Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network

By Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, G. Tison, Codie Bourn, M. Turakhia, Andrew Y. Ng. 2019

2,797 citations recorded by the source corpus.

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. 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.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

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Citations in the corpus

A Federated Learning Framework for Healthcare IoT devices (2020, positive): “We apply the state of the art architecture (Hannun et al., 2019) on the PhysioNet 2017 dataset (phy, 2017).”
AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “[49] proposed a 34-layer CNN model to map EKG signals to a series of rhythm classes to detect heart arrhythmia.”
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Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “The use of arti ﬁ cial intelligence (AI) and machine learning (ML) in the clinical arena has developed tremendously over the past decades, with numerous examples in medical imaging, cardiology, and acute care 1 – 6 .”
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019, background): “Some of the most impressive applications of machine learning rely on the availability of large amounts of data that are used to create accurate classifiers that, for example, could discriminate between the absence or presence of a disease [1].”
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