Key result
A cyclostationary least mean squares algorithm successfully detected changes in morphology during the spontaneous onset of ventricular tachycardia from sinus rhythm in all cases.
Why the study?
Does a cyclostationary least mean squares algorithm accurately discriminate ventricular tachycardia from sinus rhythm?
Does a cyclostationary least mean squares algorithm accurately discriminate ventricular tachycardia from sinus rhythm?
Answers are grounded in this paper and cite their sources.
A novel cyclostationary least mean squares algorithm successfully discriminates ventricular tachycardia from sinus rhythm in real-time by monitoring prediction errors.
A simple real-time algorithm is presented which accurately predicts a single beat of either sinus rhythm (SR) or ventricular tachycardia (VT) and detects a change from one rhythm to the other. The proposed algorithm learns from previous beats, so the prediction error decreases with time, thus accurately predicting the electrocardiogram. The algorithm detects changes in morphology (such as occurs during the spontaneous onset of VT during SR) by monitoring sudden increases in prediction error and is successful in all cases.
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Finelli et al. (2005) studied Ventricular Tachycardia and Sinus Rhythm. Cyclostationary Least Mean Squares Algorithm was evaluated on Detection of changes in morphology (onset of ventricular tachycardia during sinus rhythm). A cyclostationary least mean squares algorithm successfully detected changes in morphology during the spontaneous onset of ventricular tachycardia from sinus rhythm in all cases.
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