{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:21:17Z","timestamp":1785421277765,"version":"3.56.0"},"reference-count":43,"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":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSPD2023R1027"],"award-info":[{"award-number":["RSPD2023R1027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the \u201cECG Images dataset of Cardiac Patients\u201d, comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method\u2019s main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management.<\/jats:p>","DOI":"10.3390\/s23187697","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"7697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0078-7849","authenticated-orcid":false,"given":"Tariq","family":"Sadad","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7445-7121","authenticated-orcid":false,"given":"Mejdl","family":"Safran","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inayat","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1268-9613","authenticated-orcid":false,"given":"Sultan","family":"Alfarhood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Razaullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hindia, M.N., Rahman, T.A., Ojukwu, H., Hanafi, E.B., and Fattouh, A. (2016). Enabling Remote Health-Caring Utilizing IoT Concept over LTE-Femtocell Networks. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155077"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1001\/jama.2021.5469","article-title":"The Leading Causes of Death in the US for 2020","volume":"325","author":"Ahmad","year":"2021","journal-title":"JAMA-J. Am. Med. Assoc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/TBME.2009.2024531","article-title":"Physiological-Model-Constrained Noninvasive Reconstruction of Volumetric Myocardial Transmembrane Potentials","volume":"57","author":"Wang","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41597-020-0386-x","article-title":"A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients","volume":"7","author":"Zheng","year":"2020","journal-title":"Sci. Data"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5512243","DOI":"10.1155\/2021\/5512243","article-title":"Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network","volume":"2021","author":"Khan","year":"2021","journal-title":"Complexity"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"880","DOI":"10.3390\/electronics10080880","article-title":"A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges","volume":"10","author":"Ghaffar","year":"2021","journal-title":"Electronics"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hassan, R., Qamar, F., Hasan, M.K., Aman, A.H.M., and Ahmed, A.S. (2020). Internet of Things and Its Applications: A Comprehensive Survey. Symmetry, 12.","DOI":"10.3390\/sym12101674"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111762","DOI":"10.1016\/j.enbuild.2021.111762","article-title":"IoT Task Management Mechanism Based on Predictive Optimization for Efficient Energy Consumption in Smart Residential Buildings","volume":"257","author":"Imran","year":"2022","journal-title":"Energy Build."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5681","DOI":"10.3233\/JIFS-201614","article-title":"A Task Orchestration Approach for Efficient Mountain Fire Detection Based on Microservice and Predictive Analysis in IoT Environment","volume":"40","author":"Imran","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Iqbal, N., Ahmad, S., and Kim, D.H. (2021). Health Monitoring System for Elderly Patients Using Intelligent Task Mapping Mechanism in Closed Loop Healthcare Environment. Symmetry, 13.","DOI":"10.3390\/sym13020357"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1504\/IJBET.2019.097305","article-title":"Secure and Intelligent Architecture for Cloud-Based Healthcare Applications in Wireless Body Sensor Networks","volume":"29","author":"Rani","year":"2019","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zaman, U., Mehmood, F., Iqbal, N., Kim, J., and Ibrahim, M. (2022). Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications. Electronics, 11.","DOI":"10.3390\/electronics11121893"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qayyum, F., Kim, D.-H., Bong, S.-J., Chi, S.-Y., and Choi, Y.-H. (2022). A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery. Materials, 15.","DOI":"10.3390\/ma15041428"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2200517","DOI":"10.1002\/adts.202200517","article-title":"Intelligent Material Data Preparation Mechanism Based on Ensemble Learning for AI-Based Ceramic Material Analysis","volume":"5","author":"Imran","year":"2022","journal-title":"Adv. Theory Simul."},{"key":"ref_15","first-page":"100033","article-title":"A Review on Deep Learning Methods for ECG Arrhythmia Classification","volume":"7","author":"Ebrahimi","year":"2020","journal-title":"Expert Syst. Appl. X"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Iqbal, N., Ahmad, S., Ahmad, R., and Kim, D.-H. (2021). A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring. Sensors, 21.","DOI":"10.3390\/s21165430"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1049\/htl.2015.0029","article-title":"Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition","volume":"2","author":"Gupta","year":"2015","journal-title":"Healthc. Technol. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gaceck, A., and Pedryez, W. (2011). ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence, Springer.","DOI":"10.1007\/978-0-85729-868-3"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s10489-018-1179-1","article-title":"Deep Convolutional Neural Network for the Automated Diagnosis of Congestive Heart Failure Using ECG Signals","volume":"49","author":"Acharya","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.eswa.2018.08.011","article-title":"A Deep Learning Approach for Real-Time Detection of Atrial Fibrillation","volume":"115","author":"Andersen","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chamatidis, I., Katsika, A., and Spathoulas, G. (2017, January 23\u201326). Using Deep Learning Neural Networks for ECG Based Authentication. Proceedings of the International Carnahan Conference on Security Technology, Madrid, Spain.","DOI":"10.1109\/CCST.2017.8167816"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.procs.2017.11.238","article-title":"Cardiac Arrhythmia Detection Using Deep Learning","volume":"120","author":"Isin","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Limam, M., and Precioso, F. (2017, January 24\u201327). Atrial Fibrillation Detection and ECG Classification Based on Convolutional Recurrent Neural Network. Proceedings of the Computing in Cardiology, Rennes, France.","DOI":"10.22489\/CinC.2017.171-325"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2018.09.101","article-title":"Heart Sounds Classification Using a Novel 1-D Convolutional Neural Network with Extremely Low Parameter Consumption","volume":"392","author":"Xiao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Noman, F., Ting, C.M., Salleh, S.H., and Ombao, H. (2019, January 12\u201317). Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682668"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16529","DOI":"10.1109\/ACCESS.2018.2807700","article-title":"An Automatic Cardiac Arrhythmia Classification System with Wearable Electrocardiogram","volume":"6","author":"Xia","year":"2018","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3361561","article-title":"Transformation in Healthcare by Wearable Devices for Diagnostics and Guidance of Treatment","volume":"1","author":"Mahajan","year":"2020","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"92871","DOI":"10.1109\/ACCESS.2019.2928017","article-title":"ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102779","DOI":"10.1016\/j.bspc.2021.102779","article-title":"A Novel Deep Learning Based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition","volume":"68","author":"Virgeniya","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.bspc.2017.11.010","article-title":"Feature Fusion for Imbalanced ECG Data Analysis","volume":"41","author":"Lu","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ji, Y., Zhang, S., and Xiao, W. (2019). Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network. Sensors, 19.","DOI":"10.3390\/s19112558"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/JBHI.2018.2858789","article-title":"Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation from Single Lead Short ECG Recordings","volume":"22","author":"Fan","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3390\/ijerph9020391","article-title":"Physical Activity and Risk of Cardiovascular Disease\u2014A Meta-Analysis of Prospective Cohort Studies","volume":"9","author":"Li","year":"2012","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_34","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_35","first-page":"139","article-title":"DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks","volume":"5","author":"Johnson","year":"2001","journal-title":"Ad Hoc Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.3390\/s130201942","article-title":"A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications","volume":"13","author":"Machado","year":"2013","journal-title":"Sensors"},{"key":"ref_37","unstructured":"Khan, A.H., and Hussain, M. (2023, July 20). ECG Images Dataset of Cardiac Patients. Mendeley Data, V2. Available online: https:\/\/data.mendeley.com\/datasets\/gwbz3fsgp8\/2."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xue, J., and Dana, K. (2017, January 21\u201326). Deep ten: Texture encoding network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.309"},{"key":"ref_39","unstructured":"Fran, C. (2017, January 21\u201326). Deep learning with depth wise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"BBA-Protein Struct."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1672677","DOI":"10.1155\/2022\/1672677","article-title":"Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing","volume":"2022","author":"Sadad","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jocs.2018.09.015","article-title":"Fuzzy C-Means and Region Growing Based Classification of Tumor from Mammograms Using Hybrid Texture Feature","volume":"29","author":"Sadad","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1109\/TAI.2022.3159505","article-title":"Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods","volume":"4","author":"Abubaker","year":"2022","journal-title":"IEEE Trans. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7697\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:13Z","timestamp":1760129173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187697"],"URL":"https:\/\/doi.org\/10.3390\/s23187697","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]}}}