The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms

Detecting atrial fibrillation by deep convolutional neural networks

(2018) Yong Xia et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

(2018) Aurore Lyon et al.   Journal of the Royal Society Interface

A deep learning approach for real-time detection of atrial fibrillation

(2018) Rasmus S. Andersen et al.   EXPERT SYSTEMS WITH APPLICATIONS

Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

(2018) Zachi I. Attia et al.   NATURE MEDICINE

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

(2018) Awni Y. Hannun et al.   NATURE MEDICINE

2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry

(2017) Jonathan S. Steinberg et al.   HEART RHYTHM

Dermatologist-level classification of skin cancer with deep neural networks

(2017) Andre Esteva et al.   NATURE

Telerehabilitation in heart failure patients: The evidence and the pitfalls

(2016) Ewa Piotrowicz et al.   INTERNATIONAL JOURNAL OF CARDIOLOGY

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

(2016) Varun Gulshan et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Optimal Duration of Monitoring for Atrial Fibrillation in Cryptogenic Stroke: A Nonsystematic Review

(2016) Essa Hariri et al.   Biomed Research International

Classic Myocardial Infarction With a Silent 12-Lead Electrocardiogram (How Come?)

(2011) C. Richard Conti   CLINICAL CARDIOLOGY

Indications for the use of diagnostic implantable and external ECG loop recorders

(2009) et al.   EUROPACE