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

• Published in: Nature medicine Vol. 25; no. 1; pp. 65 - 69
• Main Authors: Hannun, Awni Y., Rajpurkar, Pranav, Haghpanahi, Masoumeh, Tison, Geoffrey H., Bourn, Codie, Turakhia, Mintu P., Ng, Andrew Y.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2019; Nature Publishing Group
• Subjects: 631/114/1305; 692/699/75/29; Algorithms; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Arrhythmias, Cardiac - diagnostic imaging; Artificial neural networks; Biomedical and Life Sciences; Biomedicine; Cancer Research; Cardiologists; Cardiology; Classification; Deep learning; Diagnosis; Diagnostic systems; Echocardiography; EKG; Electrocardiography; Heart; Humans; Infectious Diseases; Letter; Machine learning; Medical schools; Metabolic Diseases; Molecular Medicine; Monitoring, Ambulatory; Neural networks; Neural Networks, Computer; Neurosciences; Rhythm; Risk factors; ROC Curve; Sensitivity
• ISSN: 1078-8956; 1546-170X; 1546-170X
• DOI: 10.1038/s41591-018-0268-3

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow 1 . Widely available digital ECG data and the algorithmic paradigm of deep learning 2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F 1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions. Analysis of electrocardiograms using an end-to-end deep learning approach can detect and classify cardiac arrhythmia with high accuracy, similar to that of cardiologists.