Automated QT analysis that learns from cardiologist annotations

Abstract

Background: Reliable, automated QT analysis would allow the use of all the ECG data recorded during continuous Holter monitoring, rather than just intermittent 10-second ECGs. Methods: BioQT is an automated ECG analysis system based on a Hidden Markov Model, which is trained to segment ECG signals using a database of thousands of annotated waveforms. Each sample of the ECG signal is encoded by its wavelet transform coefficients. BioQT also produces a confidence measure which can be used to identify unreliable segmentations. The automatic generation of templates based on shape descriptors allows an entire 24 hours of QT data to be rapidly reviewed by a human expert, after which the template annotations can automatically be applied to all beats in the recording. Results: The BioQT software has been used to show that drug-related perturbation of the T wave is greater in subjects receiving sotalol than in those receiving moxifloxacin. Chronological dissociation of T-wave morphology changes from the QT prolonging effect of the drug was observed with sotalol. In a definitive QT study, the percentage increase of standard deviation of QT c for the standard manual method with respect to that obtained with BioQT analysis was shown to be 44% and 30% for the placebo and moxifloxacin treatments, respectively. Conclusions: BioQT provides fully automated analysis, with confidence values for self-checking, on very large data sets such as Holter recordings. Automatic templating and expert reannotation of a small number of templates lead to a reduction in the sample size requirements for definitive QT studies. © 2009, Wiley Periodicals, Inc.