This work is part of an on-going effort to implement real-time biosignal quality analysis for electrocardiograms (ECG). Biosignal quality analysis can provide an estimate of the signal-to-noise ratio (SNR), which can be used to gate alarms for myocardial ischemia, as indicated by ST deviations in ambulatory ECG. Currently, the false alarm rate is high due to contaminants in the ECG, such as motion artifact. In the proposed algorithm, ECG data are segmented into 16-beat analysis windows. Within an analysis window, the heart's PQRST complexes are segmented, aligned, and averaged to form an estimate of the true PQRST complex output by the heart. A SNR is estimated for each beat by comparing the PQRST complex of each beat to the average PQRST complex; the lowest SNR across all beats in the analysis window is the SNR assigned to the analysis window. Performance of the algorithm is evaluated using ECG data contaminated with various levels of motion artifact. The algorithm provides SNR estimates that are correlated with true SNR (r = 0.89). Leveraging the repeatability of the PQRST complex, it derives these estimates solely from the recorded ECG and does not require a noise free period or any supplementary signals. Results indicate that this algorithm may be a viable method for gating myocardial alarms for reduction of the false alarm rate.