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We describe techniques and specifications of MATLAB software to processambulatory electrocardiogram (ECG) data. Through template-based beatidentification and simple pattern recognition models on the intervals betweenregular heart beats, we filter noisy sections of waveform and ectopic beats.Our end-to-end process can be used towards analysis of ECG and calculation ofheart rate variability metrics after beat adjustments, removals andinterpolation. Classification and noise detection is assessed on thehuman-annotated MIT-BIH Arrythmia and Noise Stress Test Databases.

Amark: Automated Marking and Processing Techniques for Ambulatory ECG Data