Abstract
In signal processing of exercise electrocardiograms (ECGs), artifacts are a recurring problem. It is still difficult to discriminate the ECG curves from artifacts, especially in exercise ECGs and particularly in the high exercise phase. We focused on the artifact problem and worked on two new topics: the Finite Impulse Response Residual Filtering (FRF) algorithm and the Intelligent Lead Switch algorithm. The FRF algorithm reduces the baseline wander and muscle noise in the ECG stream, with much less distortion of the QRS complexes. It subtracts a continuously updated median beat from the current ECG, filters the residual signal with a high-pass and a low-pass filter, and adds the median beat to the filtered residual signal. The Intelligent Lead Switch algorithm takes advantage of the redundancy of a multilead system (eg, standard leads), which is nowadays used during exercise testing. It selects the best leads for QRS detection and thus improves the heart rate calculation, ST segment evaluation, and arrhythmia classification.
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