{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:48:55Z","timestamp":1781369335002,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T00:00:00Z","timestamp":1605052800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T00:00:00Z","timestamp":1605052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100011151","name":"Key Laboratory of Computer Network and Information Integration","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100011151","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shaanxi Provincial Natural Science Foundation of China","award":["2016JM8034"],"award-info":[{"award-number":["2016JM8034"]}]},{"name":"Shaanxi Provincial Natural Science Foundation of China","award":["2018GY-135"],"award-info":[{"award-number":["2018GY-135"]}]},{"name":"Shaanxi Provincial Natural Science Foundation of China","award":["2018ZDXM-GY-091"],"award-info":[{"award-number":["2018ZDXM-GY-091"]}]},{"name":"Shaanxi Provincial Natural Science Foundation of China","award":["2020SF377"],"award-info":[{"award-number":["2020SF377"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00530-020-00713-1","type":"journal-article","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T04:30:37Z","timestamp":1605069037000},"page":"1387-1399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A novel method for ECG signal classification via one-dimensional convolutional neural network"],"prefix":"10.1007","volume":"28","author":[{"given":"Xuan","family":"Hua","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungang","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haipeng","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojie","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7266-8534","authenticated-orcid":false,"given":"Jinshan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"12","key":"713_CR1","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1109\/TSMC.2017.2705582","volume":"48","author":"B Pourbabaee","year":"2017","unstructured":"Pourbabaee, B., Roshtkhari, M.J., Khorasani, K.: Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans. Syst. 48(12), 2095\u20132104 (2017). https:\/\/doi.org\/10.1109\/TSMC.2017.2705582","journal-title":"IEEE Trans. Syst."},{"key":"713_CR2","doi-asserted-by":"publisher","unstructured":"Zubair, M., Kim, J., Yoon, C.: An automated ECG beat classification system using convolutional neural networks. IEEE ICITCS (2016). https:\/\/doi.org\/10.1109\/ICITCS.2016.7740310","DOI":"10.1109\/ICITCS.2016.7740310"},{"issue":"3","key":"713_CR3","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S 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\u2013675 (2016). https:\/\/doi.org\/10.1109\/TBME.2015.2468589","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"713_CR4","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/10.740880","volume":"46","author":"K Minami","year":"1999","unstructured":"Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46(2), 179\u2013185 (1999). https:\/\/doi.org\/10.1109\/10.740880","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"713_CR5","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.bspc.2012.10.005","volume":"8","author":"M Javadi","year":"2013","unstructured":"Javadi, M., Arani, S.A.A.A., Sajedin, A., Ebrahimpour, R.: Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed. Signal Process. Control 8(3), 289\u2013296 (2013). https:\/\/doi.org\/10.1016\/j.bspc.2012.10.005","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"713_CR6","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.compbiomed.2005.01.006","volume":"36","author":"Y \u00d6zbay","year":"2006","unstructured":"\u00d6zbay, Y., Ceylan, R., Karlik, B.: A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Comput. Biol. Med 36(4), 376\u2013388 (2006). https:\/\/doi.org\/10.1016\/j.compbiomed.2005.01.006","journal-title":"Comput. Biol. Med"},{"issue":"11","key":"713_CR7","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1109\/78.650102","volume":"45","author":"B Scholkopf","year":"1997","unstructured":"Scholkopf, B., Sung, K.-K., Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11), 2758\u20132765 (1997). https:\/\/doi.org\/10.1109\/78.650102","journal-title":"IEEE Trans. Signal Process."},{"issue":"10","key":"713_CR8","doi-asserted-by":"publisher","first-page":"3165","DOI":"10.1016\/j.asoc.2012.06.004","volume":"12","author":"P-C Chang","year":"2012","unstructured":"Chang, P.-C., Lin, J.-J., Hsieh, J.-C., Weng, J.: Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl. Soft Comput. 12(10), 3165\u20133175 (2012). https:\/\/doi.org\/10.1016\/j.asoc.2012.06.004","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"713_CR9","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/10.58593","volume":"37","author":"DA Coast","year":"1990","unstructured":"Coast, D.A., Stern, R.M., Cano, G.G., Briller, S.A.: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng. 37(9), 826\u2013836 (1990). https:\/\/doi.org\/10.1109\/10.58593","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"713_CR10","doi-asserted-by":"publisher","unstructured":"Pan, S.-T., Hong, T.-P., Chen, H.-C.: ECG signal analysis by using hidden Markov model, In: 2012 IEEE\/ International Conference on Fuzzy Theory and Its Applications (iFUZZY2012) (2012). https:\/\/doi.org\/10.1109\/iFUZZY.2012.6409718","DOI":"10.1109\/iFUZZY.2012.6409718"},{"key":"713_CR11","doi-asserted-by":"crossref","unstructured":"Pyakillya, B., Kazachenko, N., Mikhailovsky, N.: Deep learning for ECG classification, IOP Publishing PhysicsWeb (2017).https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/913\/1\/012004\/meta","DOI":"10.1088\/1742-6596\/913\/1\/012004"},{"key":"713_CR12","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.ins.2016.01.082","volume":"345","author":"MMA Rahhal","year":"2016","unstructured":"Rahhal, M.M.A., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., Yager, R.R.: Deep learning approach for active classification of electrocardiogram signals. Inform. Sci. 345, 340\u2013354 (2016). https:\/\/doi.org\/10.1016\/j.ins.2016.01.082","journal-title":"Inform. Sci."},{"key":"713_CR13","doi-asserted-by":"publisher","unstructured":"Meng, H. H., Yue, Z.: Classification of electrocardiogram signals with deep belief networks, 2014 IEEE 17th International Conference on Computational Science and Engineering (2014). https:\/\/doi.org\/10.1109\/CSE.2014.36","DOI":"10.1109\/CSE.2014.36"},{"key":"713_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/4108720","volume":"2017","author":"K Luo","year":"2017","unstructured":"Luo, K., Li, J., Wang, Z., Cuschieri, A.: Patient-specific deep architectural model for ECG classification. J. Healthcare Eng. 2017, 1\u201313 (2017). https:\/\/doi.org\/10.1155\/2017\/4108720","journal-title":"J. Healthcare Eng."},{"key":"713_CR15","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1007\/s40846-018-0389-7","volume":"38","author":"MMA Rahhal","year":"2018","unstructured":"Rahhal, M.M.A., Bazi, Y., Zuair, M.A., Othman, E., BenJdira, B.: Convolutional neural networks for electrocardiogram classification. J. Med. Biol. Eng. 38, 1014\u20131025 (2018). https:\/\/doi.org\/10.1007\/s40846-018-0389-7","journal-title":"J. Med. Biol. Eng."},{"issue":"6","key":"713_CR16","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TBME.2016.2607020","volume":"64","author":"N Karimian","year":"2017","unstructured":"Karimian, N., Guo, Z., Tehranipoor, M., Forte, D.: Highly reliable key generation From electrocardiogram (ECG). IEEE Trans. Biomed. Eng. 64(6), 1400\u20131411 (2017). https:\/\/doi.org\/10.1109\/TBME.2016.2607020","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"713_CR17","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1109\/TBME.2012.2191407","volume":"59","author":"AS Alvarado","year":"2012","unstructured":"Alvarado, A.S., Lakshminarayan, C., Principe, J.C.: Time-based compression and classification of heartbeats. IEEE Trans. Biomed. Eng. 59(6), 1641\u20131648 (2012). https:\/\/doi.org\/10.1109\/TBME.2012.2191407","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"9","key":"713_CR18","first-page":"588","volume":"6","author":"F Yasmeen","year":"2017","unstructured":"Yasmeen, F., Mallick, M.A., Khan, Y.U.: A review on analysis of electrocardiogram signal (MIT-BIH Arrythmia Database). Int. J. Electron. Electr. Comput. Syst. 6(9), 588\u2013591 (2017)","journal-title":"Int. J. Electron. Electr. Comput. Syst."},{"issue":"3","key":"713_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45\u201350 (2001). https:\/\/doi.org\/10.1109\/51.932724","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"713_CR20","doi-asserted-by":"publisher","unstructured":"Apandi, Z. F.M., Ikeura, R., Hayakawa, S.: Arrhythmia Detection Using MIT-BIH Dataset: A Review, 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) (2018).https:\/\/doi.org\/10.1109\/ICASSDA.2018.8477620","DOI":"10.1109\/ICASSDA.2018.8477620"},{"key":"713_CR21","doi-asserted-by":"publisher","unstructured":"Moody, G.B., Mark, R.G.: The MIT-BIH arrhythmia database on CD-ROM and software for use with it. Proceedings Computers in Cardiology (1990). https:\/\/doi.org\/10.1109\/CIC.1990.144205","DOI":"10.1109\/CIC.1990.144205"},{"issue":"7","key":"713_CR22","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","volume":"51","author":"PD Chazal","year":"2004","unstructured":"Chazal, P.D., O\u2019Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196\u20131206 (2004). https:\/\/doi.org\/10.1109\/TBME.2004.827359","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"713_CR23","doi-asserted-by":"publisher","unstructured":"Carrillo-Alarc\u00f3n, J. C., Morales-Rosales, L. A., Rodr$$\\acute{i}$$guez-R$$\\acute{a}$$ngel, H., Lobato-B$$\\acute{a}$$ez, M., Mu$${\\tilde{n}}$$oz, A., Algredo-Badillo, I.: A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data, Sensors 2020, 20(11), 3139 (2020). https:\/\/doi.org\/10.3390\/s20113139","DOI":"10.3390\/s20113139"},{"key":"713_CR24","doi-asserted-by":"publisher","unstructured":"M$$\\acute{a}$$rquez, D. G., Otero, A., Garc\u00eda, C. A., Presedo, J.: A study on the representation of QRS complexes with the optimum number of Hermite functions, Biomed. Signal Process. Control, 22, 11-18 (2015).https:\/\/doi.org\/10.1016\/j.bspc.2015.06.006","DOI":"10.1016\/j.bspc.2015.06.006"},{"issue":"1","key":"713_CR25","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/10.362922","volume":"42","author":"C Li","year":"1995","unstructured":"Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points using wavelet transform. IEEE Trans. Biomed. Eng. 42(1), 21\u20132 (1995). https:\/\/doi.org\/10.1109\/10.362922","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"713_CR26","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TBME.1985.325532","volume":"32","author":"J Pan","year":"1985","unstructured":"Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME- 32(3), 230\u2013236 (1985). https:\/\/doi.org\/10.1109\/TBME.1985.325532","journal-title":"IEEE Trans. Biomed. Eng. BME-"},{"key":"713_CR27","doi-asserted-by":"publisher","unstructured":"Jiang, J., Zhang, H., Pi, D., Dai, C.: A novel multi-module neural network system for imbalanced heartbeats classification. Expert Systems with Applications: X 1, (2019). https:\/\/doi.org\/10.1016\/j.eswax.2019.100003","DOI":"10.1016\/j.eswax.2019.100003"},{"key":"713_CR28","unstructured":"Sahoo, J. P.: Analysis of ECG signal for Detection of Cardiac Arrhythmias, Department of Electronics and Communication Engineering National Institute Of Technology. Roll No: 209EC117 (2011)"},{"key":"713_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/7354081","volume":"2018","author":"J Li","year":"2018","unstructured":"Li, J., Si, Y., Xu, T., Jiang, S.: Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques. Math. Prob. Eng. 2018, 1\u201310 (2018). https:\/\/doi.org\/10.1155\/2018\/7354081","journal-title":"Math. Prob. Eng."},{"key":"713_CR30","unstructured":"Drummond, C., Holte, R. C.: C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling, Workshop on learning from imbalanced datasets II, 11, 1-8 (2003)"},{"issue":"7","key":"713_CR31","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/TBME.2017.2756869","volume":"65","author":"T Yang","year":"2018","unstructured":"Yang, T., Yu, L., Jin, Q., Wu, L., He, B.: Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ECG. IEEE Trans. Biomed. Eng. 65(7), 1662\u20131671 (2018). https:\/\/doi.org\/10.1109\/TBME.2017.2756869","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"8","key":"713_CR32","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1109\/TIM.2017.2674738","volume":"66","author":"X Ding","year":"2017","unstructured":"Ding, X., He, Q.: Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans. Instrumentation Measure. 66(8), 1926\u20131935 (2017). https:\/\/doi.org\/10.1109\/TIM.2017.2674738","journal-title":"IEEE Trans. Instrumentation Measure."},{"key":"713_CR33","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","volume":"377","author":"O Janssens","year":"2016","unstructured":"Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Walle, R.V., Hoecke, S.V.: Convolutional neural network based fault detection for rotating machinery. J. Sound Vib. 377, 331\u2013345 (2016). https:\/\/doi.org\/10.1016\/j.jsv.2016.05.027","journal-title":"J. Sound Vib."},{"key":"713_CR34","unstructured":"Kingma, D. P., Ba, J.: Adam: A method for stochastic optimization, Published as a conference paper at the 3rd International Conference for Learning Representations (2014). arXiv: org\/abs\/1412.6980"},{"key":"713_CR35","doi-asserted-by":"publisher","first-page":"34060","DOI":"10.1109\/ACCESS.2019.2900719","volume":"7","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., Li, J.: Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolution neural networks. IEEE Access 7, 34060\u201334067 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2900719","journal-title":"IEEE Access"},{"key":"713_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-19721-5","volume-title":"Data Mining: Concepts, Models and Techniques","author":"F Gorunescu","year":"2011","unstructured":"Gorunescu, F.: Data Mining: Concepts, Models and Techniques. Springer, Berlin (2011)"},{"key":"713_CR37","doi-asserted-by":"publisher","unstructured":"Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves, ICML \u201906: Proceedings of the 23rd international conference on Machine learning, 233-240 (2006). https:\/\/doi.org\/10.1145\/1143844.1143874","DOI":"10.1145\/1143844.1143874"},{"key":"713_CR38","doi-asserted-by":"publisher","unstructured":"Ali, A.-R. A., Deserno, T. M.: A Systematic Review of Automated Melanoma Detection in Dermatoscopic Images and its Ground Truth Data, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83181I (2012). https:\/\/doi.org\/10.1117\/12.912389","DOI":"10.1117\/12.912389"},{"key":"713_CR39","doi-asserted-by":"publisher","unstructured":"Li, P., Chan, K. L., Fu, S., Krishnan, S. M.: Kernel Machines for Imbalanced Data Problem in Biomedical Applications. In: Ma Y., Guo G. (eds) Support Vector Machines Applications, Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-02300-7_7","DOI":"10.1007\/978-3-319-02300-7_7"},{"key":"713_CR40","unstructured":"Abrishami, H., Campbell, M., Han, C., Czosek, R., Zhou, X.: Semantic ECG Interval Segmentation Using Autoencoders, Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2019)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-020-00713-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-020-00713-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-020-00713-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T21:24:45Z","timestamp":1659043485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-020-00713-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,11]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["713"],"URL":"https:\/\/doi.org\/10.1007\/s00530-020-00713-1","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,11]]},"assertion":[{"value":"8 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The ECG data used to support the findings of this study have been deposited in the MIT-BIH repository .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}}]}}