{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:15:49Z","timestamp":1784002549046,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council (NSTC) of Taiwan"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.<\/jats:p>","DOI":"10.3390\/s23062993","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:05:54Z","timestamp":1678413954000},"page":"2993","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction"],"prefix":"10.3390","volume":"23","author":[{"given":"Bach-Tung","family":"Pham","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phuong Thi","family":"Le","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan"},{"name":"Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzu-Chiang","family":"Tai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Providence University, Taichung City 43301, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8712-8714","authenticated-orcid":false,"given":"Yi-Chiung","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-3689","authenticated-orcid":false,"given":"Yung-Hui","family":"Li","sequence":"additional","affiliation":[{"name":"AI Research Center, Hon Hai Research Institute, New Taipei City 236, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1161\/CIR.0b013e318267e99f","article-title":"Our time: A call to save preventable death from cardiovascular disease (heart disease and stroke)","volume":"126","author":"Smith","year":"2012","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hassan, M.F.u., Lai, D., and Bu, Y. (2019, January 17\u201319). Characterization of single lead continuous ECG recording with various dry electrodes. Proceedings of the 2019 3rd International Conference on Computational Biology and Bioinformatics, Nagoya, Japan.","DOI":"10.1145\/3365966.3365974"},{"key":"ref_3","unstructured":"Scrugli, M.A., Loi, D., Raffo, L., and Meloni, P. (May, January 30). A runtime-adaptive cognitive IoT node for healthcare monitoring. Proceedings of the 16th ACM International Conference on Computing Frontiers, Alghero, Italy."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"064008","DOI":"10.1088\/1361-6579\/ac69a8","article-title":"Automatic ECG classification and label quality in training data","volume":"43","author":"Antoni","year":"2022","journal-title":"Physiol. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s10462-021-09999-7","article-title":"Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: State-of-the-art and future challenges","volume":"55","author":"Saini","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105325","DOI":"10.1016\/j.compbiomed.2022.105325","article-title":"A transformer-based deep neural network for arrhythmia detection using continuous ECG signals","volume":"144","author":"Hu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_7","unstructured":"Chen, J., Liao, K., Wei, K., Ying, H., Chen, D.Z., and Wu, J. (2022, January 18\u201321). ME-GAN: Learning panoptic electrocardio representations for multi-view ECG synthesis conditioned on heart diseases. Proceedings of the International Conference on Machine Learning, Guangzhou, China."},{"key":"ref_8","unstructured":"Sai, Y.P. (2020, January 22\u201323). A review on arrhythmia classification using ECG signals. Proceedings of the 2020 IEEE International Students\u2019 Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mhamdi, L., Dammak, O., Cottin, F., and Dhaou, I.B. (2022). Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems. Biomedicines, 10.","DOI":"10.3390\/biomedicines10082013"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43623","DOI":"10.1109\/ACCESS.2022.3169284","article-title":"AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals","volume":"10","author":"Yu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-020-01565-y","article-title":"Detection of atrial fibrillation from single lead ECG signal using multirate cosine filter bank and deep neural network","volume":"44","author":"Ghosh","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105210","DOI":"10.1016\/j.compbiomed.2022.105210","article-title":"ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration","volume":"142","author":"Attallah","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"e12806","DOI":"10.1111\/anec.12806","article-title":"Electrocardiogram analysis of patients with different types of COVID-19","volume":"25","author":"Wang","year":"2020","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"ref_15","unstructured":"Sun, W., Kalmady, S.V., Sepehrvan, N., Chu, L.M., Wang, Z., Salimi, A., Hindle, A., Greiner, R., and Kaul, P. (2022). Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zagan, I., Gaitan, V.G., Iuga, N., and Brezulianu, A. (2018, January 24\u201326). M-GreenCARDIO embedded system designed for out-of-hospital cardiac patients. Proceedings of the 2018 International Conference on Development and Application Systems (DAS), Suceava, Romania.","DOI":"10.1109\/DAAS.2018.8396063"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Navaz, A.N., Mohammed, E., Serhani, M.A., and Zaki, N. (2016, January 28\u201330). The use of data mining techniques to predict mortality and length of stay in an ICU. Proceedings of the 2016 12th International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates.","DOI":"10.1109\/INNOVATIONS.2016.7880045"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1001\/jama.2018.8102","article-title":"Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: The mSToPS randomized clinical trial","volume":"320","author":"Steinhubl","year":"2018","journal-title":"JAMA"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Benhamida, A., Zouaoui, A., Sz\u00f3cska, G., Kar\u00f3czkai, K., Slimani, G., and Kozlovszky, M. (2019, January 24\u201326). Problems in archiving long-term continuous ECG data\u2014A review. Proceedings of the 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Herlany, Slovakia.","DOI":"10.1109\/SAMI.2019.8782737"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5843504","DOI":"10.1155\/2017\/5843504","article-title":"A remote health monitoring system for the elderly based on smart home gateway","volume":"2017","author":"Guan","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rakovi\u0107, P., and Lutovac, B. (2015, January 14\u201318). A cloud computing architecture with wireless body area network for professional athletes health monitoring in sports organizations\u2014Case study of Montenegro. Proceedings of the 2015 4th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro.","DOI":"10.1109\/MECO.2015.7181950"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Octaviani, V., Kurniawan, A., Suprapto, Y.K., and Zaini, A. (2017, January 19\u201321). Alerting system for sport activity based on ECG signals using proportional integral derivative. Proceedings of the 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, Indonesia.","DOI":"10.1109\/EECSI.2017.8239104"},{"key":"ref_23","first-page":"317","article-title":"Nutzung der EKG-Signaldatenbank CARDIODAT der PTB \u00fcber das Internet","volume":"40","author":"Bousseljot","year":"1995","journal-title":"Biomed. Tech."},{"key":"ref_24","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_25","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 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.065-469"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s11760-021-02009-x","article-title":"Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree","volume":"16","author":"Kumari","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100615","DOI":"10.1109\/ACCESS.2021.3097614","article-title":"ECG heartbeat classification using multimodal fusion","volume":"9","author":"Ahmad","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1250027","DOI":"10.1142\/S012906571250027X","article-title":"Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals","volume":"22","author":"Martis","year":"2012","journal-title":"Int. J. Neural Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1109\/TBME.2012.2213253","article-title":"Heartbeat classification using morphological and dynamic features of ECG signals","volume":"59","author":"Ye","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pathoumvanh, S., Hamamoto, K., and Indahak, P. (2014, January 5\u20138). Arrhythmias detection and classification base on single beat ECG analysis. Proceedings of the 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), Chiang Rai, Thailand.","DOI":"10.1109\/JICTEE.2014.6804097"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","article-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals","volume":"100","author":"Acharya","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5366","DOI":"10.1007\/s10489-021-02696-6","article-title":"Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data","volume":"52","author":"Rai","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.1038\/s41467-020-17804-2","article-title":"Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram","volume":"11","author":"Besomi","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compbiomed.2018.03.016","article-title":"A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification","volume":"96","author":"Yildirim","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/JBHI.2019.2911367","article-title":"LSTM-based ECG classification for continuous monitoring on personal wearable devices","volume":"24","author":"Saadatnejad","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yeh, L.R., Chen, W.C., Chan, H.Y., Lu, N.H., Wang, C.Y., Twan, W.H., Du, W.C., Huang, Y.H., Hsu, S.Y., and Chen, T.B. (2021). Integrating ECG monitoring and classification via IoT and deep neural networks. Biosensors, 11.","DOI":"10.3390\/bios11060188"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100479","DOI":"10.1016\/j.imu.2020.100479","article-title":"A two-stage Deep CNN Architecture for the Classification of Low-risk and High-risk Hypertension Classes using Multi-lead ECG Signals","volume":"21","author":"Jain","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_40","unstructured":"Jun, T.J., Nguyen, H.M., Kang, D., Kim, D., Kim, D., and Kim, Y.H. (2018). ECG arrhythmia classification using a 2-D convolutional neural network. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1380348","DOI":"10.1155\/2018\/1380348","article-title":"Arrhythmia classification of ECG signals using hybrid features","volume":"2018","author":"Anwar","year":"2018","journal-title":"Comput. Math. Methods Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103726","DOI":"10.1016\/j.compbiomed.2020.103726","article-title":"Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review","volume":"120","author":"Murat","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ullah, H., Heyat, M.B.B., Akhtar, F., Muaad, A.Y., Ukwuoma, C.C., Bilal, M., Miraz, M.H., Bhuiyan, M.A.S., Wu, K., and Dama\u0161evi\u010dius, R. (2022). An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010087"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e386","DOI":"10.7717\/peerj-cs.386","article-title":"From ECG signals to images: A transformation based approach for deep learning","volume":"7","author":"Naz","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"094006","DOI":"10.1088\/1361-6579\/aad9ed","article-title":"ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network","volume":"39","author":"Xiong","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2020.3006756","article-title":"Automated detection and classification of arrhythmia from ECG signals using feature-induced long short-term memory network","volume":"4","author":"Ganguly","year":"2020","journal-title":"IEEE Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TIM.2019.2910342","article-title":"LSTM-based auto-encoder model for ECG arrhythmias classification","volume":"69","author":"Hou","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_49","unstructured":"(2005, February 24). MIT-BIH Arrhythmia Database. Available online: https:\/\/www.physionet.org\/content\/mitdb\/1.0.0\/."},{"key":"ref_50","unstructured":"(2004, September 25). PTB Diagnostic ECG Database. Available online: https:\/\/www.physionet.org\/content\/ptbdb\/1.0.0\/."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kachuee, M., Fazeli, S., and Sarrafzadeh, M. (2018, January 4\u20137). Ecg heartbeat classification: A deep transferable representation. Proceedings of the 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, USA.","DOI":"10.1109\/ICHI.2018.00092"},{"key":"ref_52","unstructured":"Oppenheim, A.V., Willsky, A.S., Nawab, S.H., and Ding, J.J. (1997). Signals and Systems, Prentice Hall."},{"key":"ref_53","first-page":"13539","article-title":"Evolving normalization-activation layers","volume":"33","author":"Liu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_55","unstructured":"Zhang, J., He, T., Sra, S., and Jadbabaie, A. (2019). Why gradient clipping accelerates training: A theoretical justification for adaptivity. arXiv."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"1350014","DOI":"10.1142\/S0129065713500147","article-title":"Application of higher order cumulant features for cardiac health diagnosis using ECG signals","volume":"23","author":"Martis","year":"2013","journal-title":"Int. J. Neural Syst."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, T., and Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests. Entropy, 18.","DOI":"10.3390\/e18080285"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ins.2017.06.027","article-title":"Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals","volume":"415","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_60","first-page":"48523","article-title":"A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal","volume":"2014","author":"Safdarian","year":"2014","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"51","DOI":"10.5530\/jcdr.2015.2.2","article-title":"Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram","volume":"6","author":"Kojuri","year":"2015","journal-title":"J. Cardiovasc. Dis. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1109\/TBME.2012.2213597","article-title":"ECG analysis using multiple instance learning for myocardial infarction detection","volume":"59","author":"Sun","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.compbiomed.2014.08.010","article-title":"A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection","volume":"61","author":"Liu","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1109\/TBME.2015.2405134","article-title":"Multiscale energy and eigenspace approach to detection and localization of myocardial infarction","volume":"62","author":"Sharma","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hong, S., Xiao, C., Ma, T., Li, H., and Sun, J. (2019). MINA: Multilevel knowledge-guided attention for modeling electrocardiography signals. arXiv.","DOI":"10.24963\/ijcai.2019\/816"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2993\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:32Z","timestamp":1760122292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":65,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23062993"],"URL":"https:\/\/doi.org\/10.3390\/s23062993","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]}}}