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arXiv · 1707.01836

Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Pranav Rajpurkar·Awni Y. Hannun·Masoumeh Haghpanahi·Codie Bourn·Andrew Y. Ng
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Abstract

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-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).

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Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, Andrew Y. Ng. 2017-07-06. Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. https://arxiv.org/abs/1707.01836

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