Hongo RH, Goldschlager N. Status of computerized electrocardiography. Cardiol Clin. 2006;24:491–504. https://doi.org/10.1016/j.ccl.2006.03.005.
Hughes JW, Olgin JE, Avram R, Abreau SA, Sittler T, Radia K, Hsia H, Walters T, Lee B, Gonzalez JE, Tison GH. Performance of a convolutional neural network and explainability technique for 12-lead electrocardiogram interpretation. JAMA Cardiol. 2021;6:1285. https://doi.org/10.1001/jamacardio.2021.2746.
Zhu H, Cheng C, Yin H, Li X, Zuo P, Ding J, Lin F, Wang J, Zhou B, Li Y, Hu S, Xiong Y, Wang B, Wan G, Yang X, Yuan Y. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. Lancet Digit Heal. 2020;2:e348–57. https://doi.org/10.1016/S2589-7500(20)30107-2.
Choi HY, Kim W, Kang GH, Jang YS, Lee Y, Kim JG, Lee N, Shin DG, Bae W, Song Y. Diagnostic accuracy of the deep learning model for the detection of ST elevation myocardial infarction on electrocardiogram. J Pers Med. 2022;12:336. https://doi.org/10.3390/jpm12030336.
Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol. 2019;4:428. https://doi.org/10.1001/jamacardio.2019.0640.
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med. 2019;25:70–4. https://doi.org/10.1038/s41591-018-0240-2.
Lee E, Ito S, Miranda WR, Lopez-Jimenez F, Kane GC, Asirvatham SJ, Noseworthy PA, Friedman PA, Carter RE, Borlaug BA, Attia ZI, Oh JK. Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure. Npj Digit Med. 2024;7:4. https://doi.org/10.1038/s41746-023-00993-7.
Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, Hahn RT, Tison G, Abreau SA, Barrios J, Torres JN, Hughes JW, Perez MV, Finer J, Kodali S, Khalique O, Hamid N, Schwartz A, Homma S, Kumaraiah D, Cohen DJ, Maurer MS, Einstein AJ, Nazif T, Leon MB, Perotte AJ. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol. 2022;80:613–26. https://doi.org/10.1016/j.jacc.2022.05.029.
Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, Bouzid Z, Faramand Z, Alrawashdeh MO, Gregg RE, Helman S, Riek NT, Kraevsky-Phillips K, Clermont G, Akcakaya M, Sereika SM, Van Dam P, Smith SW, Birnbaum Y, Saba S, Sejdic E, Callaway CW. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med. 2023;29:1804–13. https://doi.org/10.1038/s41591-023-02396-3.
Zheng J, Zhang J, Danioko S, Yao H, Guo H, Rakovski C. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Sci Data. 2020;7:48. https://doi.org/10.1038/s41597-020-0386-x.
Wagner P, Strodthoff N, Bousseljot R-D, Kreiseler D, Lunze FI, Samek W, Schaeffter T. PTB-XL, a large publicly available electrocardiography dataset. Sci Data. 2020;7:154. https://doi.org/10.1038/s41597-020-0495-6.
Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, Liu Y, Ma C, Wei S, He Z, Li J, Yin Kwee EN. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imaging Heal Inf. 2018;8:1368–73. https://doi.org/10.1166/jmihi.2018.2442.
Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. Int Conf Mach Learn (ICML). 2020;PartF16814:1575–85.
Luo D, Cheng W, Ni J, Yu W, Zhang X, Zong B, Liu Y, Chen Z, Song D, Chen H, Zhang X. (2021) Unsupervised document embedding via contrastive augmentation. arXiv preprint arXiv: 2103.14542.
Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV. (2019) AutoAugment: learning augmentation strategies from data. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, pp 113–123.
Cubuk ED, Zoph B, Shlens J, Le QV. (2019) RandAugment: practical automated data augmentation with a reduced search space. In: IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 3008–3017.
Wen Q, Sun L, Yang F, Song X, Gao J, Wang X, Xu H. (2021) Time series data augmentation for deep learning: a survey. In: International joint conference on artificial intelligence (IJCAI). pp 4653–4660.
Fan H, Zhang F, Gao Y. (2020) Self-supervised time series representation learning by inter-intra relational reasoning. arXiv preprint arXiv: 2011.13548.
Eldele E, Ragab M, Chen Z, Wu M, Kwoh CK, Li X, Guan C. (2021) Time-series representation learning via temporal and contextual contrasting. In: international joint conference on artificial intelligence (IJCAI). pp 2352–2359.
Kang JH, Suh IS, Kim JY. Intensive care unit nurses’ knowledge and nursing practices regarding bedside electrocardiograph monitoring. J Korean Acad Soc Nurs Educ. 2014;20:60–70. https://doi.org/10.5977/jkasne.2014.20.1.60.
Lee K-I, Jang J-S, Lee T-R. Using the X-ray image, augmented reality based electrocardiogram measurement system development. J Digit Converg. 2016;14:331–9. https://doi.org/10.14400/JDC.2016.14.9.331.
(2014) The ECG curve: what is it and how does it originate? In: ECGs for beginners. Wiley, pp 11–32.
(2009) Chap. 4. The Electrical Axis and cardiac rotation. In: Basic bedside electrocardiogr. 1st Ed. https://doctorlib.info/cardiology/electrocardiography/5.html
Kiyasseh D, Zhu T, Clifton DA. CLOCS: contrastive learning of cardiac signals across space, time, and patients. Int Conf Mach Learn (ICML). 2021;139:5606–15.
Nonaka N, Seita J. (2020) Data augmentation for electrocardiogram classification with deep neural network. arXiv preprint arXiv: 2009.04398.
Kim KG, Lee BT. (2022) Graph structure based data augmentation method. arXiv Preprint arXiv: 2205.14619.
He K, Zhang X, Ren S, Sun J. (2016) Deep residual learning for image recognition. In: IEEE/CVF Conference on computer vision and pattern recognition (CVPR). IEEE, pp 770–778.
Nejedly P, Ivora A, Smisek R, Viscor I, Koscova Z, Jurak P, Plesinger F. (2021) Classification of ECG using ensemble of residual cnns with attention mechanism. In: Computing in cardiology (CinC). IEEE, pp 1–4.
Chen X, Guo W, Zhao L, Huang W, Wang L, Sun A, Li L, Mo F. Acute myocardial infarction detection using deep learning-enabled electrocardiograms. Front Cardiovasc Med. 2021;8:654515. https://doi.org/10.3389/fcvm.2021.654515.
Sakli N, Ghabri H, Soufiene BO, Almalki FA, Sakli H, Ali O, Najjari M. ResNet-50 for 12-lead electrocardiogram automated diagnosis. Comput Intell Neurosci. 2022;2022:1–16. https://doi.org/10.1155/2022/7617551.
Weimann K, Conrad TOF. Transfer learning for ECG classification. Sci Rep. 2021;11:5251. https://doi.org/10.1038/s41598-021-84374-8.
Raghu A, Shanmugam D, Pomerantsev E, Guttag J, Stultz CM. Data augmentation for electrocardiograms. Proc Mach Learn Res. 2022;174:282–310.
Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ. (2016) Deep networks with stochastic depth. In: Lecture notes in computer science. pp 646–661.
Loshchilov I, Hutter F. (2019) Decoupled weight decay regularization. arXiv Preprint arXiv: 1711.05101.
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin transformer: hierarchical vision transformer using shifted windows. In: IEEE/CVF Int Conf Comput Vis; 2021. pp. 10012–22.
Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jégou H. (2021) Training data-efficient image transformers & distillation through attention. In: International conference on machine learning. PMLR. pp 10347–10357.
Liu Z, Mao H, Christoph CW, Trevor F, Saining D, Berkeley UC, Wu C-Y, Feichtenhofer C, Darrell T, Xie S. (2022) A ConvNet for the 2020s. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 11976–11986.
He K, Chen X, Xie S, Li Y, Dollar P, Girshick R. (2022) Masked autoencoders are scalable vision learners. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 16000–16009.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. (2017) Attention Is all you need. In: Advances in neural information processing systems (NeurIPS).
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N. An image is worth 16x16 words: transformers for image recognition at scale. Int Conf Learn Represent (ICLR); 2021.
Na Y, Park M, Tae Y, Joo S. Guiding masked representation learning to capture spatio-temporal relationship of electrocardiogram. Int Conf Learn Represent (ICLR); 2024.
Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y, Yang Z, Zhang Y, Tao D. A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell. 2023;45:87–110.
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