Abstract:Continuous electrocardiogram (ECG) recordings, which measure the electrical activity of the heart, are increasingly used to predict clinical outcomes via machine learning and artificial intelligence. [1-3] Raw ECG data can have a variety of interference artifacts which, if unattended to, can obscure or invalidate the results of machine learning algorithms. Current ECG de-noising routines focus primarily on how to purify ECG signals, but not on how to classify and excise regions of unusable voltage data. [4] He… Show more
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