Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65–9.
Ribeiro AH, Ribeiro MH, Paixão GM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MP, Andersson CR, Macfarlane PW, Meira W Jr, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11(1):1–9.
Malik J, Devecioglu OC, Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1d self-operational neural networks. IEEE Trans Biomed Eng. 2021;69(5):1788–801.
Hammad M, Alkinani MH, Gupta B, El-Latif A, Ahmed A. Myocardial infarction detection based on deep neural network on imbalanced data. Multimedia Syst. 2022;28(4):1373–85.
Mu L, Liu H. Noninvasive electrocardiographic imaging with low-rank and non-local total variation regularization. Pattern Recognit Lett. 2020;138:106–114. https://doi.org/10.1016/j.patrec.2020.07.007.
Labati RD, Muñoz E, Piuri V, Sassi R, Scotti F. Deep-ECG: convolutional neural networks for ECG biometric recognition. Pattern Recogn Lett. 2019;126:78–85.
Li Y, Pang Y, Wang K, Li X. Toward improving ECG biometric identification using cascaded convolutional neural networks. Neurocomputing. 2020;391:83–95.
Zhang Y, Zhao Z, Deng Y, Zhang X, Zhang Y. Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG. Biomed Signal Process Control. 2021;68:102689.
Puri DV, Nalbalwar SL, Nandgaonkar AB, Gawande JP, Wagh A. Automatic detection of Alzheimer’s disease from EEG signals using low-complexity orthogonal wavelet filter banks. Biomed Signal Process Control. 2023;81:104439. https://doi.org/10.1016/j.bspc.2022.104439.
Rasti-Meymandi A, Ghaffari A. A deep learning-based framework For ECG signal denoising based on stacked cardiac cycle tensor. Biomed Signal Process Control. 2022;71:103275.
Xu B, Liu R, Shu M, Shang X, Wang Y. An ECG denoising method based on the generative adversarial residual network. Comput Math Methods Med. 2021;2021:69.
Parkale YV, Nalbalwar SL. Application of compressed sensing (CS) for ECG signal compression: a review. Science. 2017;6:53–65. https://doi.org/10.1007/978-981-10-1678-3_5.
Zhang Y, Li J, Wei S, Zhou F, Li D. Heartbeats classification using hybrid time-frequency analysis and transfer learning based on ResNet. IEEE J Biomed Health Inform. 2021;25(11):4175–84.
Xiong Z, Nash MP, Cheng E, Fedorov VV, Stiles MK, Zhao J. ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol Meas. 2018;39(9):094006.
Xiong P, Xue Y, Zhang J, Liu M, Du H, Zhang H, Hou Z, Wang H, Liu X. Localization of myocardial infarction with multi-lead ECG based on DenseNet. Comput Methods Programs Biomed. 2021;203:106024.
Yildirim Ö. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med. 2018;96:189–202.
Zhang J, Liu A, Gao M, Chen X, Zhang X, Chen X. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif Intell Med. 2020;106:101856.
Kenton J.D.M.-W.C, Toutanova L.K. Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of naacL-HLT, 2019; pp. 4171–4186.
Hsu W-N, Bolte B, Tsai Y-HH, Lakhotia K, Salakhutdinov R, Mohamed AI. Transactions on audio, speech, and language processing. Science. 2021;29:3451–60.
Schneider S, Baevski A, Collobert R, Auli M. wav2vec: Unsupervised Pre-Training for Speech Recognition. In: INTERSPEECH 2019.
Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, 2020; pp. 1597–1607. PMLR
He K, Chen X, Xie S, Li Y, Dollár P, Girshick R. Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022; pp. 16000–16009.
Sarkar P, Etemad A. Self-supervised ECG representation learning for emotion recognition. IEEE Trans Affect Comput. 2020;2:96.
Zhang W, Geng S, Hong S. A simple self-supervised ECG representation learning method via manipulated temporal-spatial reverse detection. Biomed Signal Process Control. 2023;79:104194.
Kiyasseh D, Zhu T, Clifton D.A. Clocs: Contrastive learning of cardiac signals across space, time, and patients. In: International Conference on Machine Learning, 2021; pp. 5606–5615. PMLR
Kachuee M, Fazeli S, Sarrafzadeh M. ECG heartbeat classification: A deep transferable representation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), 2018; pp. 443–444. IEEE
Yan G, Liang S, Zhang Y, Liu F. Fusing transformer model with temporal features for ECG heartbeat classification. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019; pp. 898–905. IEEE
Manju B.R, Nair A.R. Classification of cardiac arrhythmia of 12 lead ecg using combination of smoteenn, xgboost and machine learning algorithms. In: 2019 9th International Symposium on Embedded Computing and System Design (ISED) 2019. https://doi.org/10.1109/ised48680.2019.9096244.
Chen J, Zheng X, Yu H, Chen D.Z, Wu J. Electrocardio panorama: synthesizing new ECG views with self-supervision. In: Zhou, Z.-H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pp. 3597–3605. International Joint Conferences on Artificial Intelligence Organization, ??? 2021. https://doi.org/10.24963/ijcai.2021/495. Main Track.
Lee B.T, Kong S.T, Song Y, Lee Y. Self-supervised learning with electrocardiogram delineation for arrhythmia detection. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021; pp. 591–594. IEEE
Alday EAP, Gu A, Shah AJ, Robichaux C, Wong A-KI, Liu C, Liu F, Rad AB, Elola A, Seyedi S, et al. Classification of 12-lead ECGs: the physionet/computing in cardiology challenge 2020. Physiol Meas. 2020;41(12):124003.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:63.
Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987;2(1–3):37–52.
Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. Cornell University - arXiv, Cornell University - arXiv, 2020.
Oh J, Chung H, Kwon J.-m, Hong D.-g, Choi E. Lead-agnostic self-supervised learning for local and global representations of electrocardiogram2
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.
Lynn HM, Kim P, Pan SB. Data independent acquisition based bi-directional deep networks for biometric ECG authentication. Appl Sci. 2021;11(3):1125.
Pourbabaee B, Roshtkhari MJ, Khorasani K. Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans Syst Man Cybern Syst. 2018;48(12):2095–104.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016; pp. 770–778.
Loni M, Sinaei S, Zoljodi A, Daneshtalab M, Sjödin M. DeepMaker: a multi-objective optimization framework for deep neural networks in embedded systems. Microprocess Microsyst. 2020;73:102989.
Comments (0)