Pattern recognition analysis of digital ECGs: decreased QT measurement error and improved precision compared to semi-automated methods.

BACKGROUND AND PURPOSE

Machine-read QT measurements employing T-wave detection algorithms (ALG) are not accepted by regulatory agencies for the primary analysis of thorough QT (TQT) studies. Newly developed pattern recognition software (PRO) which matches ECG waveforms to user-defined templates may improve this situation.

METHODS

We compared RR, QT, QTc, QT variability, T-end measurement errors, and individual QT rate correction factors and their associated coefficients of determination (R(2)) following ALG and PRO analysis. Machine-read QTc values were compared with core laboratory semi-automated (SA) values for verification.

RESULTS

Compared to ALG, PRO reduced the frequency of T-end measurement errors (5.6% vs. 0.1%), reduced the intra-individual QT variability (12.6±5.9 vs. 4.9±1.1ms) and allowed the recovery of 3/58 subjects that exhibited an unacceptable (<0.9) R(2).

CONCLUSIONS

PRO adjusted for ALG-based T-end measurement errors and provided an accurate and precise automated method for continuous QT analysis, thus offering an alternative to resource-intensive semi-automated analyses currently performed by ECG core laboratories.