Improved alignment method for noisy high-resolution ECG and Holter records using multiscale cross-correlation.
The coherent signal averaging process requires accurate estimation of a fiducial point in all beats to be averaged. The temporal cross-correlation between each detected beat and a template beat is the typical alignment method used with high-resolution electrocardiogram (HRECG) records. However, this technique does not produce a precise fiducial mark in records with high noise levels, like those found in Holter HRECG systems. In this study, we propose a new alignment method based on the multiscale cross-correlation between the template and each detected beat. We report the results of tests comparing multiscale and temporal methods for 3000 beats of simulated HRECG records corrupted separately with white noise, electromyographic noise and power line interference (50 Hz) of different root mean square levels. A second study with simulated records constructed from real Holter HRECG records is also presented. The results indicate that the multiscale alignment method produces a lower trigger jitter than the temporal method in all tests. We conclude that the proposed alignment method can be used in HRECG records with high noise levels.
Laciar
Eric
E
Department of Automatic Control (ESAII), Biomedical Engineering Research Center, Technical University of Catalonia, 08028 Barcelona, Spain.
Jané
Raimon
R
Brooks
Dana H
DH
D000465:Algorithms; D016477:Artifacts; D002311:Cardiomyopathy, Dilated / Q000175:diagnosis / Q000503:physiopathology; D002598:Chagas Cardiomyopathy / Q000175:diagnosis / Q000503:physiopathology; D003198:Computer Simulation; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000503:physiopathology; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D015716:Electrocardiography, Ambulatory / Q000379:methods; D004574:Electromagnetic Fields; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D013269:Stochastic Processes