{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T20:27:28Z","timestamp":1793478448644,"version":"4.5.0"},"reference-count":72,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human\u2019s life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart\u2019s electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT\u2013BIH arrhythmia database. The obtained results show that the proposed approach provides \u224898.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.<\/jats:p>","DOI":"10.3390\/bioengineering9040152","type":"journal-article","created":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T02:59:52Z","timestamp":1648954792000},"page":"152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":181,"title":["A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9727-5961","authenticated-orcid":false,"given":"Parul","family":"Madan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devesh Pratap","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manoj","family":"Diwakar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhaskar","family":"Pant","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avadh","family":"Kishor","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e000504","DOI":"10.1161\/JAHA.114.000504","article-title":"Sustainable development goals and the future of cardiovascular health: A statement from the Global Cardiovascular Disease Taskforce","volume":"3","author":"Zoghbi","year":"2014","journal-title":"J. Am. Heart Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.amjcard.2013.11.044","article-title":"Review of complementary and alternative medical treatment of arrhythmias","volume":"113","author":"Brenyo","year":"2014","journal-title":"Am. J. Cardiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1136\/heartjnl-2011-300953","article-title":"Sudden cardiac death and inherited channelopathy: The basic electrophysiology of the myocyte and myocardium in ion channel disease","volume":"98","author":"Martin","year":"2012","journal-title":"Heart"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zubair, M., Kim, J., and Yoon, C. (2016, January 26). An automated ECG beat classification system using convolutional neural networks. Proceedings of the 2016 6th International Conference on IT Convergence and Security (ICITCS), Prague, Czech Republic.","DOI":"10.1109\/ICITCS.2016.7740310"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.compbiomed.2018.06.002","article-title":"Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats","volume":"102","author":"Oh","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2168","DOI":"10.1109\/TBME.2011.2113395","article-title":"Optimization of ECG classification by means of feature selection","volume":"58","author":"Mar","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102162","DOI":"10.1016\/j.bspc.2020.102162","article-title":"Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM","volume":"63","author":"Londhe","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1109\/TBME.2012.2213253","article-title":"Heartbeat classification using morphological and dynamic features of ECG signals","volume":"59","author":"Ye","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.measurement.2017.05.022","article-title":"Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities","volume":"108","author":"Sahoo","year":"2017","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TBME.2004.824138","article-title":"Support vector machine-based expert system for reliable heartbeat recognition","volume":"51","author":"Osowski","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.eswa.2017.09.022","article-title":"Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system","volume":"92","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1850025","DOI":"10.1142\/S0218001418500258","article-title":"Vehicle driving direction control based on compressed network","volume":"32","author":"Yang","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_13","first-page":"898","article-title":"Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine","volume":"186","author":"Polat","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TITB.2008.923147","article-title":"Classification of electrocardiogram signals with support vector machines and particle swarm optimization","volume":"12","author":"Melgani","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nasiri, J.A., Naghibzadeh, M., Yazdi, H.S., and Naghibzadeh, B. (2009, January 25\u201327). ECG arrhythmia classification with support vector machines and genetic algorithm. Proceedings of the 2009 Third UKSim European Symposium on Computer Modeling and Simulation, Athens, Greece.","DOI":"10.1109\/EMS.2009.39"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.medengphy.2010.08.007","article-title":"Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification","volume":"32","author":"Dutta","year":"2010","journal-title":"Med. Eng. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Desai, U., Martis, R.J., Nayak, C.G., Sarika, K., and Seshikala, G. (2015, January 17\u201320). Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India.","DOI":"10.1109\/INDICON.2015.7443220"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, K., and Kang, K. (2013, January 18\u201321). Arrhythmia detection from heartbeat using k-nearest neighbor classifier. Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China.","DOI":"10.1109\/BIBM.2013.6732594"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jun, T.J., Park, H.J., Yoo, H., Kim, Y.H., and Kim, D. (2016, January 16\u201320). GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591930"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","article-title":"Real-time patient-specific ECG classification by 1-D convolutional neural networks","volume":"63","author":"Kiranyaz","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ins.2017.06.027","article-title":"Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals","volume":"415","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kachuee, M., Fazeli, S., and Sarrafzadeh, M. (2018, January 4\u20137). Ecg heartbeat classification: A deep transferable representation. Proceedings of the 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, USA.","DOI":"10.1109\/ICHI.2018.00092"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"222","DOI":"10.18201\/ijisae.2016SpecialIssue-146978","article-title":"A multistage deep belief networks application on arrhythmia classification","volume":"4","author":"Altan","year":"2016","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1016\/j.future.2017.08.039","article-title":"Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network","volume":"79","author":"Acharya","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compbiomed.2017.12.007","article-title":"Detecting atrial fibrillation by deep convolutional neural networks","volume":"93","author":"Xia","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Savalia, S., and Emamian, V. (2018). Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks. Bioengineering, 5.","DOI":"10.3390\/bioengineering5020035"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jekova, I., and Krasteva, V. (2021). Optimization of end-to-end convolutional neural networks for analysis of out-of-hospital cardiac arrest rhythms during cardiopulmonary resuscitation. Sensors, 21.","DOI":"10.3390\/s21124105"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Krasteva, V., M\u00e9n\u00e9tr\u00e9, S., Didon, J.P., and Jekova, I. (2020). Fully convolutional deep neural networks with optimized hyperparameters for detection of shockable and non-shockable rhythms. Sensors, 20.","DOI":"10.3390\/s20102875"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Elola, A., Aramendi, E., Irusta, U., Pic\u00f3n, A., Alonso, E., Owens, P., and Idris, A. (2019). Deep neural networks for ECG-based pulse detection during out-of-hospital cardiac arrest. Entropy, 21.","DOI":"10.3390\/e21030305"},{"key":"ref_33","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/s41591-019-0359-9","article-title":"Publisher correction: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"key":"ref_35","unstructured":"Kacprzyk, J. (2012). Studies in Computational Intelligence, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ping, Y., Chen, C., Wu, L., Wang, Y., and Shu, M. (2020). Automatic detection of atrial fibrillation based on CNN-LSTM and shortcut connection. Healthcare, 8.","DOI":"10.3390\/healthcare8020139"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2195922","DOI":"10.1155\/2021\/2195922","article-title":"Classification of arrhythmia in heartbeat detection using deep learning","volume":"2021","author":"Ullah","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9951905","DOI":"10.1155\/2021\/9951905","article-title":"Classification of mental stress using cnn-lstm algorithms with electrocardiogram signals","volume":"2021","author":"Kang","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH arrhythmia database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_43","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_44","first-page":"16746","article-title":"Preprocessing Techniques of Electrocardiogram","volume":"5","author":"Kulkarni","year":"2016","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.procs.2016.05.145","article-title":"Comparative analysis of wavelet thresholding techniques with wavelet-wiener filter on ECG signal","volume":"87","author":"Priya","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.measurement.2016.09.014","article-title":"Efficient automatic detection of QRS complexes in ECG signal based on reverse biorthogonal wavelet decomposition and nonlinear filtering","volume":"94","author":"Mourad","year":"2016","journal-title":"Measurement"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/1475-925X-10-56","article-title":"An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects","volume":"10","author":"Kim","year":"2011","journal-title":"Biomed. Eng. Online"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1016\/j.eswa.2015.06.046","article-title":"A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines","volume":"42","author":"Khalaf","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neucom.2011.10.045","article-title":"ECG arrhythmia classification using a probabilistic neural network with a feature reduction method","volume":"116","author":"Wang","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eswa.2018.12.037","article-title":"A robust deep convolutional neural network with batch-weighted loss for heartbeat classification","volume":"122","author":"Sellami","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s12938-018-0441-4","article-title":"Automatic QRS complex detection using two-level convolutional neural network","volume":"17","author":"Xiang","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Avanzato, R., and Beritelli, F. (2020). An innovative acoustic rain gauge based on convolutional neural networks. Information, 11.","DOI":"10.3390\/info11040183"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, Q. (2018, January 19\u201321). Convolutional neural networks. Proceedings of the 3rd International Conference on Electromechanical Control Technology and Transportation, Chongqing, China.","DOI":"10.5220\/0006972204340439"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Graves, A., and Schmidhuber, J. (August, January 31). Framewise phoneme classification with bidirectional LSTM networks. Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada.","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Li, X., and Wu, X. (2016). Long short-term memory based convolutional recurrent neural networks for large vocabulary speech recognition. arXiv.","DOI":"10.21437\/Interspeech.2015-648"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"15965","DOI":"10.1007\/s00521-019-04650-7","article-title":"Brain tumor detection: A long short-term memory (LSTM)-based learning model","volume":"32","author":"Amin","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_58","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tanabe, K., Ikeda, M., Hayashi, M., Matsuo, K., Yasaka, M., Machida, H., Shida, M., Katahira, T., Imanishi, T., and Hirasawa, T. (2020). Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer. Cancers, 12.","DOI":"10.3390\/cancers12092373"},{"key":"ref_60","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_61","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","unstructured":"Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Chen, Z., Hu, F., Zhu, J., Tang, Q., and Liang, Y. (2020). An automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology. Electronics, 9.","DOI":"10.3390\/electronics9010121"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"105607","DOI":"10.1016\/j.cmpb.2020.105607","article-title":"Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network","volume":"196","author":"Atal","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"115031","DOI":"10.1016\/j.eswa.2021.115031","article-title":"Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals","volume":"181","author":"Faust","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"102843","DOI":"10.1016\/j.bspc.2021.102843","article-title":"Automated arrhythmia classification using depthwise separable convolutional neural network with focal loss","volume":"69","author":"Lu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.neucom.2021.04.104","article-title":"Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation","volume":"454","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"102262","DOI":"10.1016\/j.bspc.2020.102262","article-title":"ECG arrhythmia classification by using a recurrence plot and convolutional neural network","volume":"64","author":"Mathunjwa","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jelectrocard.2021.04.016","article-title":"12-Lead ECG arrhythmia classification using cascaded convolutional neural network and expert feature","volume":"67","author":"Yang","year":"2021","journal-title":"J. Electrocardiol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.bbe.2021.04.004","article-title":"An improved cardiac arrhythmia classification using an RR interval-based approach","volume":"41","author":"Rahul","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Chouhan, K.S., Gajrani, J., Sharma, B., and Tazi, S.N. (2022). Arrhythmia Classification Using Deep Learning Architecture. Real-Time Applications of Machine Learning in Cyber-Physical Systems, IGI Global.","DOI":"10.4018\/978-1-7998-9308-0.ch010"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/9\/4\/152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:12Z","timestamp":1760136552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/9\/4\/152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,2]]},"references-count":72,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["bioengineering9040152"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering9040152","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,2]]}}}