Automated detection of atrial fibrillation using RR intervals and multivariate-based classification

Alan Kennedy, Dewar D Finlay, Daniel Guldenring, Raymond R Bond, Kieran Moran, James McLaughlin

Research output: Contribution to journalArticlepeer-review

Abstract

Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%).
Original languageEnglish
Pages (from-to)871-876
Number of pages6
JournalJournal of Electrocardiology
Volume49
Issue number6
DOIs
Publication statusPublished - 2016

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