceedings of the International Conference on Machine Vision, Vienna, Austria.","DOI":"10.1117\/12.2311282"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hatamian, F.N., Ravikumar, N., Vesal, S., Kemeth, F.P., Struck, M., and Maier, A. (2020, January 4\u20138). The effect of data augmentation on classification of atrial fibrillation in short single-lead ECG signals using deep neural networks. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053800"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/JBHI.2020.3040551","article-title":"Copula-Based Data Augmentation on a Deep Learning Architecture for Cardiac Sensor Fusion","volume":"25","author":"Silva","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","unstructured":"Zhu, J., Qiu, J., Yang, Z., Weber, D., Rosenberg, M.A., Liu, E., Li, B., and Zhao, D. (2022). GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105639","DOI":"10.1016\/j.cmpb.2020.105639","article-title":"Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records","volume":"196","author":"Liu","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111978","DOI":"10.1016\/j.measurement.2022.111978","article-title":"An Effective Data Enhancement Method for Classification of ECG Arrhythmia","volume":"203","author":"Ma","year":"2022","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1016\/j.jclinepi.2009.06.006","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration","volume":"62","author":"Liberati","year":"2009","journal-title":"J. Clin. Epidemiol."},{"key":"ref_17","unstructured":"Golany, T., and Radinsky, K. (February, January 27). PGANs: Generative adversarial networks for ECG synthesis to improve patient-specific deep ECG classification. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_18","unstructured":"Golany, T., Radinsky, K., and Freedman, D. (2020, January 13\u201318). SimGANs: Simulator-based generative adversarial networks for ECG synthesis to improve deep ECG classification. Proceedings of the International Conference on Machine Learning, Virtual Event."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/TBME.2022.3189617","article-title":"Fetal Electrocardiogram Extraction Using Dual-Path Source Separation of Single-Channel Non-Invasive Abdominal Recordings","volume":"70","author":"Shokouhmand","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.patrec.2018.03.028","article-title":"Deep-ECG: Convolutional Neural Networks for ECG biometric recognition","volume":"126","author":"Labati","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Barros, A., Resque, P., Almeida, J., Mota, R., Oliveira, H., Ros\u00e1rio, D., and Cerqueira, E. (2020). Data improvement model based on ECG biometric for user authentication and identification. Sensors, 20.","DOI":"10.3390\/s20102920"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhu, Y., Hong, Z., and Yang, Z. (2019, January 12\u201313). EmotionalGAN: Generating ECG to enhance emotion state classification. Proceedings of the International Conference on Artificial Intelligence and Computer Science, Wuhan, China.","DOI":"10.1145\/3349341.3349422"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Thiam, P., Kestler, H.A., and Schwenker, F. (2020, January 22\u201324). Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals. Proceedings of the ICPRAM, Valletta, Malta.","DOI":"10.5220\/0008896102890296"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.18280\/ts.390509","article-title":"Dealing with Imbalanced Sleep Apnea Data Using DCGAN","volume":"39","author":"Wicaksono","year":"2022","journal-title":"Trait. Signal"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huysmans, D., Castro, I., Borz\u00e9e, P., Patel, A., Torfs, T., Buyse, B., Testelmans, D., Van Huffel, S., and Varon, C. (2021). Capacitively-Coupled ECG and Respiration for Sleep\u2014Wake Prediction and Risk Detection in Sleep Apnea Patients. Sensors, 21.","DOI":"10.3390\/s21196409"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105335","DOI":"10.1016\/j.compbiomed.2022.105335","article-title":"Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection","volume":"143","author":"Sobahi","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shahin, I., Nassif, A.B., and Alsabek, M.B. (2021, January 7\u201310). COVID-19 Electrocardiograms Classification using CNN Models. Proceedings of the International Conference on Developments in eSystems Engineering, Sharjah, United Arab Emirates.","DOI":"10.1109\/DeSE54285.2021.9719358"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Anwar, T., and Zakir, S. (2021, January 5\u20137). Effect of image augmentation on ECG image classification using deep learning. Proceedings of the International Conference on Artificial Intelligence, Islamabad, Pakistan.","DOI":"10.1109\/ICAI52203.2021.9445258"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1007\/s00034-022-02035-1","article-title":"Automated detection of covid-19 using deep learning approaches with paper-based ecg reports","volume":"41","author":"Bassiouni","year":"2022","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106269","DOI":"10.1016\/j.cmpb.2021.106269","article-title":"ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features","volume":"208","author":"Liu","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1038\/s41598-020-79512-7","article-title":"Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks","volume":"11","author":"Alcaine","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_32","unstructured":"Moody, G., and Mark, R. (1990, January 23\u201326). The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it. Proceedings of the Computers in Cardiology, Chicago, IL, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Clifford, G.D., Liu, C., Moody, B., Li-wei, H.L., Silva, I., Li, Q., Johnson, A., 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 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.065-469"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1166\/jmihi.2018.2442","article-title":"An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection","volume":"8","author":"Liu","year":"2018","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_35","unstructured":"Bousseljot, R., Kreiseler, D., and Schnabel, A. (2023, January 11). Nutzung der EKG-Signaldatenbank CARDIODAT der PTB \u00fcber das Internet. Available online: https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bmte.1995.40.s1.317\/html."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Reyna, M.A., Sadr, N., Alday, E.A.P., Gu, A., Shah, A.J., Robichaux, C., Rad, A.B., Elola, A., Seyedi, S., and Ansari, S. (2021, January 13\u201315). Will two do? Varying dimensions in electrocardiography: The PhysioNet\/Computing in Cardiology Challenge 2021. Proceedings of the Computing in Cardiology (CinC), Brno, Czech Republic.","DOI":"10.23919\/CinC53138.2021.9662687"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nonaka, N., and Seita, J. (2021, January 8\u201311). RandECG: Data Augmentation for Deep Neural Network based ECG classification. Proceedings of the Advances in Artificial Intelligence: Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence, Virtual Event, Japan.","DOI":"10.1007\/978-3-030-96451-1_16"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hasani, H., Bitarafan, A., and Baghshah, M.S. (2020, January 13\u201316). Classification of 12-lead ECG signals with adversarial multi-source domain generalization. Proceedings of the Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.445"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nonaka, N., and Seita, J. (2020, January 13\u201316). Electrocardiogram classification by modified EfficientNet with data augmentation. Proceedings of the Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.063"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Guo, G., Gao, P., Zheng, X., and Ji, C. (2022, January 6\u20138). Multimodal Emotion Recognition Using CNN-SVM with Data Augmentation. Proceedings of the International Conference on Bioinformatics and Biomedicine. IEEE, Las Vegas, NV, USA.","DOI":"10.1109\/BIBM55620.2022.9994936"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1109\/JBHI.2022.3152538","article-title":"Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach","volume":"26","author":"Eskandari","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xu, X., Xu, H., Wang, L., Zhang, Y., and Xiao, F. (2022). Hygeia: A multilabel deep learning-based classification method for imbalanced electrocardiogram data. IEEE\/ACM Trans. Comput. Biol. Bioinform.","DOI":"10.1109\/TCBB.2022.3176905"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Loh, J., Wen, J., and Gemmeke, T. (2020, January 6\u20138). Low-Cost DNN Hardware Accelerator for Wearable, High-Quality Cardiac Arrythmia Detection. Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors, Manchester, UK.","DOI":"10.1109\/ASAP49362.2020.00042"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Y., Xie, H., Cao, Q., Yan, J., Wu, F., Zhu, H., and Pan, Y. (2021, January 3\u201315). Multi-Label Classification of Multi-lead ECG Based on Deep 1D Convolutional Neural Networks With Residual and Attention Mechanism. Proceedings of the Computing in Cardiology (CinC), Brno, Czech Republic.","DOI":"10.23919\/CinC53138.2021.9662873"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Qiu, J., Oppelt, M.P., Nissen, M., Anneken, L., Breininger, K., and Eskofier, B. (2022, January 11\u201315). Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation. Proceedings of the International Conference of the Engineering in Medicine &Biology Society, Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9871969"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cayce, G.I., Depoian, A.C., Bailey, C.P., and Guturu, P. (2022, January 3). Improved Neural Network Arrhythmia Classification Through Integrated Data Augmentation. Proceedings of the 2022 IEEE MetroCon, Hurst, TX, USA.","DOI":"10.1109\/MetroCon56047.2022.9971141"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"104194","DOI":"10.1016\/j.bspc.2022.104194","article-title":"A simple self-supervised ECG representation learning method via manipulated temporal\u2013spatial reverse detection","volume":"79","author":"Zhang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zihlmann, M., Perekrestenko, D., and Tschannen, M. (2017, January 24\u201327). Convolutional recurrent neural networks for electrocardiogram classification. Proceedings of the Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.070-060"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Duan, R., He, X., and Ouyang, Z. (2020, January 13\u201316). MADNN: A multi-scale attention deep neural network for arrhythmia classification. Proceedings of the Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.282"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105114","DOI":"10.1016\/j.compbiomed.2021.105114","article-title":"Self-supervised representation learning from 12-lead ECG data","volume":"141","author":"Mehari","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"An, J., Gregg, R.E., and Borhani, S. (2022, January 11\u201315). Effective Data Augmentation, Filters, and Automation Techniques for Automatic 12-Lead ECG Classification Using Deep Residual Neural Networks. Proceedings of the International Conference of the Engineering in Medicine &Biology Society, Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9871654"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/10.43620","article-title":"A comparison of the noise sensitivity of nine QRS detection algorithms","volume":"37","author":"Friesen","year":"1990","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s42979-021-00924-x","article-title":"Data Augmentation for 12-lead ECG Beat Classification","volume":"3","author":"Do","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104067","DOI":"10.1016\/j.bspc.2022.104067","article-title":"Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN","volume":"79","author":"Wang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sigurthorsdottir, H., Van Zaen, J., Delgado-Gonzalo, R., and Lemay, M. (2020, January 13\u201316). ECG classification with a convolutional recurrent neural network. Proceedings of the Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.198"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Oppelt, M.P., Riehl, M., Kemeth, F.P., and Steffan, J. (2020, January 13-16). Combining scatter transform and deep neural networks for multilabel electrocardiogram signal classification. Proceedings of the Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.133"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_59","first-page":"2323625","article-title":"Interpatient ECG Arrhythmia Detection by Residual Attention CNN","volume":"2022","author":"Xu","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104788","DOI":"10.1109\/ACCESS.2020.2998788","article-title":"DeepArrNet: An efficient deep CNN architecture for automatic arrhythmia detection and classification from denoised ECG beats","volume":"8","author":"Mahmud","year":"2020","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4987","DOI":"10.1109\/JBHI.2022.3191754","article-title":"DDCNN: A Deep Learning Model for AF Detection from a Single-Lead Short ECG Signal","volume":"26","author":"Yu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3408501","DOI":"10.1155\/2022\/3408501","article-title":"An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal","volume":"2022","author":"Ullah","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4293","DOI":"10.1109\/TKDE.2021.3140058","article-title":"Time series anomaly detection with adversarial reconstruction networks","volume":"35","author":"Liu","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Sangeetha, D., Selvi, S., and Ram, M.S.A. (2019, January 18\u201320). A CNN based similarity learning for cardiac arrhythmia prediction. Proceedings of the International Conference on Advanced Computing. IEEE, Chennai, India.","DOI":"10.1109\/ICoAC48765.2019.247132"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Goodfellow, S.D., Shubin, D., Greer, R.W., Nagaraj, S., McLean, C., Dixon, W., Goodwin, A.J., Assadi, A., Jegatheeswaran, A., and Laussen, P.C. (2020, January 13\u201316). Rhythm classification of 12-lead ECGs using deep neural networks and class-activation maps for improved explainability. Proceedings of the 2020 Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.353"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Weber, L., Gaiduk, M., Scherz, W.D., and Seepold, R. (2020, January 13\u201316). Cardiac abnormality detection in 12-lead ECGs with deep convolutional neural networks using data augmentation. Proceedings of the 2020 Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.229"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Natesan, P., and Gothai, E. (2020, January 11\u201313). Classification of multi-lead ECG signals to predict myocardial infarction using CNN. Proceedings of the International Conference on Computing Methodologies and Communication, Erode, India.","DOI":"10.1109\/ICCMC48092.2020.ICCMC-000192"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Almalchy, M.T., ALGayar, S.M.S., and Popescu, N. (2020, January 18\u201320). Atrial fibrillation automatic diagnosis based on ECG signal using pretrained deep convolution neural network and SVM multiclass model. Proceedings of the International Conference on Communications, Bucharest, Romania.","DOI":"10.1109\/COMM48946.2020.9141994"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhou, B., Liu, S., Hooi, B., Cheng, X., and Ye, J. (2019, January 10\u201316). BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series. Proceedings of the IJCAI, Macao, China.","DOI":"10.24963\/ijcai.2019\/616"},{"key":"ref_70","first-page":"1","article-title":"Automatic cardiac arrhythmia classification using residual network combined with long short-term memory","volume":"71","author":"Kim","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xie, H., Liu, H., Zhou, S., Gao, T., and Shu, M. (2022). A lightweight 2-D CNN model with dual attention mechanism for heartbeat classification. Appl. Intell., 1\u201316.","DOI":"10.1007\/s10489-022-04303-8"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"8571970","DOI":"10.1155\/2022\/8571970","article-title":"Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images","volume":"2022","author":"Shanmugavadivel","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101675","DOI":"10.1016\/j.bspc.2019.101675","article-title":"A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation","volume":"56","author":"Cao","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.bspc.2019.03.009","article-title":"Automatic staging model of heart failure based on deep learning","volume":"52","author":"Li","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"He, J., Rong, J., Sun, L., Wang, H., and Zhang, Y. (2020, January 11\u201314). An advanced two-step DNN-based framework for arrhythmia detection. Proceedings of the Advances in Knowledge Discovery and Data Mining: Pacific-Asia Conference, PAKDD 2020, Singapore.","DOI":"10.1007\/978-3-030-47436-2_32"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"100271","DOI":"10.1016\/j.bdr.2021.100271","article-title":"CardioNet: An efficient ECG arrhythmia classification system using transfer learning","volume":"26","author":"Pal","year":"2021","journal-title":"Big Data Res."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Nankani, D., and Dutta Baruah, R. (2019, January 17\u201320). An End-to-End framework for automatic detection of Atrial Fibrillation using Deep Residual Learning. Proceedings of the TENCON 2019\u20142019 IEEE Region 10 Conference (TENCON), Kochi, India.","DOI":"10.1109\/TENCON.2019.8929342"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Hong, S., Shang, J., Wu, M., Wang, Q., Li, H., and Xie, J. (2019). K-margin-based residual-convolution-recurrent neural network for atrial fibrillation detection. arXiv.","DOI":"10.24963\/ijcai.2019\/839"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Han, H., Park, S., Min, S., Choi, H.S., Kim, E., Kim, H., Park, S., Kim, J., Park, J., and An, J. (2021, January 13\u201315). Towards High Generalization Performance on Electrocardiogram Classification. Proceedings of the Computing in Cardiology (CinC), Brno, Czech Republic.","DOI":"10.23919\/CinC53138.2021.9662737"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"5918","DOI":"10.1109\/JBHI.2022.3207456","article-title":"Robust Arrhythmia Classification Based on QRS Detection and a Compact 1D-CNN for Wearable ECG Devices","volume":"26","author":"Sabor","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"983543","DOI":"10.3389\/fcvm.2022.983543","article-title":"Electrocardiogram classification using TSST-based spectrogram and ConViT","volume":"9","author":"Bing","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TIFS.2017.2784362","article-title":"Learning Deep Off-the-Person Heart Biometrics Representations","volume":"13","author":"Moreira","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Mura, V., Orr\u00f9, G., Casula, R., Sibiriu, A., Loi, G., Tuveri, P., Ghiani, L., and Marcialis, G.L. (2018, January 20\u201323). LivDet 2017 Fingerprint Liveness Detection Competition 2017. Proceedings of the International Conference on Biometrics, Gold Coast, QLD, Australia.","DOI":"10.1109\/ICB2018.2018.00052"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.cose.2018.11.003","article-title":"Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network","volume":"81","author":"Hammad","year":"2019","journal-title":"Comput. Secur."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"19638","DOI":"10.1038\/s41598-022-19495-9","article-title":"Robust R-peak detection in an electrocardiogram with stationary wavelet transformation and separable convolution","volume":"12","author":"Yun","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Huerta, \u00c1., Mart\u00ednez-Rodrigo, A., Rieta, J.J., and Alcaraz, R. (2021, January 13\u201315). ECG Quality Assessment via Deep Learning and Data Augmentation. Proceedings of the Computing in Cardiology (CinC), Brno, Czech Republic.","DOI":"10.23919\/CinC53138.2021.9662919"},{"key":"ref_87","unstructured":"Laguna, P., Mark, R.G., Goldberg, A., and Moody, G.B. (1997, January 7\u201310). A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Proceedings of the Computers in Cardiology, Lund, Sweden."},{"key":"ref_88","unstructured":"Yhdego, H., Kidane, N., Mckenzie, F., and Audette, M. (2020, January 18\u201321). ECG-based virtual pathology stethoscope tracking using transfer learning. Proceedings of the Spring Simulation Conference, Fairfax, VA, USA."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"106762","DOI":"10.1016\/j.dib.2021.106762","article-title":"ECG Images dataset of Cardiac and COVID-19 Patients","volume":"34","author":"Khan","year":"2021","journal-title":"Data Brief"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A multimodal database for affect recognition and implicit tagging","volume":"3","author":"Soleymani","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"103580","DOI":"10.1016\/j.bspc.2022.103580","article-title":"A new data augmentation convolutional neural network for human emotion recognition based on ECG signals","volume":"75","author":"Nita","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"105551","DOI":"10.1016\/j.compbiomed.2022.105551","article-title":"Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks","volume":"146","author":"Xiong","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhu, X., Nakamura, K., and Noro, M. (2021). Electrocardiogram Quality Assessment with a Generalized Deep Learning Model Assisted by Conditional Generative Adversarial Networks. Life, 11.","DOI":"10.3390\/life11101013"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"52089","DOI":"10.1109\/ACCESS.2021.3069827","article-title":"ProEGAN-MS: A progressive growing generative adversarial networks for electrocardiogram generation","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"103684","DOI":"10.1016\/j.bspc.2022.103684","article-title":"Multi-classification of arrhythmias using ResNet with CBAM on CWGAN-GP augmented ECG Gramian Angular Summation Field","volume":"77","author":"Ma","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Suh, J., Kim, J., Lee, E., Kim, J., Hwang, D., Park, J., Lee, J., Park, J., Moon, S.Y., and Kim, Y. (2021, January 13-15). Learning ECG representations for multi-label classification of cardiac abnormalities. Proceedings of the Computing in Cardiology (CinC), Brno, Czech Republic.","DOI":"10.23919\/CinC53138.2021.9662753"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Sarkar, P., and Etemad, A. (2021, January 2\u20139). CardioGAN: Attentive generative adversarial network with dual discriminators for synthesis of ECG from PPG. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event.","DOI":"10.1609\/aaai.v35i1.16126"},{"key":"ref_98","unstructured":"Tan, S., Androz, G., Chamseddine, A., Fecteau, P., Courville, A., Bengio, Y., and Cohen, J.P. (2019). Icentia11k: An unsupervised representation learning dataset for arrhythmia subtype discovery. arXiv."},{"key":"ref_99","unstructured":"Fonseca, K., Osorio, S., Castillo, J., and Fajardo, C. (2, January 29). Contrastive learning for atrial fibrillation detection in challenging scenarios. Proceedings of the European Signal Processing Conference, Belgrade, Serbia."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Adib, E., Afghah, F., and Prevost, J.J. (2022, January 6\u20138). Arrhythmia Classification Using CGAN-Augmented ECG Signals. Proceedings of the International Conference on Bioinformatics and Biomedicine, Las Vegas, NV, USA.","DOI":"10.1109\/BIBM55620.2022.9995088"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Al Nazi, Z., Biswas, A., Rayhan, M.A., and Abir, T.A. (2019, January 18\u201320). Classification of ECG signals by dot residual LSTM network with data augmentation for anomaly detection. Proceedings of the International Conference on Computer and Information Technology, Dhaka, Bangladesh.","DOI":"10.1109\/ICCIT48885.2019.9038287"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Li, F., Chang, H., Jiang, M., and Su, Y. (2022, January 15\u201317). A Contrastive Learning Framework for ECG Anomaly Detection. Proceedings of the International Conference on Intelligent Computing and Signal Processing, Xi\u2019an, China.","DOI":"10.1109\/ICSP54964.2022.9778634"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"100910","DOI":"10.1109\/ACCESS.2019.2930882","article-title":"ECG arrhythmias detection using auxiliary classifier generative adversarial network and residual network","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Banerjee, R., and Ghose, A. (2021, January 23\u201327). Synthesis of realistic ECG waveforms using a composite generative adversarial network for classification of atrial fibrillation. Proceedings of the European Signal Processing Conference, Dublin, Ireland.","DOI":"10.23919\/EUSIPCO54536.2021.9616079"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"104276","DOI":"10.1016\/j.bspc.2022.104276","article-title":"Generative adversarial network with transformer generator for boosting ECG classification","volume":"80","author":"Xia","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_106","unstructured":"Sun, H., Zhang, F., and Zhang, Y. An LSTM and GAN Based ECG Abnormal Signal Generator. Proceedings of the Advances in Artificial Intelligence and Applied Cognitive Computing: Proceedings from ICAI\u201920 and ACC\u201920."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"158936","DOI":"10.1109\/ACCESS.2021.3130421","article-title":"Multivariate Generative Adversarial Networks and Their Loss Functions for Synthesis of Multichannel ECGs","volume":"9","author":"Brophy","year":"2021","journal-title":"IEEE Access"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"35592","DOI":"10.1109\/ACCESS.2020.2974712","article-title":"Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks","volume":"8","author":"Shaker","year":"2020","journal-title":"IEEE Access"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Hossain, K.F., Kamran, S.A., Tavakkoli, A., Pan, L., Ma, X., Rajasegarar, S., and Karmaker, C. (2021, January 13\u201316). ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks. Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA.","DOI":"10.1109\/ICMLA52953.2021.00016"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Liu, J., Xia, X., Peng, X., Hui, J., and Han, C. (2022, January 15\u201320). Research on ECG Signal Classification Based on Data Enhancement of Generative Adversarial Network. Proceedings of the Artificial Intelligence and Security: International Conference, ICAIS 2022, Qinghai, China.","DOI":"10.1007\/978-3-031-06794-5_33"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"102820","DOI":"10.1016\/j.bspc.2021.102820","article-title":"Heart disease detection using deep learning methods from imbalanced ECG samples","volume":"68","author":"Rath","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.ins.2021.08.095","article-title":"Interactive ECG annotation: An artificial intelligence method for smart ECG manipulation","volume":"581","author":"Wang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1109\/JBHI.2022.3169325","article-title":"An ECG Signal Denoising Method Using Conditional Generative Adversarial Net","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_114","first-page":"2304","article-title":"CAB: Classifying arrhythmias based on imbalanced sensor data","volume":"15","author":"Wang","year":"2021","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"106483","DOI":"10.1016\/j.cmpb.2021.106483","article-title":"Classification of imbalanced electrocardiosignal data using convolutional neural network","volume":"214","author":"Du","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"58081","DOI":"10.1109\/ACCESS.2022.3178710","article-title":"New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia","volume":"10","author":"Islam","year":"2022","journal-title":"IEEE Access"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"He, Y., Fu, B., Yu, J., Li, R., and Jiang, R. (2020). Efficient learning of healthcare data from IoT devices by edge convolution neural networks. Appl. Sci., 10.","DOI":"10.3390\/app10248934"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Golany, T., Lavee, G., Yarden, S.T., and Radinsky, K. (2020, January 7\u201312). Improving ECG classification using generative adversarial networks. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i08.7037"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1577778","DOI":"10.1155\/2022\/1577778","article-title":"Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms","volume":"2022","author":"Ma","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Guryanova, V. (2019, January 17\u201319). Online augmentation for quality improvement of neural networks for classification of single-channel electrocardiograms. Proceedings of the Analysis of Images, Social Networks and Texts: International Conference, AIST 2019, Kazan, Russia.","DOI":"10.1007\/978-3-030-39575-9_5"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"108664","DOI":"10.1109\/ACCESS.2020.3000638","article-title":"Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN from ECG Data","volume":"8","author":"Shin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"100501","DOI":"10.1109\/ACCESS.2022.3206431","article-title":"HeartNet: Self Multihead Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-Based Arrhythmia Classification","volume":"10","author":"Rafi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Golany, T., Freedman, D., and Radinsky, K. (2021, January 2\u20139). ECG ODE-GAN: Learning ordinary differential equations of ECG dynamics via generative adversarial learning. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event.","DOI":"10.1609\/aaai.v35i1.16086"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.ins.2020.06.019","article-title":"A novel approach to create synthetic biomedical signals using BiRNN","volume":"541","author":"Fujita","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Jia, Z., Hong, F., Ping, L., Shi, Y., and Hu, J. (2021, January 5\u20139). Enabling On-Device Model Personalization for Ventricular Arrhythmias Detection by Generative Adversarial Networks. Proceedings of the ACM\/IEEE Design Automation Conference (DAC), San Francisco, CA, USA.","DOI":"10.1109\/DAC18074.2021.9586123"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"7178","DOI":"10.1109\/JIOT.2021.3108792","article-title":"BeatClass: A Sustainable ECG Classification System in IoT-Based eHealth","volume":"9","author":"Sun","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3077049","article-title":"Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks","volume":"70","author":"Maweu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"103010","DOI":"10.1016\/j.bspc.2021.103010","article-title":"Self-supervised ECG pre-training","volume":"70","author":"Liu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Dahal, K., and Ali, M.H. (2022). A Hybrid GAN-Based DL Approach for the Automatic Detection of Shockable Rhythms in AED for Solving Imbalanced Data Problems. Electronics, 12.","DOI":"10.3390\/electronics12010013"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"101997","DOI":"10.1016\/j.bspc.2020.101997","article-title":"ST-Net: Synthetic ECG tracings for diagnosing various cardiovascular diseases","volume":"61","author":"Deng","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.ins.2021.12.083","article-title":"SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis","volume":"589","author":"Li","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ins.2022.01.070","article-title":"FHRGAN: Generative adversarial networks for synthetic fetal heart rate signal generation in low-resource settings","volume":"594","author":"Zhang","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Furdui, A., Zhang, T., Worring, M., Cesar, P., and El Ali, A. (2021, January 21\u201326). AC-WGAN-GP: Augmenting ECG and GSR Signals Using Conditional Generative Models for Arousal Classification. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, Virtual Event, USA.","DOI":"10.1145\/3460418.3479301"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Garg, A., and Karimian, N. (2021, January 10\u201312). ECG Biometric Spoofing Using Adversarial Machine Learning. Proceedings of the International Conference on Consumer Electronics, Las Vegas, NV, USA.","DOI":"10.1109\/ICCE50685.2021.9427645"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Hu, J., and Li, Y. (2022, January 1\u20133). Electrocardiograph Based Emotion Recognition via WGAN-GP Data Enhancement and Improved CNN. Proceedings of the Intelligent Robotics and Applications: International Conference, ICIRA 2022, Harbin, China.","DOI":"10.1007\/978-3-031-13844-7_16"},{"key":"ref_136","unstructured":"Munia, M.S., Nourani, M., and Houari, S. (December, January 30). Biosignal oversampling using wasserstein generative adversarial network. Proceedings of the International Conference on Healthcare Informatics, Oldenburg, Germany."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"186181","DOI":"10.1109\/ACCESS.2020.3029211","article-title":"LUDB: A new open-access validation tool for electrocardiogram delineation algorithms","volume":"8","author":"Kalyakulina","year":"2020","journal-title":"IEEE Access"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"638191","DOI":"10.3389\/fgene.2021.638191","article-title":"Interpretable feature generation in ECG using a variational autoencoder","volume":"12","author":"Kuznetsov","year":"2021","journal-title":"Front. Genet."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"159369","DOI":"10.1109\/ACCESS.2019.2950383","article-title":"ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient","volume":"7","author":"Ye","year":"2019","journal-title":"IEEE Access"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"106858","DOI":"10.1016\/j.cmpb.2022.106858","article-title":"Multiple electrocardiogram generator with single-lead electrocardiogram","volume":"221","author":"Seo","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"5527904","DOI":"10.1155\/2021\/5527904","article-title":"An ECG denoising method based on the generative adversarial residual network","volume":"2021","author":"Xu","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Hazra, D., and Byun, Y.C. (2020). SynSigGAN: Generative adversarial networks for synthetic biomedical signal generation. Biology, 9.","DOI":"10.3390\/biology9120441"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Soleimani, R., and Lobaton, E. (2022). Enhancing Inference on Physiological and Kinematic Periodic Signals via Phase-Based Interpretability and Multi-Task Learning. Information, 13.","DOI":"10.3390\/info13070326"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Nankani, D., and Baruah, R.D. (2020, January 19\u201324). Investigating deep convolution conditional GANs for electrocardiogram generation. Proceedings of the International Joint Conference on Neural Networks, Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207613"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/JBHI.2019.2936583","article-title":"Synthesis of electrocardiogram V-lead signals from limb-lead measurement using R-peak aligned generative adversarial network","volume":"24","author":"Lee","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"6734","DOI":"10.1038\/s41598-019-42516-z","article-title":"Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network","volume":"9","author":"Zhu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"104444","DOI":"10.1016\/j.bspc.2022.104444","article-title":"Noise ECG generation method based on generative adversarial network","volume":"81","author":"Huang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/TCBB.2020.2976981","article-title":"A New ECG Denoising Framework Using Generative Adversarial Network","volume":"18","author":"Singh","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Chen, F., Pan, Y., Li, K., Cheng, K.T., and Huan, R. (2015, January 27\u201329). Standard 12-lead ECG synthesis using a GA optimized BP neural network. Proceedings of the International Conference on Advanced Computational Intelligence, Wuyi, China.","DOI":"10.1109\/ICACI.2015.7184716"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Abdelmadjid, M.A., and Boukadoum, M. (2022, January 19\u201322). Neural Network-Based Signal Translation with Application to the ECG. Proceedings of the IEEE Interregional NEWCAS Conference, Quebec City, QC, Canada.","DOI":"10.1109\/NEWCAS52662.2022.9842248"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:46:21Z","timestamp":1760125581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":151,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115237"],"URL":"https:\/\/doi.org\/10.3390\/s23115237","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}