{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:29:21Z","timestamp":1792463361899,"version":"4.5.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,5,4]],"date-time":"2018-05-04T00:00:00Z","timestamp":1525392000000},"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>The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.<\/jats:p>","DOI":"10.3390\/bioengineering5020035","type":"journal-article","created":{"date-parts":[[2018,5,7]],"date-time":"2018-05-07T03:12:21Z","timestamp":1525662741000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks"],"prefix":"10.3390","volume":"5","author":[{"given":"Shalin","family":"Savalia","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, St. Mary\u2019s University, 1 Camino Santa Maria, San Antonio, TX 78228, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vahid","family":"Emamian","sequence":"additional","affiliation":[{"name":"School of Science, Engineering and Technology, St. Mary\u2019s University, San Antonio, TX 78228, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,4]]},"reference":[{"key":"ref_1","unstructured":"Artis, S.G., Mark, R.G., and Moody, G.B. (1991, January 23\u201326). Detection of atrial fibrillation using artificial neural networks. Proceedings of the Computers in Cardiology, Venice, Italy."},{"key":"ref_2","unstructured":"Melo, S.L., Caloba, L.P., and Nadal, J. (2000, January 24\u201327). Arrhythmia analysis using artificial neural network and decimated electrocardiographic data. Proceedings of the IEEE Conference on Computers in Cardiology, Cambridge, MA, USA."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Salam, A.K., and Srilakshmi, G. (2015, January 5\u20137). An algorithm for ECG analysis of arrhythmia detection. Proceedings of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India.","DOI":"10.1109\/ICECCT.2015.7226130"},{"key":"ref_5","first-page":"5","article-title":"Model of differentiation between normal and abnormal heart sounds in using the discrete wavelet transform","volume":"3","author":"Debbal","year":"2014","journal-title":"J. Med. Bioeng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Perez, R.R., Marques, A., and Mohammadi, F. (2016, January 15\u201318). The application of supervised learning through feed-forward neural networks for ECG signal classification. Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, BC, Canada.","DOI":"10.1109\/CCECE.2016.7726762"},{"key":"ref_7","unstructured":"Palreddy, S., Tompkins, W.J., and Hu, Y.H. (1995, January 20\u201323). Customization of ECG beat classifiers developed using SOM and LVQ. Proceedings of the IEEE 17th Annual Conference on Engineering in Medicine and Biology Society, Montreal, QC, Canada."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Elsayad, A.M. (2009, January 14\u201316). Classification of ECG arrhythmia using learning vector quantization neural networks. Proceedings of the 2009 International Conference on Computer Engineering & Systems, Cairo, Egypt.","DOI":"10.1109\/ICCES.2009.5383295"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gautam, M.K., and Giri, V.K. (2016, January 17\u201318). A neural network approach and wavelet analysis for ECG classification. Proceedings of the 2016 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India.","DOI":"10.1109\/ICETECH.2016.7569428"},{"key":"ref_10","first-page":"679","article-title":"A new generalized regression artificial neural networks approach for diagnosing heart disease","volume":"4","author":"Zebardast","year":"2013","journal-title":"Int. J. Innov. Appl. Stud."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.knosys.2017.06.003","article-title":"Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network","volume":"132","author":"Acharya","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nilanon, T., Yao, J., Hao, J., Purushotam, S., and Liu, Y. (2016, January 11\u201314). Normal\/abnormal heart recordings classification by using convolutional neural network. Proceedings of the IEEE Conference on Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.169-535"},{"key":"ref_13","unstructured":"Jigar, A.D. (2016, January 21\u201322). Swift single image super resolution using deep convolution neural network. Proceedings of the International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India."},{"key":"ref_14","unstructured":"Fernando, A., Oliver, C., Marco, A.F.P., Adam, M., and de Maarten, V. (2017, January 24\u201327). Comparing feature based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG. Proceedings of the Conference on Computing in Cardiology (CinC), Rennes, France."},{"key":"ref_15","unstructured":"Shadi, G., Mostafa, A., Nasimalsadat, M., Kamran, K., and Ali, G. (2017, January 24\u201327). Atrial fibrillation detection using feature based algorithm and deep conventional neural network. Proceedings of the Conference on Computing in Cardiology (CinC), Rennes, France."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.cger.2012.07.003","article-title":"Epidemiology of arrhythmias and conduction disorders in older adults","volume":"28","author":"Chow","year":"2012","journal-title":"Clin. Geriatr. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., Fujita, H., Adam, M., Oh, S.L., Tan, J.H., Sudarshan, V.K., and Koh, J.E.W. (2016, January 9\u201312). Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844294"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Desai, U., Martis, R.J., Acharya, U.R., Nayak, C.G., Seshikala, G., and Shettyk, R. (2016). Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers. J. Mech. Med. Biol., 16.","DOI":"10.1142\/S0219519416400054"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zubair, M., Kim, J., and Yoon, C.W. (2016, January 26\u201329). An automated ECG beat classification system using convolutional neural networks. Proceedings of the 6th International Conference on IT Convergence and Security (ICITCS), Prague, Czech Republic.","DOI":"10.1109\/ICITCS.2016.7740310"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2015, January 12). Is object localization for free?\u2014Weakly-supervised learning with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298668"},{"key":"ref_21","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing Systems 2012, Lake Tahoe, NV, USA."},{"key":"ref_22","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 network","volume":"63","author":"Kiranyaz","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TITB.2010.2094197","article-title":"Diagnosis of cardiovascular abnormalities from compressed ECG: A datamining-based approach","volume":"15","author":"Fahim","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ciresan, D.C., Meier, U., Gambardella, L.M., and Schumacher, J. (2011, January 18\u201321). Convolutional neural network committees for handwritten character classification. Proceedings of the 2011 International Conference on Document Analysis and Recognition (ICDAR), Beijing, China.","DOI":"10.1109\/ICDAR.2011.229"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Mohamed, A.-R., Kingsbury, B., and Ramabhadran, B. (2013, January 26\u201331). Deep convolutional neural networks for lvcsr. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639347"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/TMI.2016.2551324","article-title":"q-Space deep learning: Twelve-fold shorter and model-free diffusion MRI scans","volume":"35","author":"Golkov","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.bspc.2014.04.001","article-title":"Computer-aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation","volume":"13","author":"Martis","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.bspc.2013.08.008","article-title":"Application of higher order statistics for atrial arrhythmia classification","volume":"8","author":"Martis","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/10.942588","article-title":"A short-time multifractal approach for arrhythmia detection based on fuzzy neural network","volume":"48","author":"Wang","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/TMI.2016.2524985","article-title":"Multi-instance deep learning: Discover discriminative local anatomies for body part recognition","volume":"35","author":"Yan","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","unstructured":"Najarian, K., and Splinter, R. (2012). Biomedical Signal and Image Processing, CRC Press, Taylor and Francis Group. [2nd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 8\u201316). Identity mappings in deep residual networks. Proceedings of the 14th European Conference on Computer Vision, Cham, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Clifford, G.D., Liu, C.Y., Moody, B., Lehman, L., Silva, I., Li, Q., Johnson, A.E.W., and Mark, R.G. (2017, January 24\u201327). AF classification from a short single lead ECG recording: The PhysioNet computing in cardiology challenge 2017. Proceedings of the Conference on Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.065-469"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min Knowl Discov."},{"key":"ref_35","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/5\/2\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:14Z","timestamp":1760194994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/5\/2\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,4]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["bioengineering5020035"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering5020035","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,4]]}}}