Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review

S Hong, Y Zhou, J Shang, C Xiao, J Sun - Computers in biology and …, 2020 - Elsevier
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …

Computer-interpreted electrocardiograms: benefits and limitations

J Schläpfer, HJ Wellens - Journal of the American College of Cardiology, 2017 - jacc.org
Computerized interpretation of the electrocardiogram (CIE) was introduced to improve the
correct interpretation of the electrocardiogram (ECG), facilitating health care decision …

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

AY Hannun, P Rajpurkar, M Haghpanahi, GH Tison… - Nature medicine, 2019 - nature.com
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 …

Cardiologist-level arrhythmia detection with convolutional neural networks

P Rajpurkar, AY Hannun, M Haghpanahi… - arXiv preprint arXiv …, 2017 - arxiv.org
We develop an algorithm which exceeds the performance of board certified cardiologists in
detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single …

Performance of a convolutional neural network and explainability technique for 12-lead electrocardiogram interpretation

JW Hughes, JE Olgin, R Avram, SA Abreau… - JAMA …, 2021 - jamanetwork.com
Importance Millions of clinicians rely daily on automated preliminary electrocardiogram
(ECG) interpretation. Critical comparisons of machine learning–based automated analysis …

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals

Z Wang, S Stavrakis, B Yao - Computers in Biology and Medicine, 2023 - Elsevier
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …

The computerized ECG: friend and foe

H Smulyan - The American journal of medicine, 2019 - Elsevier
Computerized interpretation of the electrocardiogram (ECG) began in the 1950s when
conversion of its analog signal to digital form became available. Since then, automatic …

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

AA Armoundas, SM Narayan, DK Arnett… - Circulation, 2024 - Am Heart Assoc
A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …

Electrocardiographic left atrial abnormality and stroke subtype in the atherosclerosis risk in communities study

H Kamel, WT O'Neal, PM Okin, LR Loehr… - Annals of …, 2015 - Wiley Online Library
Objective The aim of this study was to assess the relationship between abnormally
increased P‐wave terminal force in lead V1, an electrocardiographic (ECG) marker of left …

[HTML][HTML] A deep-learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: algorithm development

CS Lin, C Lin, WH Fang, CJ Hsu, SJ Chen… - JMIR medical …, 2020 - medinform.jmir.org
Background The detection of dyskalemias—hypokalemia and hyperkalemia—currently
depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia …