{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:05:40Z","timestamp":1768255540887,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T00:00:00Z","timestamp":1669420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the R\u00e9gion Grand Est"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart\u2019s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.<\/jats:p>","DOI":"10.3390\/s22239190","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T08:13:09Z","timestamp":1669623189000},"page":"9190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5041-0938","authenticated-orcid":false,"given":"Jad","family":"Botros","sequence":"first","affiliation":[{"name":"Computer Science and Digital Society Laboratory (LIST3N), Universit\u00e9 de Technologie de Troyes, 10300 Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5048-9372","authenticated-orcid":false,"given":"Farah","family":"Mourad-Chehade","sequence":"additional","affiliation":[{"name":"Computer Science and Digital Society Laboratory (LIST3N), Universit\u00e9 de Technologie de Troyes, 10300 Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0399-5985","authenticated-orcid":false,"given":"David","family":"Laplanche","sequence":"additional","affiliation":[{"name":"Computer Science and Digital Society Laboratory (LIST3N), Universit\u00e9 de Technologie de Troyes, 10300 Troyes, France"},{"name":"P\u00f4le Sant\u00e9 Publique, H\u00f4pitaux Champagne Sud (HCS), 10000 Troyes, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1093\/oxfordjournals.eurheartj.a061500","article-title":"The defintion of heart failure","volume":"4","author":"Denolin","year":"1983","journal-title":"Eur. Heart J."},{"key":"ref_2","unstructured":"Malik, A., Brito, D., Vaqar, S., and Chhabra, L. (2022). Congestive Heart Failure, StatPearls [Internet]."},{"key":"ref_3","unstructured":"(2022, September 30). What Is Heart Failure? A Brief Description. Available online: https:\/\/www.heartfailurematters.org\/understanding-heart-failure\/what-is-heart-failure\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1002\/ejhf.483","article-title":"Epidemiology of heart failure: The prevalence of heart failure and ventricular dysfunction in older adults over time. A systematic review","volume":"18","author":"Hoes","year":"2016","journal-title":"Eur. J. Heart Fail."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.15420\/cfr.2016:25:2","article-title":"Global public health burden of heart failure","volume":"3","author":"Savarese","year":"2017","journal-title":"Card. Fail. Rev."},{"key":"ref_6","unstructured":"Sattar, Y., and Chhabra, L. (2021). Electrocardiogram, StatPearls [Internet]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10741-006-9481-0","article-title":"Diagnosis of heart failure in primary care","volume":"11","author":"Fonseca","year":"2006","journal-title":"Heart Fail. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jelectrocard.2022.10.011","article-title":"Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration","volume":"76","author":"Monfredi","year":"2022","journal-title":"J. Electrocardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1093\/eurheartj\/ehab368","article-title":"2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC","volume":"42","author":"McDonagh","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3186355","article-title":"Multilevel Classification and Detection of Cardiac Arrhythmias With High-Resolution Superlet Transform and Deep Convolution Neural Network","volume":"71","author":"Tripathi","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","article-title":"Machine learning for medical diagnosis: History, state of the art and perspective","volume":"23","author":"Kononenko","year":"2001","journal-title":"Artif. Intell. Med."},{"key":"ref_12","unstructured":"Asyali, M. (2003, January 17\u201321). Discrimination power of long-term heart rate variability measures. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine And Biology Society (IEEE Cat. No. 03CH37439), Cancun, Mexico."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11517-010-0728-5","article-title":"Discrimination power of long-term heart rate variability measures for chronic heart failure detection","volume":"49","author":"Melillo","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, G., Wang, L., Wang, Q., Zhou, G., Wang, Y., and Jiang, Q. (2014). A New Approach to Detect Congestive Heart Failure Using Short-Term Heart Rate Variability Measures. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093399"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cmpb.2016.03.020","article-title":"Congestive heart failure detection using random forest classifier","volume":"130","author":"Masetic","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, W., Liu, G., Su, S., Jiang, Q., and Nguyen, H. (2017, January 11\u201315). A CHF detection method based on deep learning with RR intervals. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine And Biology Society (EMBC), Jeju Island, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8037578"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhou, W., Liu, N., Xing, Y., and Zhou, X. (2018, January 18\u201321). CHF Detection with LSTM Neural Network. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine And Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512300"},{"key":"ref_18","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_19","first-page":"2303","article-title":"Heart Disease Recognition from ECG Signal Using Deep Learning","volume":"29","author":"Padmavathi","year":"2020","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1742-6596\/1642\/1\/012021","article-title":"Application of Deep Neural Network for Congestive Heart Failure Detection Using ECG Signals","volume":"1642","author":"Zhang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101597","DOI":"10.1016\/j.bspc.2019.101597","article-title":"A convolutional neural network approach to detect congestive heart failure","volume":"55","author":"Porumb","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, L., and Zhou, X. (2019). Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals. Sensors, 19.","DOI":"10.3390\/s19071502"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/S0735-1097(86)80478-8","article-title":"Survival of patients with severe congestive heart failure treated with oral milrinone","volume":"7","author":"Baim","year":"1986","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"118933","DOI":"10.1016\/j.eswa.2022.118933","article-title":"A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model","volume":"213","author":"Tiwari","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12641","DOI":"10.1038\/s41598-022-16517-4","article-title":"QRS detection and classification in Holter ECG data in one inference step","volume":"12","author":"Ivora","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1016\/j.procs.2020.03.309","article-title":"Hybrid CNN-SVM Classifier for Handwritten Digit Recognition","volume":"167","author":"Ahlawat","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fernandez, G., Madduri, A., Marami, B., Prastawa, M., Scott, R., Zeineh, J., and Donovan, M. (2021). Artificial intelligence methods for predictive image-based grading of human cancers. Artificial Intelligence and Deep Learning in Pathology, Elsevier.","DOI":"10.1016\/B978-0-323-67538-3.00009-9"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:27:15Z","timestamp":1760146035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,26]]},"references-count":30,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239190"],"URL":"https:\/\/doi.org\/10.3390\/s22239190","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,26]]}}}