… for ECG data [135] (2018), there have no systematic reviews focusing on deep learning
methods, which we consider to be promising methods for mining ECG data. Therefore, we …

… ECG data sources. We also present open research problems, such as the lack of attempts to
address the issue of blood pressure variability in training data … methods applied to ECG data …

… Machine learning, including deep learning, have shown to be … ECG biomarkers extracted
from machine learning techniques… Deep learning algorithms are designed for learning the data …

… the public databases for ingestion by deep learning models. These efforts have … deep
learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning …

… data from cardiology. Compared to these surveys, we only present state-of-the-art deep
learning techniques … , introduction on different deep learning methods, performance evaluation …

… machine-learning and deep-learning techniques on the same data set, which would be an
interesting approach to ECG … Finally, deep learning has been criticized for being a “black box.” …

… 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…

… In the experiments, we use three different ECG databases to evaluate the propose method
as … To learn a suitable feature representation of the data with DAE, we use the complete …

… For digital ECG data, deep learning algorithms could detect subtle alterations in ECGs …
For example, when using the traditional machine learning method, we usually input known …

… This allows deep learning to have large sets of data trained … review of deep learning’s
application in ECG diagnosis has … in studies of deep learning method applied in ECG diagnosis is …