{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:37:49Z","timestamp":1780591069798,"version":"3.54.1"},"reference-count":22,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.<\/jats:p>","DOI":"10.3390\/s24113485","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T13:32:55Z","timestamp":1716903175000},"page":"3485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2436-6835","authenticated-orcid":false,"given":"Lerina","family":"Aversano","sequence":"first","affiliation":[{"name":"Department of Agricultural Science, Food, Natural Resources and Engineering, University of Foggia, 71122 Foggia, FG, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3223-7032","authenticated-orcid":false,"given":"Mario Luca","family":"Bernardi","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, 82100 Benevento, BN, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2403-8313","authenticated-orcid":false,"given":"Marta","family":"Cimitile","sequence":"additional","affiliation":[{"name":"Department of Law and Digital Society, Unitelma Sapienza University, 00161 Rome, RM, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5598-0822","authenticated-orcid":false,"given":"Debora","family":"Montano","sequence":"additional","affiliation":[{"name":"CeRICT scrl, Regional Center Information Communication Technology, 82100 Benevento, BN, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5948-5845","authenticated-orcid":false,"given":"Riccardo","family":"Pecori","sequence":"additional","affiliation":[{"name":"Institute of Materials for Electronics and Magnetism, National Research Council of Italy, 43124 Parma, PR, Italy"},{"name":"SMARTEST Research Centre, eCampus University, 22060 Novedrate, CO, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"ref_1","unstructured":"Kaplan, D.T., Furman, M.I., and Pincus, S.M. (1990, January 23\u201326). Techniques for analyzing complexity in heart rate and beat-to-beat blood pressure signals. Proceedings of the Computers in Cardiology, Chicago, IL, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.emc.2005.08.013","article-title":"ECG techniques and technologies","volume":"24","author":"Garvey","year":"2006","journal-title":"Emerg. Med. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.1016\/j.procs.2023.10.279","article-title":"Early Diagnosis of Cardiac Diseases using ECG Images and CNN-2D","volume":"225","author":"Aversano","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100334","DOI":"10.1016\/j.cosrev.2020.100334","article-title":"Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review","volume":"39","author":"Matias","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Serhal, H., Abdallah, N., Marion, J.M., Chauvet, P., Oueidat, M., and Humeau-Heurtier, A. (2022). Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG. Comput. Biol. Med., 142.","DOI":"10.1016\/j.compbiomed.2021.105168"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6674695","DOI":"10.1155\/2021\/6674695","article-title":"Extreme learning machine for heartbeat classification with hybrid time-domain and wavelet time-frequency features","volume":"2021","author":"Xu","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Singh, R., Rajpal, N., and Mehta, R. (2021). An empiric analysis of wavelet-based feature extraction on deep learning and machine learning algorithms for arrhythmia classification. Int. J. Interact. Multimed. Artif. Intell., 25\u201334.","DOI":"10.9781\/ijimai.2020.11.005"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M., Montano, D., and Verdone, C. (2022, January 25\u201326). Using Machine Learning for early prediction of Heart Disease. Proceedings of the 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Larnaca, Cyprus.","DOI":"10.1109\/EAIS51927.2022.9787720"},{"key":"ref_9","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_10","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 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682668"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ferretti, J., Randazzo, V., Cirrincione, G., and Pasero, E. (2021). 1-D convolutional neural network for ECG arrhythmia classification. Prog. Artif. Intell. Neural Syst., 269\u2013279.","DOI":"10.1007\/978-981-15-5093-5_25"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Potes, C., Parvaneh, S., Rahman, A., and Conroy, B. (2016, January 11\u201314). Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. Proceedings of the 2016 computing in cardiology conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.182-399"},{"key":"ref_13","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_14","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_15","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":"2023","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_16","first-page":"495","article-title":"Introduction to convolutional neural networks","volume":"5","author":"Wu","year":"2017","journal-title":"Natl. Key Lab Nov. Softw. Technol. Nanjing Univ. China"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2020.04.157","article-title":"Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives","volume":"444","author":"Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1161\/CIRCULATIONAHA.107.187397","article-title":"Universal definition of myocardial infarction","volume":"116","author":"Thygesen","year":"2007","journal-title":"Circulation"},{"key":"ref_19","first-page":"II-180","article-title":"A new algorithm for detection of S1 and S2 heart sounds","volume":"Volume 2","author":"Kumar","year":"2006","journal-title":"Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings"},{"key":"ref_20","first-page":"2021","article-title":"ECG Images dataset of Cardiac Patients","volume":"2","author":"Khan","year":"2021","journal-title":"Mendeley Data"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Banerjee, C., Mukherjee, T., and Pasiliao, E. (2019, January 18\u201320). An Empirical Study on Generalizations of the ReLU Activation Function. Proceedings of the 2019 ACM Southeast Conference, ACM SE \u201919, New York, NY, USA.","DOI":"10.1145\/3299815.3314450"},{"key":"ref_22","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3485\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:49:41Z","timestamp":1760107781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,28]]},"references-count":22,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113485"],"URL":"https:\/\/doi.org\/10.3390\/s24113485","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,28]]}}}