title: Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification.
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%).
authors.affiliation: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK. Electronic address: kennedy-a23@email.ulster.ac.uk.
authors.forename: Alan
authors.initials: A
authors.lastname: Kennedy
authors.affiliation: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK.
authors.forename: Dewar D
authors.initials: DD
authors.lastname: Finlay
authors.affiliation: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Guldenring
authors.affiliation: Computer Science Research Institute, University of Ulster, Northern Ireland, UK.
authors.forename: Raymond R
authors.initials: RR
authors.lastname: Bond
authors.affiliation: Dublin City University, Ireland.
authors.forename: Kieran
authors.initials: K
authors.lastname: Moran
authors.affiliation: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK.
authors.forename: James
authors.initials: J
authors.lastname: McLaughlin
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D000070276:Heart Rate Determination / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: Algorithms; Atrial fibrillation; R-R intervals