Motion and noise artifact-resilient atrial fibrillation detection algorithm for a smartphone

Jo Woon Chong, Chae Ho Cho, Nada Esa, David D. McManus, Ki H. Chon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

We have developed a motion and noise artifact (MNA)-resilient atrial fibrillation (AF) detection algorithm for smartphones that eliminates MNAs, and then detects AFs in smartphone camera recordings. MNA-corrupted episodes are observed to have larger values of turning point ratio (TPR), pulse slope, or Kurtosis compared to clean AF and normal sinus rhythm (NSR) episodes. On the other hand, AFs are shown to have larger root mean square of successive RR differences (RMSSD) and Shannon Entropy (ShE) [1]. Our developed AF algorithm is capable of separating MNAs, NSRs, AFs, which enhances the specificity of AF detection. We have recruited 88 subjects having AF at baseline and NSR after electrical cardioversion, and 11 subjects having MNA-corrupted NSRs to evaluate the performance of our AF algorithm. The clinical tests show that the proposed AF algorithm gives higher accuracy, sensitivity and specificity of 0.9667, 0.9765, 0.9714 compared to the previous AF algorithm [1].

Original languageEnglish
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages591-594
Number of pages4
ISBN (Electronic)9781509024551
DOIs
StatePublished - Apr 18 2016
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: Feb 24 2016Feb 27 2016

Publication series

Name3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

Conference

Conference3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Country/TerritoryUnited States
CityLas Vegas
Period02/24/1602/27/16

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