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Using machine learning to detect problems in ECG data collection
24
Zitationen
5
Autoren
2011
Jahr
Abstract
We describe a data-driven approach, using a combination of machine learning algorithms to solve the 2011 Physionet/Computing in Cardiology (CinC) challenge — identifying data collection problems at 12 leads electrocardiography (ECG). Our data-driven approach reaches an internal (cross-validation) accuracy of almost 93% on the training set, and accuracy of 91.2% on the test set.
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