An algorithm combining LDA feature reduction and an SVM classifier demonstrates high sensitivity and specificity for detecting atrial fibrillation episodes from RR intervals.
This paper explains an atrial fibrillation (AF) detection algorithm, which consists of a linear discriminant analysis (LDA) based feature reduction scheme and a support vector machine (SVM) based classifier. Initially nine features were extracted from the input episodes each containing 32 RR intervals by linear and nonlinear methods. Next, to improve the learning efficiency of the classifier and to reduce the learning time, these features are reduced to 4 features by LDA. The performance of the proposed method in discriminating AF episodes was evaluated using MIT-BIH arrhythmia database. The obtained sensitivity, specificity and positive predictivity were 99.07%, 100% and 100%, respectively.
Mohebbi et al. (Fri,) studied this question.
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