{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:39:50Z","timestamp":1783791590960,"version":"3.55.0"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RAEng Research Chair","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"NIHR Research Professorship","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"NIHR Oxford Biomedical Research Centre","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"nnoHK Hong Kong Centre for Cerebro-cardiovascular Health Engineering","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"the Pandemic Sciences Institute at the University of Oxford","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging\u2014continuous wavelet transforms\u2014is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 \u00b1 0.00, precision of 0.92 \u00b1 0.02, recall of 0.85 \u00b1 0.01, specificity of 0.96 \u00b1 0.01, accuracy of 0.93 \u00b1 0.02 and AUC of 0.90 \u00b1 0.00.<\/jats:p>","DOI":"10.3390\/s23187705","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"7705","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-3783","authenticated-orcid":false,"given":"Ping","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew P.","family":"Creagh","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6140-3394","authenticated-orcid":false,"given":"Huiqi Y.","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho Bich","family":"Hai","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"VITAL Consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louise","family":"Thwaites","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Clifton","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"},{"name":"Oxford Suzhou Centre for Advanced Research, Suzhou 215123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/j.1365-3156.2006.01562.x","article-title":"Predicting the clinical outcome of tetanus: The tetanus severity score","volume":"11","author":"Thwaites","year":"2006","journal-title":"Trop. Med. Int. Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1016\/S0140-6736(18)33131-3","article-title":"Tetanus","volume":"393","author":"Yen","year":"2019","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.4269\/ajtmh.16-0470","article-title":"Tetanus in southern Vietnam: Current situation","volume":"96","author":"Thuy","year":"2017","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.mpmed.2017.09.004","article-title":"Botulism and tetanus","volume":"45","author":"Thwaites","year":"2017","journal-title":"Medicine"},{"key":"ref_5","unstructured":"(2021, March 21). Disease Factsheet about Tetanus. Available online: https:\/\/www.ecdc.europa.eu\/en\/tetanus\/facts."},{"key":"ref_6","unstructured":"(2021, October 06). The Importance of Diagnostic Tests in Fighting Infectious Diseases. Available online: https:\/\/www.lifechanginginnovation.org\/medtech-facts\/importance-diagnostic-tests-fighting-infectious-diseases.html."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1136\/bmjinnov-2021-000707","article-title":"Vital sign monitoring using wearable devices in a Vietnamese intensive care unit","volume":"7","author":"Van","year":"2021","journal-title":"BMJ Innov."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13613-017-0333-y","article-title":"Admission of tetanus patients to the ICU: A retrospective multicentre study","volume":"7","author":"Mahieu","year":"2017","journal-title":"Ann. Intensive Care"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"893200","DOI":"10.3389\/fpubh.2022.893200","article-title":"Direct Medical Costs of Tetanus, Dengue, and Sepsis Patients in an Intensive Care Unit in Vietnam","volume":"10","author":"Hung","year":"2022","journal-title":"Front. Public Health"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.pt.2018.07.007","article-title":"The estimates of the health and economic burden of dengue in Vietnam","volume":"34","author":"Hung","year":"2018","journal-title":"Trends Parasitol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1080\/17434440.2019.1563480","article-title":"Wearable sensors to improve detection of patient deterioration","volume":"16","author":"Joshi","year":"2019","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, P., Ghiasi, S., Hagenah, J., Hai, H.B., Hao, N.V., Khanh, P.N.Q., Khoa, L.D.V., VITAL Consortium, Thwaites, L., and Clifton, D.A. (2022). Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing. Sensors, 22.","DOI":"10.3390\/s22176554"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1109\/TBME.2022.3216383","article-title":"Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network","volume":"70","author":"Lu","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"403","DOI":"10.4269\/ajtmh.19-0720","article-title":"Heart rate variability as an indicator of autonomic nervous system disturbance in tetanus","volume":"102","author":"Duong","year":"2020","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/B978-0-444-53491-0.00031-6","article-title":"Heart rate variability","volume":"117","author":"Cygankiewicz","year":"2013","journal-title":"Handb. Clin. Neurol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1161\/01.CIR.93.5.1043","article-title":"Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology","volume":"93","author":"Lombardi","year":"1996","journal-title":"Circulation"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bolanos, M., Nazeran, H., and Haltiwanger, E. (September, January 30). Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA.","DOI":"10.1109\/IEMBS.2006.260607"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1109\/JBHI.2019.2959839","article-title":"Multi-modal diagnosis of infectious diseases in the developing world","volume":"24","author":"Tadesse","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3226","DOI":"10.1109\/JBHI.2020.2979608","article-title":"Plethaugment: Gan-based ppg augmentation for medical diagnosis in low-resource settings","volume":"24","author":"Kiyasseh","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ghiasi, S., Zhu, T., Lu, P., Hagenah, J., Khanh, P.N.Q., Hao, N.V., Vital Consortium, Thwaites, L., and Clifton, D.A. (2022). Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries. Sensors, 22.","DOI":"10.3390\/s22103866"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1049\/htl.2019.0030","article-title":"Severity detection tool for patients with infectious disease","volume":"7","author":"Tadesse","year":"2020","journal-title":"Healthc. Technol. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ullah, A., Anwar, S.M., Bilal, M., and Mehmood, R.M. (2020). Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sens., 12.","DOI":"10.3390\/rs12101685"},{"key":"ref_23","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 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.070-060"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Diker, A., C\u00f6mert, Z., Avc\u0131, E., To\u011fa\u00e7ar, M., and Ergen, B. (2019, January 6\u20137). A novel application based on spectrogram and convolutional neural network for ecg classification. Proceedings of the 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey.","DOI":"10.1109\/UBMYK48245.2019.8965506"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1109\/JBHI.2020.2998187","article-title":"Smartphone-and smartwatch-based remote characterisation of ambulation in multiple sclerosis during the two-minute walk test","volume":"25","author":"Creagh","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tutuko, B., Nurmaini, S., Tondas, A.E., Rachmatullah, M.N., Darmawahyuni, A., Esafri, R., Firdaus, F., and Sapitri, A.I. (2021). AFibNet: An implementation of atrial fibrillation detection with convolutional neural network. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01571-1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D convolutional neural networks and applications: A survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","unstructured":"Wu, Y., Yang, F., Liu, Y., Zha, X., and Yuan, S. (2018). A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification. arXiv."},{"key":"ref_30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_34","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 13\u201316). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning, Pasadena, CA, USA."},{"key":"ref_35","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_36","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., and Wang, Y. (2021, January 6\u201314). Transformer in transformer. Proceedings of the Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Online."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., and Xu, D. (2022, January 3\u20138). Unetr: Transformers for 3d medical image segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_38","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_39","unstructured":"Zhao, C., Droste, R., Drukker, L., Papageorghiou, A.T., and Noble, J.A. (October, January 27). Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning Using Landmark Retrieval. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106586","DOI":"10.1016\/j.cmpb.2021.106586","article-title":"A CNN-transformer hybrid approach for decoding visual neural activity into text","volume":"214","author":"Zhang","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102327","DOI":"10.1016\/j.media.2021.102327","article-title":"FAT-Net: Feature adaptive transformers for automated skin lesion segmentation","volume":"76","author":"Wu","year":"2022","journal-title":"Med Image Anal."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gong, Y., Chung, Y.A., and Glass, J. (2021). AST: Audio Spectrogram Transformer. arXiv.","DOI":"10.21437\/Interspeech.2021-698"},{"key":"ref_43","unstructured":"Park, S., Jeong, Y., and Lee, T. (2021, January 15\u201319). Many-to-Many Audio Spectrogram Transformer: Transformer for Sound Event Localization and Detection. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021, Online."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1109\/TASLP.2020.3014737","article-title":"Sound event detection of weakly labelled data with CNN-transformer and automatic threshold optimization","volume":"28","author":"Kong","year":"2020","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Byeon, Y.H., and Kwak, K.C. (2019). Pre-configured deep convolutional neural networks with various time-frequency representations for biometrics from ECG signals. Appl. Sci., 9.","DOI":"10.3390\/app9224810"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MEMB.2009.934244","article-title":"Time\u2013frequency analysis of biosignals","volume":"28","author":"Addison","year":"2009","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., and Ou, C. (2021). Automatic ECG classification using continuous wavelet transform and convolutional neural network. Entropy, 23.","DOI":"10.3390\/e23010119"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1175\/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","article-title":"A practical guide to wavelet analysis","volume":"79","author":"Torrence","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/TSP.2008.2007607","article-title":"Higher-order properties of analytic wavelets","volume":"57","author":"Lilly","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.gaitpost.2017.07.030","article-title":"Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations","volume":"59","author":"Khandelwal","year":"2018","journal-title":"Gait Posture"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/TIM.2013.2279001","article-title":"Application of cross wavelet transform for ECG pattern analysis and classification","volume":"63","author":"Banerjee","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_53","unstructured":"Abry, P. (1997). Ondelettes et turbulences: Multir\u00e9solutions, Algorithmes de D\u00e9composition, Invariance d\u2019\u00e9chelle et Signaux de Pression, Diderot \u00e9d."},{"key":"ref_54","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv."},{"key":"ref_55","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). 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_57","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., and Dong, L. (2022, January 18\u201324). Swin transformer v2: Scaling up capacity and resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1007\/s40846-018-0389-7","article-title":"Convolutional neural networks for electrocardiogram classification","volume":"38","author":"Bazi","year":"2018","journal-title":"J. Med. Biol. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7705\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:20Z","timestamp":1760129180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":60,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187705"],"URL":"https:\/\/doi.org\/10.3390\/s23187705","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]}}}