Detecting QT prolongation from a single-lead ECG with deep learning.
For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I-the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-06-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000539 |