{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:45:00Z","timestamp":1792597500083,"version":"4.5.0"},"reference-count":26,"publisher":"MyJove Corporation","issue":"221","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JoVE"],"DOI":"10.3791\/68227","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T19:02:54Z","timestamp":1751569374000},"source":"Crossref","is-referenced-by-count":4,"title":["Convolutional Neural Network-Transformer Model to Predict and Classify Early Arrhythmia Using Electrocardiogram Signal"],"prefix":"10.3791","author":[{"given":"B. N.","family":"Manjesh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. N.","family":"Raja Praveen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azadeh","family":"Amoozegar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2098","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"issue":"3","key":"ref1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/jimaging8030070","type":"journal-article","article-title":"Novel deep-learning-based framework for the classification of cardiac arrhythmia","volume":"9","author":"Jamil","year":"2020","unstructured":"Jamil, S., Rahman, M. A. Novel deep-learning-based framework for the classification of cardiac arrhythmia. J Imaging. 9 (3), 70 (2020).","journal-title":"J Imaging"},{"issue":"8","key":"ref2","doi-asserted-by":"crossref","first-page":"6337","DOI":"10.3390\/su15086337","type":"journal-article","article-title":"Blockchain-based framework for interoperable electronic health records for an improved healthcare system","volume":"15","author":"Reegu","year":"2023","unstructured":"Reegu, F. A. et al. Blockchain-based framework for interoperable electronic health records for an improved healthcare system. Sustainability. 15 (8), 6337 (2023).","journal-title":"Sustainability"},{"issue":"6","key":"ref3","doi-asserted-by":"crossref","first-page":"82","DOI":"10.3390\/computers10060082","type":"journal-article","article-title":"Uncertainty-aware deep learning-based cardiac arrhythmias classification model of electrocardiogram signals","volume":"11","author":"Aseeri","year":"2021","unstructured":"Aseeri, A. O. Uncertainty-aware deep learning-based cardiac arrhythmias classification model of electrocardiogram signals. Computers. 11 (6), 82 (2021).","journal-title":"Computers"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"111842","DOI":"10.1109\/ACCESS.2022.3215665","type":"journal-article","article-title":"Lightweight ShuffleNet-based CNN for arrhythmia classification","volume":"12","author":"Tesfai","year":"2022","unstructured":"Tesfai, H. et al. Lightweight ShuffleNet-based CNN for arrhythmia classification. IEEE Access. 12, 111842-111854 (2022).","journal-title":"IEEE Access"},{"issue":"4","key":"ref5","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1007\/s13246-019-00815-9","type":"journal-article","article-title":"Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE","volume":"42","author":"Pandey","year":"2019","unstructured":"Pandey, S. K., Janghel, R. R. Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE. Australas Phys Eng Sci Med. 42 (4), 1129-1139 (2019).","journal-title":"Australas Phys Eng Sci Med"},{"key":"ref6","first-page":"325","type":"journal-article","article-title":"Transformer-based deep neural network for arrhythmia detection using continuous ECG signals","volume":"105","author":"Hu","year":"2022","unstructured":"Hu, R., Chen, J., Zhou, L. Transformer-based deep neural network for arrhythmia detection using continuous ECG signals. Comput Biol Med. 105, 325 (2022).","journal-title":"Comput Biol Med"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"75577","DOI":"10.1109\/ACCESS.2019.2918792","type":"journal-article","article-title":"Novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals","volume":"7","author":"Dang","year":"2019","unstructured":"Dang, H. et al. Novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals. IEEE Access. 7, 75577-75590 (2019).","journal-title":"IEEE Access"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"66132","DOI":"10.1109\/ACCESS.2021.3076683","type":"journal-article","article-title":"Arrhythmia classification using biased dropout and morphology-rhythm feature with incremental broad learning","volume":"9","author":"Li","year":"2021","unstructured":"Li, J., Zhang, Y., Gao, L., Li, X. Arrhythmia classification using biased dropout and morphology-rhythm feature with incremental broad learning. IEEE Access. 9, 66132-66140 (2021).","journal-title":"IEEE Access"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"050006","DOI":"10.1063\/5.0161558","type":"journal-article","article-title":"Heart disease prediction system using SMOTE-balanced dataset and decision classifier","volume":"2834","author":"Joddoa","year":"2023","unstructured":"Joddoa, A. S. Heart disease prediction system using SMOTE-balanced dataset and decision classifier. AIP Conf. Proc. 2834, 050006 (2023).","journal-title":"AIP Conf. Proc"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"106582","DOI":"10.1016\/j.cmpb.2021.106582","type":"journal-article","article-title":"Inter-patient arrhythmia classification with improved deep residual convolutional neural network","volume":"214","author":"Li","year":"2022","unstructured":"Li, Y., Qian, R., Li, K. Inter-patient arrhythmia classification with improved deep residual convolutional neural network. Comput Methods Programs Biomed. 214, 106582 (2022).","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"ref11","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","type":"journal-article","article-title":"Real-time patient-specific ECG classification by 1-D convolutional neural networks","volume":"63","author":"Kiranyaz","year":"2016","unstructured":"Kiranyaz, S., Ince, T., Gabbouj, M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng. 63 (3), 664-675 (2016).","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref12","article-title":"Attention is all you need","author":"Vaswani","year":"2017","unstructured":"Vaswani, A. et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA, 6000-6010 (2017)."},{"key":"ref13","type":"book","article-title":"Cardiologist-level arrhythmia detection with convolutional neural networks","volume-title":"arXiv","author":"Rajpurkar","year":"2017","unstructured":"Rajpurkar, P. et al. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv. preprint arXiv:1707.01836 (2017)."},{"key":"ref14","type":"book","article-title":"Convolutional recurrent neural networks for electrocardiogram classification","volume-title":"arXiv","author":"Zihlmann","year":"2018","unstructured":"Zihlmann, M., Perekrestenko, D., Tschannen, M. Convolutional recurrent neural networks for electrocardiogram classification. arXiv. preprint arXiv:1710.06122 (2018)."},{"key":"ref15","doi-asserted-by":"crossref","first-page":"7817","DOI":"10.1038\/s41598-025-92582-9","type":"journal-article","article-title":"Novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using Stockwell transform","volume":"15","author":"Kim","year":"2025","unstructured":"Kim, D., Lee, K. Novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using Stockwell transform. Sci Rep. 15, 7817 (2025).","journal-title":"Sci Rep"},{"key":"ref16","first-page":"102679","type":"journal-article","article-title":"Transformer-based attention model for arrhythmia detection using ECG signals","volume":"68","author":"Diker","year":"2021","unstructured":"Diker, A., Aydin, K. Transformer-based attention model for arrhythmia detection using ECG signals. Biomed Signal Process Control. 68, 102679 (2021).","journal-title":"Biomed Signal Process Control"},{"key":"ref17","article-title":"Convolutional neural network hyperparameter tuning with Adam optimizer for ECG classification","author":"Sen","year":"2020","unstructured":"Sen, S. Y., Ozkurt, N. Convolutional neural network hyperparameter tuning with Adam optimizer for ECG classification. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU). 50717 (2020)."},{"key":"ref18","first-page":"321","type":"journal-article","article-title":"Synthetic minority over-sampling technique for handling class imbalance","volume":"6","author":"Smote","year":"2002","unstructured":"Smote, N. V. C. Synthetic minority over-sampling technique for handling class imbalance. J Artif Intell Res. 6, 321-357 (2002).","journal-title":"J Artif Intell Res"},{"key":"ref19","first-page":"103345","type":"journal-article","article-title":"Detecting atrial fibrillation by deep convolutional neural networks","volume":"116","author":"Xia","year":"2020","unstructured":"Xia, Y., Wulan, N., Wang, K., Zhang, H. Detecting atrial fibrillation by deep convolutional neural networks. Comput Biol Med. 116, 103345 (2020).","journal-title":"Comput Biol Med"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"100502","DOI":"10.1016\/j.sbsr.2022.100502","type":"journal-article","article-title":"Electrocardiogram-based arrhythmia classification using wavelet transform with deep learning model","volume":"37","author":"Mohonta","year":"2022","unstructured":"Mohonta, S. C., Motin, M. A., Kumar, D. K. Electrocardiogram-based arrhythmia classification using wavelet transform with deep learning model. Sensing Bio-Sensing Res. 37, 100502 (2022).","journal-title":"Sensing Bio-Sensing Res"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.compbiomed.2018.06.002","type":"journal-article","article-title":"Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable-length heart beats","volume":"102","author":"Oh","year":"2018","unstructured":"Oh, S. L., Ng, E. Y. K., Tan, R. S., Acharya, U. R. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable-length heart beats. Comput Biol Med. 102, 278-287 (2018).","journal-title":"Comput Biol Med"},{"key":"ref22","first-page":"1","type":"journal-article","article-title":"Cardiac arrhythmia detection from 2D ECG images by using deep learning technique","author":"Izci","year":"2019","unstructured":"Izci, E., Ozdemir, M. A., Degirmenci, M., Akan, A. Cardiac arrhythmia detection from 2D ECG images by using deep learning technique. In 2019 Medical Technologies Congress (TIPTEKNO)., 1-4 (2019).","journal-title":"2019 Medical Technologies Congress (TIPTEKNO)"},{"issue":"1","key":"ref23","doi-asserted-by":"crossref","first-page":"121","DOI":"10.3390\/electronics9010121","type":"journal-article","article-title":"Automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology","volume":"9","author":"Zheng","year":"2020","unstructured":"Zheng, Z., Chen, Z., Hu, F., Zhu, J., Tang, Q., Liang, Y. Automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology. Electronics. 9 (1), 121 (2020).","journal-title":"Electronics"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.procs.2017.11.238","type":"journal-article","article-title":"Cardiac arrhythmia detection using deep learning","volume":"120","author":"Isin","year":"2017","unstructured":"Isin, A., Ozdalili, S. Cardiac arrhythmia detection using deep learning. Procedia Comput Sci. 120, 268-275 (2017).","journal-title":"Procedia Comput Sci"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"92871","DOI":"10.1109\/ACCESS.2019.2928017","type":"journal-article","article-title":"ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network","volume":"7","author":"Huang","year":"2019","unstructured":"Huang, J., Chen, B., Yao, B., He, W. ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access. 7, 92871-92880 (2019).","journal-title":"IEEE Access"},{"issue":"1","key":"ref26","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/e23010119","type":"journal-article","article-title":"Automatic ECG classification using continuous wavelet transform and convolutional neural network","volume":"23","author":"Wang","year":"2021","unstructured":"Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., Ou, C. Automatic ECG classification using continuous wavelet transform and convolutional neural network. Entropy. 23 (1), 119 (2021).","journal-title":"Entropy"}],"container-title":["Journal of Visualized Experiments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/app.jove.com\/t\/68227\/convolutional-neural-network-transformer-model-to-predict-classify","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:51:53Z","timestamp":1792594313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jove.com\/t\/68227\/convolutional-neural-network-transformer-model-to-predict-classify"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":26,"journal-issue":{"issue":"221","published-online":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.3791\/68227","relation":{},"ISSN":["1940-087X"],"issn-type":[{"value":"1940-087X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,3]]}}}