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Automatic diagnosis of short-duration 12-lead ECG using a deep convolutional network
Universidade Federal de Minas Gerais.
Universidade Federal de Minas Gerais.
Universidade Federal de Minas Gerais.
Universidade Federal de Minas Gerais.
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2018 (English)In: ML4H: Machine Learning for Health Workshop at NeurIPS, Montréal, Canada, December 2018., 2018Conference paper, Published paper (Refereed)
Abstract [en]

We present a model for predicting electrocardiogram (ECG) abnormalities in shortduration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard in-clinics exams. Using this data, we trained a residual neural network with 9 convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG abnormalities. Future work should extend these results to cover a large range of ECG abnormalities, which could improve the accessibility of this diagnostic tool and avoid wrong diagnosis from medical doctors.

Place, publisher, year, edition, pages
2018.
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:uu:diva-368627OAI: oai:DiVA.org:uu-368627DiVA, id: diva2:1268457
Conference
ML4H: Machine Learning for Health Workshop at NeurIPS, December 08, 2018, Montreal, Canada
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-03-06Bibliographically approved

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https://arxiv.org/pdf/1811.12194.pdf

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  • asciidoc
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