Why the study?
Manual ECG analysis for cardiac arrhythmia is time-consuming and error-prone, while machine learning approaches face limitations such as hand-crafted feature selection, extended training time, and a lack of labeled data.
A novel deep learning architecture combining Residual Network and Orthogonal Matching Pursuit achieves high accuracy (98.99%) in classifying eight types of arrhythmias from ECG recordings.
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May aid automated ECG arrhythmia screening; leaves open prospective validation before clinical use.
Boulif et al. (2024) studied this question.
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