Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification
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 databa...
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| Published in | Journal of electrocardiology Vol. 49; no. 6; pp. 871 - 876 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.11.2016
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-0736 1532-8430 |
| DOI | 10.1016/j.jelectrocard.2016.07.033 |
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| 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%). |
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| AbstractList | 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%). 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%). |
| Author | Moran, Kieran Finlay, Dewar D. Guldenring, Daniel Bond, Raymond R. Kennedy, Alan McLaughlin, James |
| Author_xml | – sequence: 1 givenname: Alan surname: Kennedy fullname: Kennedy, Alan email: kennedy-a23@email.ulster.ac.uk organization: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK – sequence: 2 givenname: Dewar D. surname: Finlay fullname: Finlay, Dewar D. organization: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK – sequence: 3 givenname: Daniel surname: Guldenring fullname: Guldenring, Daniel organization: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK – sequence: 4 givenname: Raymond R. surname: Bond fullname: Bond, Raymond R. organization: Computer Science Research Institute, University of Ulster, Northern Ireland, UK – sequence: 5 givenname: Kieran surname: Moran fullname: Moran, Kieran organization: Dublin City University, Ireland – sequence: 6 givenname: James surname: McLaughlin fullname: McLaughlin, James organization: Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Northern Ireland, UK |
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| Keywords | Atrial fibrillation Algorithms R-R intervals Atrial Fibrillation |
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| Snippet | Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification... Abstract Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study we investigated two multivariate based... |
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| SubjectTerms | Adolescent Adult Aged Aged, 80 and over Algorithms Atrial fibrillation Atrial Fibrillation - diagnosis Automation Cardiac arrhythmia Cardiovascular Diagnosis, Computer-Assisted - methods Electrocardiography Electrocardiography - methods Female Heart Rate Determination - methods Humans Male Medical screening Middle Aged Multivariate analysis Pattern Recognition, Automated - methods R-R intervals Reproducibility of Results Sensitivity and Specificity Young Adult |
| Title | Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification |
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