… by conventional machine-learning and deep-learning techniques on the same data set,
which would be an interesting approach to ECG interpretation. Finally, deep learning has been …

… machine learning, deep learning, and their combination for automated ECG interpretation
… the best machine learning method for ECG monitoring and interpretation. Promising results …

… learning-based systems applied to the electrocardiogram as well as pros and cons in the use
of these techniques. Machine learning, including deep learning, … ECG interpretation is time-…

… This study is the first to show the improved performance of a deep learning algorithm
over a standard ECG interpretation algorithm on unselected 12‑lead ECGs, although the …

… Our main contributions in this work are as follows: (i) we are the first to explore arrhythmia
recognition from ECG images with deep learning approaches; (ii) we construct a large scale …

… in interpreting ECGs, only a handful offer insight into the model’s learning representation of
the ECG … Without explaining what these DL models are sensing on the ECG to perform their …

… has been demonstrated to add important diagnostic and prognostic information to the
ECG interpretation, even for those identified as normal by physicians, it remains unclear what …

… the robustness of the image-based ECG interpretation pipeline and improve diagnostic quality:
… To test this hypothesis, we attempt to use DL to achieve accurate ECG interpretation of a …

… In this spirit, we develop DDxNet, a deep architecture for time-… (ECG/EEG/EHR), required
level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/…

… A number of deep learning methods have been applied to feature extraction and classification
in ECG interpretation. SAE is an unsupervised way to extract features by encoding and …