{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:42:23Z","timestamp":1765546943201,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wellcome Trust","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"InnoHK Project on Project 1.1\u2014Wearable Intelligent Sensing Engineering (WISE) at Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE)","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"Pandemic Sciences Institute, University of Oxford, Oxford, UK","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"NHS, the NIHR, the Department of Health, the University of Oxford, or InnoHK\u2014ITC","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>Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible. Machine learning analysis of ECG has been shown of extra value in predicting tetanus severity, however any additional ECG signal analysis places a high demand on time-limited hospital staff and requires specialist equipment. Therefore, we present a novel approach to tetanus monitoring from low-cost wearable sensors combined with a deep-learning-based automatic severity detection. This approach can automatically triage tetanus patients and reduce the burden on hospital staff. In this study, we propose a two-dimensional (2D) convolutional neural network with a channel-wise attention mechanism for the binary classification of ECG signals. According to the Ablett classification of tetanus severity, we define grades 1 and 2 as mild tetanus and grades 3 and 4 as severe tetanus. The one-dimensional ECG time series signals are transformed into 2D spectrograms. The 2D attention-based network is designed to extract the features from the input spectrograms. Experiments demonstrate a promising performance for the proposed method in tetanus classification with an F1 score of 0.79 \u00b1 0.03, precision of 0.78 \u00b1 0.08, recall of 0.82 \u00b1 0.05, specificity of 0.85 \u00b1 0.08, accuracy of 0.84 \u00b1 0.04 and AUC of 0.84 \u00b1 0.03.<\/jats:p>","DOI":"10.3390\/s22176554","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:13:56Z","timestamp":1661904836000},"page":"6554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing"],"prefix":"10.3390","volume":"22","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3674-9489","authenticated-orcid":false,"given":"Shadi","family":"Ghiasi","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"}]},{"given":"Jannis","family":"Hagenah","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"}]},{"given":"Ho Bich","family":"Hai","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Nguyen Van","family":"Hao","sequence":"additional","affiliation":[{"name":"Hospital of Tropical Diseases, Ho Chi Minh City 700000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7455-8862","authenticated-orcid":false,"given":"Phan Nguyen Quoc","family":"Khanh","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Le Dinh Van","family":"Khoa","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam"}]},{"name":"VITAL Consortium","sequence":"additional","affiliation":[]},{"given":"Louise","family":"Thwaites","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam"}]},{"given":"David A.","family":"Clifton","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"},{"name":"Hthe Oxford Suzhou Centre for Advanced Research, University of Oxford, Suzhou Dushu Lake Science and Education Innovation District, Suzhou 215123, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1552-5630","authenticated-orcid":false,"given":"Tingting","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.12688\/wellcomeopenres.16731.1","article-title":"The management of tetanus in adults in an intensive care unit in Southern Vietnam","volume":"6","author":"Yen","year":"2021","journal-title":"Wellcome Open Res."},{"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":"(2022, August 21). What are the Symptoms of Tetanus?. Available online: https:\/\/healthclinics.superdrug.com\/tetanus-symptoms\/."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"329","DOI":"10.7326\/0003-4819-154-5-201103010-00007","article-title":"Narrative review: Tetanus\u2014A health threat after natural disasters in developing countries","volume":"154","author":"Afshar","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref_8","unstructured":"(2022, August 21). 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_9","doi-asserted-by":"crossref","unstructured":"Trieu, H.T., Anh, N.T.K., Vuong, H.N.T., Dao, T., Hoa, N.T.X., Tuong, V.N.C., Dinh, P.T., Wills, B., Qui, P.T., and Van Tan, L. (2017). Long-term outcome in survivors of neonatal tetanus following specialist intensive care in Vietnam. BMC Infect. Dis., 17.","DOI":"10.1186\/s12879-017-2748-3"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ijid.2014.12.011","article-title":"Prognosis of neonatal tetanus in the modern management era: An observational study in 107 Vietnamese infants","volume":"33","author":"Lam","year":"2015","journal-title":"Int. J. Infect. Dis."},{"key":"ref_11","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_12","unstructured":"Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043\u20131065."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Ghiasi, S., Zhu, T., Lu, P., Hagenah, J., Khanh, P.N.Q., Hao, N.V., Thwaites, L., Clifton, D.A., and Consortium, V. (2022). Sepsis Mortality Prediction Using Wearable Monitoring in Low\u2013Middle Income Countries. Sensors, 22.","DOI":"10.3390\/s22103866"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jcrc.2006.02.005","article-title":"Heart rate variability monitoring in the detection of central nervous system complications in children with enterovirus infection","volume":"21","author":"Lin","year":"2006","journal-title":"J. Crit. Care"},{"key":"ref_16","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_17","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_18","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_19","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_20","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_21","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_22","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_23","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_24","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_25","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_26","unstructured":"Guo, M.H., Xu, T.X., Liu, J.J., Liu, Z.N., Jiang, P.T., Mu, T.J., Zhang, S.H., Martin, R.R., Cheng, M.M., and Hu, S.M. (2021). Attention Mechanisms in Computer Vision: A Survey. arXiv."},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/TIP.2017.2778563","article-title":"Recurrent spatial-temporal attention network for action recognition in videos","volume":"27","author":"Du","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4810","DOI":"10.3390\/app9224810","article-title":"Pre-configured deep convolutional neural networks with various time-frequency representations for biometrics from ECG signals","volume":"9","author":"Byeon","year":"2019","journal-title":"Appl. Sci."},{"key":"ref_34","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_35","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT press."},{"key":"ref_36","unstructured":"(2022, August 21). ePatch\u2122. The World\u2019s Most Wearable Holter Monitor. Available online: https:\/\/www.gobio.com\/clinical-research\/cardiac-safety\/epatch\/."},{"key":"ref_37","unstructured":"Dorthe Bodholt, S., Helge Bjarup Dissing, S., Ingeborg Helbech, H., Kenneth, E., Poul, J., and Karsten, H. (2022, August 21). Available online: https:\/\/backend.orbit.dtu.dk\/ws\/portalfiles\/portal\/102966188\/ePatch_A_Clinical_Overview_DTU_Technical_Report.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Van, H.M.T., Van Hao, N., Quoc, K.P.N., Hai, H.B., Yen, L.M., Nhat, P.T.H., Duong, H.T.H., Thuy, D.B., Zhu, T., and Greeff, H. (2021). Vital sign monitoring using wearable devices in a Vietnamese intensive care unit. BMJ Innov., 7.","DOI":"10.1136\/bmjinnov-2021-000707"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, P., Qiu, H., Qin, C., Bai, W., Rueckert, D., and Noble, J.A. (2020). Going deeper into cardiac motion analysis to model fine spatio-temporal features. Proceedings of the Annual Conference on Medical Image Understanding and Analysis, Springer.","DOI":"10.1007\/978-3-030-52791-4_23"},{"key":"ref_41","unstructured":"Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.c. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TBME.2017.2697916","article-title":"Highly accurate facial nerve segmentation refinement from CBCT\/CT imaging using a super-resolution classification approach","volume":"65","author":"Lu","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105004","DOI":"10.1088\/1361-6579\/aae021","article-title":"An open source benchmarked toolbox for cardiovascular waveform and interval analysis","volume":"39","author":"Vest","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Vollmer, M. (2019, January 8\u201311). HRVTool\u2014An Open-Source Matlab Toolbox for Analyzing Heart Rate Variability. Proceedings of the Computing in Cardiology 2019, Singapore.","DOI":"10.22489\/CinC.2019.032"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Vollmer, M. (2015, January 6\u20139). A robust, simple and reliable measure of heart rate variability using relative RR intervals. Proceedings of the 2015 Computing in Cardiology Conference (CinC), Nice, France.","DOI":"10.1109\/CIC.2015.7410984"},{"key":"ref_47","unstructured":"(2022, August 21). Heart Rate Variability Analysis. Available online: https:\/\/pypi.org\/project\/hrv-analysis\/."},{"key":"ref_48","unstructured":"(2022, August 21). Seven ECG Heartbeat Detection Algorithms and Heartrate Variability Analysis. Available online: https:\/\/www.ecdc.europa.eu\/en\/tetanus\/facts."},{"key":"ref_49","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 18\u201324). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning, PMLR, Virtual. Available online: https:\/\/proceedings.mlr.press\/v139\/touvron21a.html."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., and Shah, M. (2021). Transformers in vision: A survey. ACM Comput. Surv. CSUR.","DOI":"10.1145\/3505244"},{"key":"ref_51","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_52","unstructured":"Lopes, R.G., Fenu, S., and Starner, T. (2017). Data-free knowledge distillation for deep neural networks. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6554\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:20:37Z","timestamp":1760142037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,30]]},"references-count":53,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176554"],"URL":"https:\/\/doi.org\/10.3390\/s22176554","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,8,30]]}}}