Key result
An algorithm using Fisher's Linear Discriminant and R-R time series features successfully separated normal and premature atrial complex beats with 99% accuracy.
Why the study?
Can an algorithm using Fisher Linear Discriminant accurately detect premature atrial complexes from ECG signals?
Can an algorithm using Fisher Linear Discriminant accurately detect premature atrial complexes from ECG signals?
A novel algorithm using Fisher Linear Discriminant can detect premature atrial complexes with 99% accuracy, potentially aiding in the prediction of atrial arrhythmias.
Requires prospective validation before clinical use; leaves open real-world performance in arrhythmia prediction.
Currently, no reliable method exists to detect premature atrial complexes (PAC). The detection of PACs is clinically essential to predict supraventricular tachycardia, postoperative atrial fibrillation and paroxysmal atrial fibrillation. We propose an algorithm for intra-class classification that includes an analysis of the R-R time series. In the pre-processing phase, we used Butter worth filters to remove the baseline wander and the other noise. In the feature extraction phase, we detected the RR interval duration and the distance between the occurrence of P wave and T wave. Using these features we applied Fisherpsilas Linear Discriminant to create a criterion that can be used for classification. Combining pre-processing, feature extraction and Fisherpsilas Linear Discriminant we succeed in separating Normal and PAC beats with 99% Accuracy.
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Elgendi et al. (2008) studied Premature atrial complexes. Fisher Linear Discriminant algorithm was evaluated on Accuracy in separating Normal and PAC beats. An algorithm using Fisher's Linear Discriminant and R-R time series features successfully separated normal and premature atrial complex beats with 99% accuracy.
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