Jin Z, Sun Y, Cheng AC. Predicting cardiovascular disease from real-time electrocardiographic monitoring: an adaptive machine learning approach on a cell phone. Ann Int Conf IEEE Eng Med Biol Soc. 2009;2009:6889–92.

MATH  Google Scholar 

Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18:465–78.

Article  Google Scholar 

Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71:2668–79.

Article  Google Scholar 

Chen L, Yu H, Huang Y, Jin H. ECG signal-enabled automatic diagnosis technology of heart failure. J Healthc Eng. 2021;2021:5802722.

Article  Google Scholar 

Awan SE, Sohel F, Sanfilippo FM, Bennamoun M, Dwivedi G. Machine learning in heart failure: ready for prime time. Curr Opin Cardiol. 2018;33:190–5.

Article  MATH  Google Scholar 

Cun YL, Boser B, Denker J, Henderson D, Jackel L. Handwritten digit recognition with a backpropogation network. Adv Neural Inform Process Syst. 1990.

Yann L, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition.

Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, et al. State-of-the-art deep learning methods on electrocardiogram data: systematic review. JMIR Med Inform. 2022;10: e38454.

Article  Google Scholar 

Vaillant R., Monrocq C., Le Cun Y. Original approach for the localisation of objects in images.

Krizhevsky A, Sutskever I, Hinton GE. Computer Vision and its implications.

Zeiler MD, Fergus R. Visualizing and Understanding Convolutional Networks. 2014.

Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Sci. 2014. https://doi.org/10.48550/arXiv.1409.1556.

Szegedy C, Liu W, Jia Y, Sermanet P, Rabinovich A. Going deeper with convolutions. 2015.

He K, Zhang X, Ren S, Sun J. [IEEE 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Las Vegas, NV, USA (2016.6.27–2016.6.30)] 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Deep Residual Learning for Image Recognition. 2016; 1:770–8.

Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back propagating errors. Nature. 1986;323:533–6.

Article  MATH  Google Scholar 

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735–80.

Article  MATH  Google Scholar 

Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Computer Science. 2014. https://doi.org/10.3115/v1/D14-1179.

Article  Google Scholar 

Shi B, Xiang, Yao C. transactions on pattern analysis and machine intelligence IEEE transactions on pattern analysis and machine intelligence 1 an end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE. 2017. https://doi.org/10.1109/tpami.2016.2646371.

Rumelhart DE, Mcclelland JL. Parallel distributed processing: explorations in the microstructure of cognition. Language. 1986. https://doi.org/10.2307/415721.

Article  MATH  Google Scholar 

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Networks. 2014.

Wok K, Wok A. Early and Remote Detection of Possible Heartbeat Problems With Convolutional Neural Networks and Multipart Interactive Training. IEEE Access. 2019;PP.

Wang P, Hou B, Shao S, Yan R. ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network. IEEE Access. 2019;1:1–1.

Google Scholar 

Chen B, Javadi G, Hamilton A, Sibley S, Laird P, Abolmaesumi P, et al. Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels. Sci Rep. 2022;12:20140.

Article  Google Scholar 

Li D, Sun T, Xue Y, Xie Y, Chen X, Nan J. Detection of premature ventricular contractions using 12-lead dynamic ECG based on squeeze-excitation residual network. Int J Adv Comp Sci Appl. 2022;13:8–15.

Google Scholar 

Lai D, Zhang X, Zhang Y, Bin Heyat MB, Detection CNNB, of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum. In,. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany: IEEE. 2019;2019:4897–900.

Google Scholar 

Xiong Z, Stiles MK, Gillis AM, Zhao J. Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks. Comput Biol Med. 2022;146: 105551.

Article  Google Scholar 

Ganeshkumar M, Ravi V, Sowmya V, Gopalakrishnan EA, Soman KP. Explainable deep learning-based approach for multilabel classification of electrocardiogram. IEEE Trans Eng Manage. 2023;70:2787–99.

Article  Google Scholar 

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339: 2535.

Yu J, Wang X, Chen X, Guo J. Automatic premature ventricular contraction detection using deep metric learning and KNN. Biosensors-Basel. 2021;11:69.

Article  MATH  Google Scholar 

Cinar A, Tuncer SA. Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks. Comp Methods Biomech Biomed Eng. 2021;24:203–14.

Article  MATH  Google Scholar 

Ma F, Zhang J, Chen W, Liang W, Yang W. An automatic system for atrial fibrillation by using a CNN-LSTM model. Discrete Dyn Nat Soc. 2020;2020:3198783.

Article  MathSciNet  MATH  Google Scholar 

Pandey SK, Kumar G, Shukla S, Kumar A, Singh KU, Mahato S. Automatic detection of atrial fibrillation from ECG signal using hybrid deep learning techniques. J Sensors. 2022;2022:6732150.

Article  MATH  Google Scholar 

Yu Z, Chen J, Liu Y, Chen Y, Wang T, Nowak R, et al. DDCNN: a deep learning model for AF detection from a single-lead short ECG signal. IEEE J Biomed Health Inform. 2022;26:4987–95.

Article  Google Scholar 

Wagner P, Strodthoff N, Bousseljot R-D, Kreiseler D, Lunze FI, Samek W, et al. PTB-XL, a large publicly available electrocardiography dataset. Sci Data. 2020;7:154.

Article  Google Scholar 

Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci. 2017;415:190–8.

Article  Google Scholar 

Liu W, Zhang M, Zhang Y, Liao Y, Huang Q, Chang S, et al. Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J Biomed Health Inform. 2018;22:1434–44.

Article  MATH  Google Scholar 

Li W, Tang YM, Yu KM, To S. SLC-GAN: an automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis. Inf Sci. 2022;589:738–50.

Article  Google Scholar 

Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, El-Latif AAA. Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia Tools Appl. 2020. https://doi.org/10.1007/s11042-020-08769-x.

Article  Google Scholar 

Strodthoff N, Strodthoff C. Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol Meas. 2019;40: 015001.

Article  MATH  Google Scholar 

He Z, Yuan S, Zhao J, Du B, Yuan Z, Alhudhaif A, et al. A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning. Inf Sci. 2022;606:649–68.

Article  MATH  Google Scholar 

Li D, Shi C, Zhao J, Liu Y, Li C. Intra-patient and inter-patient multi-classification of severe cardiovascular diseases based on CResFormer. Tsinghua Sci Technol. 2023;28:386–404.

Article  MATH  Google Scholar 

Liu W, Huang Q, Chang S, Wang H, He J. Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Sign Process Control. 2018;45:22–32.

Article  MATH  Google Scholar 

Zhang X, Li R, Dai H, Liu Y, Zhou B, Wang Z. Localization of myocardial infarction with multi-lead bidirectional gated recurrent unit neural network. IEEE Access. 2019;7:161152–66.

Article  Google Scholar 

Fu L, Lu B, Nie B, Peng Z, Liu H, Pi X. Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors (Basel). 2020;20:1020.

Article  Google Scholar 

Karhade J, Ghosh SK, Gajbhiye P, Tripathy RK, Acharya UR. Multichannel multiscale two-stage convolutional neural network for the detection and localization of myocardial infarction using vectorcardiogram signal. Appl Sci. 2021;11:7920.

Article  MATH  Google Scholar 

Cao Y, Liu W, Zhang S, Xu L, Zhu B, Cui H, et al. Detection and localization of myocardial infarction based on multi-scale ResNet and attention mechanism. Front Physiol. 2022;13: 783184.

Article  Google Scholar 

Jahmunah V, Ng EYK, San TR, Acharya UR. Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput Biol Med. 2021;134: 104457.

Article  Google Scholar 

Cho Y, Kwon JM, Kim KH, Medina-Inojosa JR, Jeon KH, Cho S, et al. Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography. Sci Rep. 2020;10:20495.

Article  MATH  Google Scholar 

Gustafsson S, Gedon D, Lampa E, Ribeiro AH, Holzmann MJ, Schon TB, et al. Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients. Sci Rep. 2022;12:19615.

Article  MATH  Google Scholar 

Han C, Song Y, Lim H-S, Tae Y, Jang J-H, Lee BT, et al. Automated detection of acute myocardial infarction using asynchronous electrocardiogram signals-preview of implementing artificial intelligence with multichannel electrocardiographs obtained from smartwatches: retrospective study. J Med Internet Res. 2021;23: e31129.

Article  Google Scholar 

Xiao R, Ding C, Hu X, Clifford GD, Wright DW, Shah AJ, et al. Integrating multimodal information in machine learning for classifying acute myocardial infarction. Physiol Meas. 202