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arXiv:1707.01836cs.CV

Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

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

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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cs.CVComputer Vision and Pattern Recognition

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Linked by 2 identifiable commenters in 2 comments across 1 thread, from Jan 2019 to Jan 2019. Found in 3 HN submissions from Jul 2017 to Aug 2017. Open submission discussions.

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  1. Cardiologist-Level Arrhythmia Detection Using a Deep Neural Network 2 comments

    • link to the paper: https://arxiv.org/pdf/1707.01836.pdf

      signa11View on HNParent context

    • Pretty impressive. From the Figure in on page 2 https://arxiv.org/pdf/1707.01836.pdf#page=2 > Figure 2. Deep Neural Network architecture. Our deep neural network consisted of 33 convolutional layers followed by a linear output layer into a softmax. The network accepts raw ECG…

      ivan_ahView on HNParent context

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