Why the study?
Most ECG diagnosis relies on personal judgment by medical staff, causing heavy burden and low efficiency, which automatic ECG analysis could alleviate.
An RBF neural network combined with Pan-Tompkins feature extraction and K-means clustering achieves 98.9% accuracy in classifying ECG signals from the MIT-BIH database.
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Achieves high database ECG accuracy; hypothesis-generating and requires prospective clinical validation before adoption.
Fang et al. (2022) studied this question.
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