[HTML][HTML] Deep learning applied to electrocardiogram interpretation
The 12-lead electrocardiogram (ECG) has remained, since its invention, 1 the centerpiece
noninvasive diagnostic tool among paramedics, emergency medical technicians, and …
noninvasive diagnostic tool among paramedics, emergency medical technicians, and …
Deep learning to automatically interpret images of the electrocardiogram: Do we need the raw samples?
Rule-based, computerised electrocardiogram (ECG) interpretation has been employed as
an important diagnostic aid for over half a century [1]. Despite this, there is significant room …
an important diagnostic aid for over half a century [1]. Despite this, there is significant room …
Usefulness of machine learning-based detection and classification of cardiac arrhythmias with 12-lead electrocardiograms
Background Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify
different types of cardiac arrhythmias with the use of a single-lead ECG input data set have …
different types of cardiac arrhythmias with the use of a single-lead ECG input data set have …
Electrocardiogram monitoring and interpretation: from traditional machine learning to deep learning, and their combination
S Parvaneh, J Rubin - 2018 Computing in Cardiology …, 2018 - ieeexplore.ieee.org
Cardiac arrhythmia can lead to morbidity and mortality and is a substantial economic
burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The …
burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The …
Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning
AH Kashou, AM May, PA Noseworthy - Journal of Electrocardiology, 2023 - Elsevier
Background Artificial intelligence-augmented ECG (AI-ECG) refers to the application of
novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG …
novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG …
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
ECG-based deep learning framework to identify ventricular arrhythmias in patients monitored with mct
R Mahajan, P Pundir, A Gambhir, S Adumala - Journal of the American …, 2023 - jacc.org
Background Ventricular Arrhythmias (VA), including Ventricular Tachycardia, Ventricular
Fibrillation, and Ventricular Flutter, are life-threatening arrhythmias that can lead to sudden …
Fibrillation, and Ventricular Flutter, are life-threatening arrhythmias that can lead to sudden …
[HTML][HTML] Validation of an automated artificial intelligence system for 12‑lead ECG interpretation
R Herman, A Demolder, B Vavrik, M Martonak… - Journal of …, 2024 - Elsevier
Background The electrocardiogram (ECG) is one of the most accessible and comprehensive
diagnostic tools used to assess cardiac patients at the first point of contact. Despite …
diagnostic tools used to assess cardiac patients at the first point of contact. Despite …
Artificial intelligence for the electrocardiogram
A Mincholé, B Rodriguez - Nature medicine, 2019 - nature.com
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Validation of an artificial intelligence model for 12-lead ECG interpretation
A Demolder, R Herman, B Vavrik… - European Heart …, 2023 - academic.oup.com
Background The electrocardiogram (ECG) is one of the most accessible and comprehensive
diagnostic tools to assess cardiac abnormalities. However, automated ECG interpretation …
diagnostic tools to assess cardiac abnormalities. However, automated ECG interpretation …