A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Keonsoo | - |
dc.contributor.author | Choi, Hyung Oh | - |
dc.contributor.author | Min, Se Dong | - |
dc.contributor.author | Lee, Jinseok | - |
dc.contributor.author | Guptha, Brij B. | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.date.accessioned | 2021-08-11T16:24:27Z | - |
dc.date.available | 2021-08-11T16:24:27Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/8437 | - |
dc.description.abstract | 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. | - |
dc.format.extent | 7 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2017.2700488 | - |
dc.identifier.scopusid | 2-s2.0-85028926088 | - |
dc.identifier.wosid | 000404486900045 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.5, pp 11437 - 11443 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 5 | - |
dc.citation.startPage | 11437 | - |
dc.citation.endPage | 11443 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | RISK-FACTORS | - |
dc.subject.keywordPlus | PREVALENCE | - |
dc.subject.keywordPlus | STROKE | - |
dc.subject.keywordPlus | ADULTS | - |
dc.subject.keywordAuthor | Arrhythmia | - |
dc.subject.keywordAuthor | atrial fibrillation | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | photoplethysmography | - |
dc.subject.keywordAuthor | smartphone | - |
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