Show simple item record

dc.contributor.authorChong, Jo Woon
dc.contributor.authorCho, Chae Ho
dc.contributor.authorTabei, Fatemehsadat
dc.contributor.authorLe-Anh, Duy
dc.contributor.authorEsa, Nada
dc.contributor.authorMcManus, David D.
dc.contributor.authorChon, Ki H.
dc.date2022-08-11T08:08:24.000
dc.date.accessioned2022-08-23T15:54:17Z
dc.date.available2022-08-23T15:54:17Z
dc.date.issued2018-06-01
dc.date.submitted2020-08-10
dc.identifier.citation<p>Chong JW, Cho CH, Tabei F, Le-Anh D, Esa N, McManus DD, Chon KH. Motion and Noise Artifact-Resilient Atrial Fibrillation Detection using a Smartphone. IEEE J Emerg Sel Top Circuits Syst. 2018 Jun;8(2):230-239. doi: 10.1109/JETCAS.2018.2818185. Epub 2018 Mar 22. PMID: 30687580; PMCID: PMC6345530. <a href="https://doi.org/10.1109/JETCAS.2018.2818185">Link to article on publisher's site</a></p>
dc.identifier.issn2156-3357 (Linking)
dc.identifier.doi10.1109/JETCAS.2018.2818185
dc.identifier.pmid30687580
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29521
dc.description.abstractWe have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects' fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals (PPIs) are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly-varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-minute pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.97, 0.98, 0.97, respectively, which are higher than those of the previous AF algorithm.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=30687580&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345530/
dc.subjectatrial fibrillation
dc.subjectmotion and noise artifact
dc.subjectroot mean square of successive RR differences (RMSSD)
dc.subjectShannon entropy
dc.subjectsupport vector machine (SVM)
dc.subjectAnalytical, Diagnostic and Therapeutic Techniques and Equipment
dc.subjectBiomedical Devices and Instrumentation
dc.subjectCardiology
dc.subjectCardiovascular Diseases
dc.subjectTelemedicine
dc.titleMotion and Noise Artifact-Resilient Atrial Fibrillation Detection using a Smartphone
dc.typeJournal Article
dc.source.journaltitleIEEE journal on emerging and selected topics in circuits and systems
dc.source.volume8
dc.source.issue2
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/1743
dc.identifier.contextkey18844213
html.description.abstract<p>We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects' fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals (PPIs) are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly-varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-minute pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.97, 0.98, 0.97, respectively, which are higher than those of the previous AF algorithm.</p>
dc.identifier.submissionpathfaculty_pubs/1743
dc.contributor.departmentDepartment of Medicine, Division of Cardiovascular Medicine
dc.source.pages230-239


This item appears in the following Collection(s)

Show simple item record