Evaluation of an algorithm for highly automated measurements of QT interval.

摘要

INTRODUCTION: Noise, artifact, and labile morphology of ECGs collected from freely moving animals in safety pharmacology studies render accurate measurements of QT interval challenging. Consequently, a high percentage of beats are uninterpretable and results provided by currently available analysis algorithms often require extensive manual review to correct errors. Performance of a novel algorithm, Multi-Domain Signal Processing (MDSP), is evaluated as a means of removing noise (denoising) without distorting morphology and for obtaining accurate beat-to-beat QT measurements. METHODS: Performance was evaluated using controlled experiments and an observational evaluation as follows: a) a clean ECG strip was intentionally corrupted with varying levels of noise to provide recordings of known signal-to-noise ratio (SNR). SNR and fidelity were compared pre- and post-MDSP denoising, b) beat-to-beat QT of a noisy ECG was measured manually pre- and post-MDSP denoising and automatically by MDSP, c) beat-to-beat QT of a clean ECG was measured manually and automatically using MDSP, and d) beat-to-beat QT was computed for 3 freely moving non-human primates (NHP) pre- and post-torsadogen administration and the impact of averaging on QTSD, QT/RR dynamics and relationship was evaluated. RESULTS: MDSP reduced noise amplitude by up to 85% while preserving signal morphology. Mean QTs for manual and automatic measurements on a noisy ECG were within 2+/-15ms. MDSP-denoising prior to manual QT measurements resulted in a 22% decrease in QTSD compared to measurements obtained without denoising. Average QT standard deviation of the mean (QTSD) for automatic MDSP-derived measurements for 3 freely moving subjects was 7ms with 2.5% of beats automatically excluded due to noise. DISCUSSION: This work demonstrates that the MDSP algorithm shows promise as a tool for providing accurate automatic beat-to-beat measurements of QT interval from NHP in safety pharmacology studies. A methodology is presented for characterizing the impact of noise on algorithm performance.