Why the study?
Does GTR1DA improve classification accuracy of 12-lead ECG signals compared to traditional vector- and tensor-based methods?
Population
12-lead ECG signals from a patient database
Comparison
Generalized tensor rank one discriminant… vs Other vector- and tensor-based methods including…
Design
Other
Authors
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May enhance ECG classification research; leaves open clinical validation before practice adoption.
Does GTR1DA improve classification accuracy of 12-lead ECG signals compared to traditional vector- and tensor-based methods?
GTR1DA provides a novel tensor-based feature extraction method for 12-lead ECG signals that improves classification accuracy and convergence compared to traditional methods.
Huang et al. (2014) studied this question.
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