{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T11:23:54Z","timestamp":1792841034052,"version":"4.5.0"},"reference-count":56,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T00:00:00Z","timestamp":1608508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004233","name":"Universitat Polit\u00e8cnica de Val\u00e8ncia","doi-asserted-by":"publisher","award":["PAID-10-19"],"award-info":[{"award-number":["PAID-10-19"]}],"id":[{"id":"10.13039\/501100004233","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007637","name":"COLCIENCIAS","doi-asserted-by":"publisher","award":["PDBCEx COLDOC 679"],"award-info":[{"award-number":["PDBCEx COLDOC 679"]}],"id":[{"id":"10.13039\/100007637","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.<\/jats:p>","DOI":"10.3390\/s20247353","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T20:42:01Z","timestamp":1608583321000},"page":"7353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1153-0616","authenticated-orcid":false,"given":"Jaime A.","family":"Rincon","sequence":"first","affiliation":[{"name":"Institut Valenci\u00e0 d\u2019Investigaci\u00f3 en Intel\u00b7lig\u00e8ncia Artificial (VRAIN), Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3018-4683","authenticated-orcid":false,"given":"Solanye","family":"Guerra-Ojeda","sequence":"additional","affiliation":[{"name":"Department of Physiology, School of Medicine, Universitat de Val\u00e8ncia, 46010 Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3649-6530","authenticated-orcid":false,"given":"Carlos","family":"Carrascosa","sequence":"additional","affiliation":[{"name":"Institut Valenci\u00e0 d\u2019Investigaci\u00f3 en Intel\u00b7lig\u00e8ncia Artificial (VRAIN), Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2743-6037","authenticated-orcid":false,"given":"Vicente","family":"Julian","sequence":"additional","affiliation":[{"name":"Institut Valenci\u00e0 d\u2019Investigaci\u00f3 en Intel\u00b7lig\u00e8ncia Artificial (VRAIN), Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1161\/CIRCRESAHA.117.308903","article-title":"Reducing the global burden of cardiovascular disease, part 1: The epidemiology and risk factors","volume":"121","author":"Joseph","year":"2017","journal-title":"Circ. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1093\/eurheartj\/ehaa641","article-title":"ESC Core Curriculum for the Cardiologist","volume":"41","author":"Tanner","year":"2020","journal-title":"Eur. Heart J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1177\/1357633X16689432","article-title":"Telecardiology and its settings of application: An update","volume":"24","author":"Molinari","year":"2018","journal-title":"J. Telemed. Telecare"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Majumder, S., Mondal, T., and Deen, M. (2017). Wearable Sensors for Remote Health Monitoring. Sensors, 17.","DOI":"10.3390\/s17010130"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Steinberg, C., Philippon, F., Sanchez, M., Fortier-Poisson, P., O\u2019Hara, G., Molin, F., Sarrazin, J.F., Nault, I., Blier, L., and Roy, K. (2019). A Novel Wearable Device for Continuous Ambulatory ECG Recording: Proof of Concept and Assessment of Signal Quality. Biosensors, 9.","DOI":"10.3390\/bios9010017"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TBME.2015.2465936","article-title":"Fully Textile, PEDOT:PSS Based Electrodes for Wearable ECG Monitoring Systems","volume":"63","author":"Pani","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"455401","DOI":"10.1088\/1361-6463\/ab3869","article-title":"Screen printed silver nanowire and graphene oxide hybrid transparent electrodes for long-term electrocardiography monitoring","volume":"52","author":"Xu","year":"2019","journal-title":"J. Phys. Appl. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Elkhodr, M., Shahrestani, S., and Cheung, H. (2016). Emerging wireless technologies in the internet of things: A comparative study. arXiv.","DOI":"10.4018\/978-1-5225-0287-6.ch015"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rohokale, V.M., Prasad, N.R., and Prasad, R. (March, January 28). A cooperative Internet of Things (IoT) for rural healthcare monitoring and control. Proceedings of the 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), Chennai, India.","DOI":"10.1109\/WIRELESSVITAE.2011.5940920"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Darshan, K., and Anandakumar, K. (2015, January 17\u201319). A comprehensive review on usage of Internet of Things (IoT) in healthcare system. Proceedings of the 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India.","DOI":"10.1109\/ERECT.2015.7499001"},{"key":"ref_11","first-page":"1","article-title":"ECG arrhythmia classification using modified visual geometry group network (mVGGNet)","volume":"38","author":"Jangra","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, J., Huang, X., Hu, Y., and Liu, Z. (2020, January 23\u201326). A Severity Diagnosis Method for Heart Disease based on Fusion Rough Sets. Proceedings of the 2020 2nd International Conference on Intelligent Medicine and Image Processing, Tianjin, China.","DOI":"10.1145\/3399637.3399643"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101788","DOI":"10.1016\/j.artmed.2019.101788","article-title":"An intelligent learning approach for improving ECG signal classification and arrhythmia analysis","volume":"103","author":"Sangaiah","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Marinucci, D., Sbrollini, A., Marcantoni, I., Morettini, M., Swenne, C.A., and Burattini, L. (2020). Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices. Sensors, 20.","DOI":"10.3390\/s20123570"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jothiramalingam, R., Jude, A., Patan, R., Ramachandran, M., Duraisamy, J.H., and Gandomi, A.H. (2020). Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal. Neural Comput. Appl., 1\u201311.","DOI":"10.1007\/s00521-020-05238-2"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101789","DOI":"10.1016\/j.artmed.2019.101789","article-title":"Comprehensive electrocardiographic diagnosis based on deep learning","volume":"103","author":"Lih","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_17","first-page":"1","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_18","doi-asserted-by":"crossref","first-page":"100251","DOI":"10.1016\/j.iot.2020.100251","article-title":"An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase","volume":"11","author":"Moghadas","year":"2020","journal-title":"Internet Things"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.2147\/COPD.S246353","article-title":"A post hoc Holter ECG analysis of olodaterol and formoterol in moderate-to-very-severe COPD","volume":"15","author":"Andreas","year":"2020","journal-title":"Int. J. Chronic Obstr. Pulm. Dis."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adam, S., Zahra, S.A., Chor, C.Y.T., Khare, Y., and Harky, A. (2020). COVID-19 pandemic and its impact on service provision: A cardiology prospect. Acta Cardiol., 1\u20138.","DOI":"10.1080\/00015385.2020.1787636"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jadhav, K.B., and Chaskar, U.M. (2017, January 19\u201320). Design and development of smart phone based ECG monitoring system. Proceedings of the 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT.2017.8256862"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/JIOT.2016.2645125","article-title":"A Wireless Health Monitoring System Using Mobile Phone Accessories","volume":"4","author":"Mahmud","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1186\/s13638-018-1308-x","article-title":"Wearable IoT enabled real-time health monitoring system","volume":"2018","author":"Wan","year":"2018","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1016\/j.procs.2017.08.265","article-title":"The IoT-based heart disease monitoring system for pervasive healthcare service","volume":"112","author":"Li","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s10916-018-1093-4","article-title":"Internet of things with maximal overlap discrete wavelet transform for remote health monitoring of abnormal ECG signals","volume":"42","author":"Sundarasekar","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Djelouat, H., Baali, H., Amira, A., and Bensaali, F. (2017, January 21\u201323). IoT based compressive sensing for ECG monitoring. Proceedings of the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, UK.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2017.32"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jethwa, B., Panchasara, M., Zanzarukiya, A., and Parekh, R. (2020, January 2\u20134). Realtime Wireless Embedded Electronics for Soldier Security. Proceedings of the 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT50063.2020.9198537"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Panagi, G., and Katzis, K. (2020, January 7\u201311). Towards 3-Lead Electrocardiogram Monitoring over LoRa: A Conceptual Design. Proceedings of the 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland.","DOI":"10.1109\/ICCWorkshops49005.2020.9145424"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s13246-020-00863-6","article-title":"Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques","volume":"43","author":"Qaisar","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huda, N., Khan, S., Abid, R., Shuvo, S.B., Labib, M.M., and Hasan, T. (2020, January 5\u20137). A Low-cost, Low-energy Wearable ECG System with Cloud-Based Arrhythmia Detection. Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh.","DOI":"10.1109\/TENSYMP50017.2020.9230619"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xia, X., and Leng, M. (2020, January 8\u201310). Big Health Data Resource Integration Method Based on Hybrid Cloud and Fog Computing. Proceedings of the 3rd International Conference on Electronics, Communications and Control Engineering, Phuket, Thailand.","DOI":"10.1145\/3396730.3396737"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shukla, S., Hassan, M.F., Khan, M.K., Jung, L.T., and Awang, A. (2019). An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224934"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/IOTM.0001.1900096","article-title":"Leveraging IoT and Fog Computing in Healthcare Systems","volume":"3","author":"Awaisi","year":"2020","journal-title":"IEEE Internet Things Mag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shaheen, Q., Shiraz, M., Hashmi, M.U., Mahmood, D., Zhu, Z., and Akhtar, R. (2020). A Lightweight Location-Aware Fog Framework (LAFF) for QoS in Internet of Things Paradigm. Mob. Inf. Syst., 2020.","DOI":"10.1155\/2020\/8871976"},{"key":"ref_35","unstructured":"Gia, T.N., Jiang, M., Sarker, V.K., Rahmani, A.M., Westerlund, T., Liljeberg, P., and Tenhunen, H. (2017, January 26\u201330). Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes. Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mutlag, A.A., Khanapi Abd Ghani, M., Mohammed, M.A., Maashi, M.S., Mohd, O., Mostafa, S.A., Abdulkareem, K.H., Marques, G., and de la Torre D\u00edez, I. (2020). MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management. Sensors, 20.","DOI":"10.3390\/s20071853"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/CC.2017.8233646","article-title":"Proactive personalized services through fog-cloud computing in large-scale IoT-based healthcare application","volume":"14","author":"He","year":"2017","journal-title":"China Commun."},{"key":"ref_38","first-page":"100378","article-title":"Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing","volume":"26","author":"Bandopadhaya","year":"2020","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tayeh, G.B., Azar, J., Makhoul, A., Guyeux, C., and Demerjian, J. (July, January 28). A Wearable LoRa-Based Emergency System for Remote Safety Monitoring. Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Beijing, China.","DOI":"10.1109\/IWCMC48107.2020.9148359"},{"key":"ref_40","first-page":"2581","article-title":"Real Time Tracking and Security System for Rural Areas Using LoRa Network","volume":"3","author":"Shobha","year":"2020","journal-title":"Int. J. Res. Eng. Sci. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1093\/eurheartj\/ehw334","article-title":"Cardiovascular disease in Europe: Epidemiological update 2016","volume":"37","author":"Townsend","year":"2016","journal-title":"Eur. Heart J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"16016","DOI":"10.1038\/nrdp.2016.16","article-title":"Atrial fibrillation","volume":"2","author":"Lip","year":"2016","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_43","first-page":"1468","article-title":"Prevalence of atrial fibrillation in the Italian elderly population and projections from 2020 to 2060 for Italy and the European Union: The FAI Project","volume":"21","author":"Bellino","year":"2019","journal-title":"EP Eur."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"E886","DOI":"10.21037\/jtd.2016.09.13","article-title":"Atrial fibrillation: Review of current treatment strategies","volume":"8","author":"Xu","year":"2016","journal-title":"J. Thorac. Dis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1038\/s41569-020-0413-9","article-title":"COVID-19 and cardiovascular disease: From basic mechanisms to clinical perspectives","volume":"17","author":"Nishiga","year":"2020","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/S0140-6736(11)61229-4","article-title":"Telemedicine and remote management of patients with heart failure","volume":"378","author":"Anker","year":"2011","journal-title":"Lancet"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00399-020-00671-1","article-title":"Derzeitige und zuk\u00fcnftige Bedeutung der Telemedizin im Arrhythmienotfall","volume":"31","author":"Schwab","year":"2020","journal-title":"Herzschrittmachertherapie Elektrophysiologie"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8840910","DOI":"10.1155\/2020\/8840910","article-title":"Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System","volume":"2020","author":"Yin","year":"2020","journal-title":"J. Healthc. Eng."},{"key":"ref_49","unstructured":"(2020, December 14). Fog Computing and the Internet of Things: Extend the Cloud to Where the Things are. Cisco White Paper. Available online: https:\/\/www.cisco.com\/c\/dam\/en_us\/solutions\/trends\/iot\/docs\/computing-overview.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., and Buyya, R. (2016). Fog computing: Principles, architectures, and applications. Internet of Things, Elsevier.","DOI":"10.1016\/B978-0-12-805395-9.00004-6"},{"key":"ref_51","unstructured":"OpenFog Consortium (2017). OpenFog Reference Architecture for Fog Computing, OpenFog Consortium."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pet\u00e4j\u00e4j\u00e4rvi, J., Mikhaylov, K., H\u00e4m\u00e4l\u00e4inen, M., and Iinatti, J. (2016, January 20\u201323). Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring. Proceedings of the 2016 10th International Symposium on Medical Information and Communication Technology (ISMICT), Worcester, MA, USA.","DOI":"10.1109\/ISMICT.2016.7498898"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.3390\/s16091466","article-title":"A study of LoRa: Long range & low power networks for the internet of things","volume":"16","author":"Augustin","year":"2016","journal-title":"Sensors"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"183","DOI":"10.11591\/eei.v7i2.1174","article-title":"A review of low power wide area technology in licensed and unlicensed spectrum for IoT use cases","volume":"7","author":"Ismail","year":"2018","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1550147717750030","DOI":"10.1177\/1550147717750030","article-title":"Energy efficiency comparison between data rate control and transmission power control algorithms for wireless body sensor networks","volume":"14","author":"Sodhro","year":"2018","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_56","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7353\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:48:09Z","timestamp":1760179689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7353"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,21]]},"references-count":56,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247353"],"URL":"https:\/\/doi.org\/10.3390\/s20247353","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,21]]}}}