Open AccessAutomated QT Analysis That Learns from Cardiologist Annotations
Author(s) -
Strachan Iain Guy David,
Hughes Nicholas Peter,
Poonawala Mustafa Hashim,
Mason Jay W.,
Tarassenko Lionel
Publication year - 2009
Publication title -
annals of noninvasive electrocardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.494
H-Index - 48
eISSN - 1542-474X
pISSN - 1082-720X
DOI - 10.1111/j.1542-474x.2008.00259.x
Subject(s) - qt interval , medicine , sotalol , pattern recognition (psychology) , artificial intelligence , confidence interval , data mining , computer science , atrial fibrillation
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.