The aim of this study was to enhance the ECG pre-processing modalities for beat-to-beat QT interval variability measurement based on template matching. The R-peak detection algorithm has been ...substituted and an efficient baseline removal algorithm has been implemented in existing computer software. To test performance we used simulated ECG data with fixed QT intervals featuring Gaussian noise, baseline wander and amplitude modulation and two alternative algorithms. We computed the standard deviation of beat-to-beat QT intervals as a marker of QT interval variability (QTV). Significantly a lower beat-to-beat QTV was found in the updated approach compared the original algorithm. In addition, the updated template matching computer software outperformed the previous version in discarding fewer beats. In conclusion, the updated ECG preprocessing algorithm is recommended for more accurate quantification of beat-to-beat QT interval variability.
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