A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone
This paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calcul...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7917244/ |
| _version_ | 1819172026345062400 |
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| author | Keonsoo Lee Hyung Oh Choi Se Dong Min Jinseok Lee Brij B. Gupta Yunyoung Nam |
| author_facet | Keonsoo Lee Hyung Oh Choi Se Dong Min Jinseok Lee Brij B. Gupta Yunyoung Nam |
| author_sort | Keonsoo Lee |
| collection | DOAJ |
| description | This paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calculate the root-mean square of the successive differences in the R-R intervals and Shannon entropy which are validated from x-means and Massachusetts Institute of Technology and Beth Israel Hospital database for the features for AF detection. A smartphone camera was used to obtain photoplethysmography signals. Clinicians labeled 29 records by referring to the electrocardiogram signals. These labeled records were used as a ground truth set to evaluate the accuracy of each method. In the experiments, the kNN, NN, and SVM methods achieved 98.65%, 99.32%, and 97.98% accuracies, respectively. |
| first_indexed | 2024-12-22T20:00:38Z |
| format | Article |
| id | doaj.art-dcaf082c6d664dfd8849d5a547cfd6ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:00:38Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dcaf082c6d664dfd8849d5a547cfd6ec2022-12-21T18:14:17ZengIEEEIEEE Access2169-35362017-01-015114371144310.1109/ACCESS.2017.27004887917244A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a SmartphoneKeonsoo Lee0Hyung Oh Choi1Se Dong Min2Jinseok Lee3Brij B. Gupta4Yunyoung Nam5https://orcid.org/0000-0002-3318-9394Medical Information Communication Technology, Soonchunhyang University, Asan, South KoreaDepartment of Cardiology, College of Medicine, Soonchunhyang University, Bucheon, South KoreaDepartment of Medical IT Engineering, Soonchunhyang University, Asan, South KoreaDepartment of Biomedical Engineering, School of Medicine, Wonkwang University, Iksan, South KoreaNational Institute of Technology at Kurukshetra, Kurukshetra, IndiaDepartment of Computer Science and Engineering, Soonchunhyang University, Asan, South KoreaThis paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calculate the root-mean square of the successive differences in the R-R intervals and Shannon entropy which are validated from x-means and Massachusetts Institute of Technology and Beth Israel Hospital database for the features for AF detection. A smartphone camera was used to obtain photoplethysmography signals. Clinicians labeled 29 records by referring to the electrocardiogram signals. These labeled records were used as a ground truth set to evaluate the accuracy of each method. In the experiments, the kNN, NN, and SVM methods achieved 98.65%, 99.32%, and 97.98% accuracies, respectively.https://ieeexplore.ieee.org/document/7917244/Arrhythmiaatrial fibrillationmachine learningphotoplethysmographysmartphone |
| spellingShingle | Keonsoo Lee Hyung Oh Choi Se Dong Min Jinseok Lee Brij B. Gupta Yunyoung Nam A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone IEEE Access Arrhythmia atrial fibrillation machine learning photoplethysmography smartphone |
| title | A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone |
| title_full | A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone |
| title_fullStr | A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone |
| title_full_unstemmed | A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone |
| title_short | A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone |
| title_sort | comparative evaluation of atrial fibrillation detection methods in koreans based on optical recordings using a smartphone |
| topic | Arrhythmia atrial fibrillation machine learning photoplethysmography smartphone |
| url | https://ieeexplore.ieee.org/document/7917244/ |
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