… This paper summarizes existing deep learning research using ECG data from multiple
perspectives and highlights existing challenges and problems to identify potential future research …

… In this study, we have analyzed literature reports that use deep learning on arrhythmia ECG
data. Some important observations obtained as a result of these examinations are as follows…

… studies of deep learning applied in ECG diagnosis according to … , deep belief network,
convolutional neural network and … This allows deep learning to have large sets of data trained and …

… DL models for the removal and rejection of such noise in ECG signals. The paper is structured
as follows: the DL models … in the sequence input data, while the CNN model tries to do the …

… 24,800 test beats, and provides more than 99 % accuracy using all three proposed models.
… including ECG dataset, data preprocessing, data balancing, CNN, and LSTM models. …

… [40] while also performing ECG classification using deep neural network (DNN… ECG data.
Zubair et al. [43] used CNN with 44 recordings of ECG signals obtained from MIT-BIH database…

… sets, and point out potential gaps in the design and implementation of DL models. … applied
to ECG data and point to future directions for research on DL to create robust models that can …

… In this paper, we propose an ensemble of multi-model deep learning methods to
automatically classify heartbeats arrhythmias in highly imbalanced ECG data. Both convolutional …

… deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of
deep learning on ECGs… demonstrate end-to-end training on the largest ECG database found in …

… /algorithms that are capable of analysing these massive amount of data in real-time. This
paper proposes a deep learning model for real-time segmentation of heartbeats. …