Validation of a novel algorithm for ventricular repolarization analysis: use of Physionet resources

Ventricular repolarization analysis allows extraction from the ECG signal of quantitative indexes (namely the QT interval), of prognostic value in unselected populations and cardiac patients, being related with arrhythmic risk. Several attempts to improve automatic ECG waveform detection have been accomplished, using signal derivatives, digital filtering, wavelet analysis, neural network techniques, nonlinear approaches. In the present study, a single-lead low-pass differentiation detector of ECG significant points (PulseMeter) has been evaluated. The algorithm performance has been validated against the manual annotation of the "QT database" (http://www.physionet.org/), developed for validation purposes. QRS complex and other ECG waveform boundaries were independently evaluated in the present study. The mean values and standard deviations computed improve the result of automatic annotation in QT database, especially in T wave detection. The QRS detector has a sensitivity of 99.96% and a positive predictivity of 99.96% on the first lead and a sensitivity of 99.90% and a positive predictivity of 99.94% on the second lead, showing a better performance than the automatic annotation in the QT database.