Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Top Cited Papers
Open Access
- 7 January 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Medicine
- Vol. 25 (1), 65-69
- https://doi.org/10.1038/s41591-018-0268-3
Abstract
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 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 F1 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.Keywords
This publication has 45 references indexed in Scilit:
- An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networksNeurocomputing, 2013
- Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated LearningBiomedical Signal Processing and Control, 2013
- Computational intelligence for heart disease diagnosis: A medical knowledge driven approachExpert Systems with Applications, 2013
- Application of artificial neural networks for versatile preprocessing of electrocardiogram recordingsJournal of Medical Engineering & Technology, 2012
- Errors in the computerized electrocardiogram interpretation of cardiac rhythmJournal of Electrocardiology, 2007
- Common errors in computer electrocardiogram interpretationInternational Journal of Cardiology, 2006
- PhysioBank, PhysioToolkit, and PhysioNetCirculation, 2000
- Long Short-Term MemoryNeural Computation, 1997
- A Neural Network System for Detection of Atrial Fibrillation in Ambulatory ElectrocardiogramsJournal of Cardiovascular Electrophysiology, 1994
- Guidelines for electrocardiography: A report of the American College of Cardiology/American Heart Association Task Force on assessment of diagnostic and therapeutic cardiovascular procedures (Committee on Electrocardiography)Journal of the American College of Cardiology, 1992