{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:19:33Z","timestamp":1783570773032,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University Research Support","award":["TURSP-2020\/277"],"award-info":[{"award-number":["TURSP-2020\/277"]}]},{"name":"Taif University Research Support","award":["IF-2020-NBU-228"],"award-info":[{"award-number":["IF-2020-NBU-228"]}]},{"name":"Taif University Research Support","award":["619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP"],"award-info":[{"award-number":["619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP"]}]},{"name":"Ministry of Education of Saudi Arabia","award":["TURSP-2020\/277"],"award-info":[{"award-number":["TURSP-2020\/277"]}]},{"name":"Ministry of Education of Saudi Arabia","award":["IF-2020-NBU-228"],"award-info":[{"award-number":["IF-2020-NBU-228"]}]},{"name":"Ministry of Education of Saudi Arabia","award":["619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP"],"award-info":[{"award-number":["619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP"]}]},{"name":"European Commission","award":["TURSP-2020\/277"],"award-info":[{"award-number":["TURSP-2020\/277"]}]},{"name":"European Commission","award":["IF-2020-NBU-228"],"award-info":[{"award-number":["IF-2020-NBU-228"]}]},{"name":"European Commission","award":["619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP"],"award-info":[{"award-number":["619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.<\/jats:p>","DOI":"10.3390\/s22155606","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T03:21:16Z","timestamp":1658978476000},"page":"5606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2985-2771","authenticated-orcid":false,"given":"Saad","family":"Irfan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6470-075X","authenticated-orcid":false,"given":"Nadeem","family":"Anjum","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6674-7890","authenticated-orcid":false,"given":"Turke","family":"Althobaiti","sequence":"additional","affiliation":[{"name":"Faculty of Science, Northern Border University, Arar 1321, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Alhumaidi","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, College of Ranyah, Taif University, Taif 11099, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul Basit","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ccep.2010.10.012","article-title":"Overview of basic mechanisms of cardiac arrhythmia","volume":"3","author":"Antzelevitch","year":"2011","journal-title":"Card. Electrophysiol. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8013","DOI":"10.1016\/j.eswa.2012.01.164","article-title":"Daily living activity recognition based on statistical feature quality group selection","volume":"39","author":"Banos","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_3","unstructured":"Clifford, G.D., Azuaje, F., and McSharry, P. (2006). Advanced Methods and Tools for ECG Data Analysis, Artech House."},{"key":"ref_4","unstructured":"Potter, L. (2011). Understanding an ECG, Independently."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compbiomed.2014.02.012","article-title":"Current methods in electrocardiogram characterization","volume":"48","author":"Martis","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_6","first-page":"179","article-title":"ECG signal classification using ensemble decision tree","volume":"16","author":"Mert","year":"2012","journal-title":"J. Trends Dev. Mach. Assoc. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102","DOI":"10.4018\/jehmc.2012100106","article-title":"Comparative study of ECG classification performance using decision tree algorithms","volume":"3","author":"Charfi","year":"2012","journal-title":"Int. J. E-Health Med. Commun. (IJEHMC)"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s11760-021-02009-x","article-title":"Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree","volume":"16","author":"Kumari","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jare.2012.05.007","article-title":"QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases","volume":"4","author":"Saini","year":"2013","journal-title":"J. Adv. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saini, I., Singh, D., and Khosla, A. (2013, January 15\u201317). Delineation of ecg wave components using k-nearest neighbor (knn) algorithm: Ecg wave delineation using knn. Proceedings of the 2013 10th International Conference on Information Technology: New Generations, Las Vegas, NV, USA.","DOI":"10.1109\/ITNG.2013.76"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Saini, R., Bindal, N., and Bansal, P. (2015, January 15\u201316). Classification of heart diseases from ECG signals using wavelet transform and kNN classifier. Proceedings of the International Conference on Computing, Communication & Automation, Greater Noida, India.","DOI":"10.1109\/CCAA.2015.7148561"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"122","DOI":"10.4314\/ijest.v3i8.10","article-title":"Arrhythmia classification using SVM with selected features","volume":"3","author":"Kohli","year":"2011","journal-title":"Int. J. Eng. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1083-6","article-title":"ECG signal classification using various machine learning techniques","volume":"42","author":"Celin","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ge, Z., Zhu, Z., Feng, P., Zhang, S., Wang, J., and Zhou, B. (2019, January 9\u201310). ECG-signal classification using SVM with multi-feature. Proceedings of the 2019 8th International Symposium on Next Generation Electronics (ISNE), Zhengzhou, China.","DOI":"10.1109\/ISNE.2019.8896430"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Thilagavathy, R., Srivatsan, R., Sreekarun, S., Sudeshna, D., Priya, P.L., and Venkataramani, B. (2020, January 5\u20137). Real-time ECG signal feature extraction and classification using support vector machine. Proceedings of the 2020 International Conference on Contemporary Computing and Applications (IC3A), Lucknow, India.","DOI":"10.1109\/IC3A48958.2020.233266"},{"key":"ref_16","first-page":"31","article-title":"Investigating cardiac arrhythmia in ECG using random forest classification","volume":"37","author":"Kumar","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s41782-021-00138-4","article-title":"Sleep Apnea Classification Using Random Forest via ECG","volume":"5","author":"Razi","year":"2021","journal-title":"Sleep Vigil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.21037\/mhealth.2017.09.01","article-title":"Deep learning for cardiac computer-aided diagnosis: Benefits, issues & solutions","volume":"3","author":"Loh","year":"2017","journal-title":"Mhealth"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1255","DOI":"10.1002\/widm.1255","article-title":"From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures","volume":"9","author":"Zhong","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1016\/j.procs.2018.05.045","article-title":"Classification of ECG arrhythmia using recurrent neural networks","volume":"132","author":"Singh","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101856","DOI":"10.1016\/j.artmed.2020.101856","article-title":"ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network","volume":"106","author":"Zhang","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sadasivuni, S., Chowdhury, R., Karnam, V.E.G., Banerjee, I., and Sanyal, A. (2021, January 22\u201328). Recurrent neural network circuit for automated detection of atrial fibrillation from raw ECG. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea.","DOI":"10.1109\/ISCAS51556.2021.9401666"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1007\/s11760-020-01666-8","article-title":"Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model","volume":"14","author":"Pandey","year":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103753","DOI":"10.1016\/j.compbiomed.2020.103753","article-title":"Automated pre-screening of arrhythmia using hybrid combination of Fourier\u2013Bessel expansion and LSTM","volume":"120","author":"Sharma","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-021-01571-1","article-title":"AFibNet: An implementation of atrial fibrillation detection with convolutional neural network","volume":"21","author":"Tutuko","year":"2021","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Izci, E., Ozdemir, M.A., Degirmenci, M., and Akan, A. (2019, January 3\u20135). Cardiac arrhythmia detection from 2d ecg images by using deep learning technique. Proceedings of the 2019 Medical Technologies Congress (TIPTEKNO), Izmir, Turkey.","DOI":"10.1109\/TIPTEKNO.2019.8895011"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.inffus.2019.06.024","article-title":"Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network","volume":"53","author":"Yao","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Verma, D., and Agarwal, S. (2018, January 19\u201322). Cardiac Arrhythmia Detection from Single-lead ECG using CNN and LSTM assisted by Oversampling. Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India.","DOI":"10.1109\/ICACCI.2018.8554541"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102194","DOI":"10.1016\/j.bspc.2020.102194","article-title":"Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets","volume":"63","author":"Petmezas","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_30","unstructured":"Wang, J., and Li, W. (2020). Atrial fibrillation detection and ECG classification based on CNN-BILSTM. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"114452","DOI":"10.1016\/j.eswa.2020.114452","article-title":"Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach","volume":"169","author":"Hirsch","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Nurmaini, S., Darmawahyuni, A., Sakti Mukti, A.N., Rachmatullah, M.N., Firdaus, F., and Tutuko, B. (2020). Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification. Electronics, 9.","DOI":"10.3390\/electronics9010135"},{"key":"ref_33","unstructured":"Canlas, R. (2009). Data Mining in Healthcare: Current Applications and Issues. [Master\u2019s Thesis, School of Information Systems & Management, Carnegie Mellon University]."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gupta, A., Banerjee, A., Babaria, D., Lotlikar, K., and Raut, H. (2022). Prediction and classification of cardiac arrhythmia. Sentimental Analysis and Deep Learning, Springer.","DOI":"10.1007\/978-981-16-5157-1_41"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3561","DOI":"10.1016\/j.eswa.2012.12.063","article-title":"ECG arrhythmia classification based on optimum-path forest","volume":"40","author":"Luz","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sarfraz, M., Khan, A.A., and Li, F.F. (2014, January 2\u20135). Using independent component analysis to obtain feature space for reliable ECG Arrhythmia classification. Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Belfast, UK.","DOI":"10.1109\/BIBM.2014.6999249"},{"key":"ref_37","first-page":"1","article-title":"Classification of arrhythmia using conjunction of machine learning algorithms and ECG diagnostic criteria","volume":"1","author":"Batra","year":"1975","journal-title":"Train. J."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Singh, N., and Singh, P. (2019). Cardiac arrhythmia classification using machine learning techniques. Engineering Vibration, Communication and Information Processing, Springer.","DOI":"10.1007\/978-981-13-1642-5_42"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3389\/fphy.2019.00103","article-title":"A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection","volume":"7","author":"Alfaras","year":"2019","journal-title":"Front. Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6320651","DOI":"10.1155\/2019\/6320651","article-title":"An effective LSTM recurrent network to detect arrhythmia on imbalanced ECG dataset","volume":"2019","author":"Gao","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rana, A., and Kim, K.K. (2019, January 6\u20139). ECG heartbeat classification using a single layer lstm model. Proceedings of the 2019 International SoC Design Conference (ISOCC), Jeju, Korea.","DOI":"10.1109\/ISOCC47750.2019.9027740"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s12553-021-00552-8","article-title":"A comparative study and analysis of LSTM deep neural networks for heartbeats classification","volume":"11","author":"Hiriyannaiah","year":"2021","journal-title":"Health Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"S70","DOI":"10.1016\/j.jelectrocard.2019.08.004","article-title":"Cardiac arrhythmia detection using deep learning: A review","volume":"57","author":"Parvaneh","year":"2019","journal-title":"J. Electrocardiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3033072","article-title":"A multitier deep learning model for arrhythmia detection","volume":"70","author":"Hammad","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100886","DOI":"10.1016\/j.isci.2020.100886","article-title":"Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model","volume":"23","author":"Chen","year":"2020","journal-title":"Iscience"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"564015","DOI":"10.3389\/fncom.2020.564015","article-title":"A study on arrhythmia via ECG signal classification using the convolutional neural network","volume":"14","author":"Wu","year":"2021","journal-title":"Front. Comput. Neurosci."},{"key":"ref_48","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_49","unstructured":"Guvenir, H.A., Acar, B., Demiroz, G., and Cekin, A. (1997, January 7\u201310). A supervised machine learning algorithm for arrhythmia analysis. Proceedings of the Computers in Cardiology 1997, Lund, Sweden."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1109\/78.157290","article-title":"The discrete wavelet transform: Wedding the a trous and Mallat algorithms","volume":"40","author":"Shensa","year":"1992","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_53","unstructured":"Warden, P., and Situnayake, D. (2019). Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers, O\u2019Reilly Media."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5606\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:57:17Z","timestamp":1760140637000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,27]]},"references-count":53,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155606"],"URL":"https:\/\/doi.org\/10.3390\/s22155606","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,27]]}}}