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Automatic diagnosis...
Automatic diagnosis of short-duration 12-lead ECG using a deep convolutional network
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- Ribeiro, Antonio (författare)
- Universidade Federal de Minas Gerais
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- Ribeiro, Manoel (författare)
- Universidade Federal de Minas Gerais
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- Paixao, Gabriela (författare)
- Universidade Federal de Minas Gerais
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- Oliveira, Derick (författare)
- Universidade Federal de Minas Gerais
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- Gomes, Paulo (författare)
- Universidade Federal de Minas Gerais
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- Canazart, Jessica (författare)
- Universidade Federal de Minas Gerais
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- Pifano, Milton (författare)
- Universidade Federal de Minas Gerais
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- Wagner, Meira (författare)
- Universidade Federal de Minas Gerais
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- Schön, Thomas B., Professor, 1977- (författare)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
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- Ribeiro, Antonio (författare)
- Universidade Federal de Minas Gerais
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(creator_code:org_t)
- 2018
- 2018
- Engelska.
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Ingår i: <em>ML4H: Machine Learning for Health Workshop at NeurIPS</em>, Montréal, Canada, December 2018..
- Relaterad länk:
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https://arxiv.org/pd...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)