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dc.contributor.authorTabei, Fatemehsadat
dc.contributor.authorKumar, Rajnish
dc.contributor.authorPhan, Tra Nguyen
dc.contributor.authorMcManus, David D.
dc.contributor.authorChong, Jo Woon
dc.date2022-08-11T08:08:24.000
dc.date.accessioned2022-08-23T15:54:18Z
dc.date.available2022-08-23T15:54:18Z
dc.date.issued2018-10-16
dc.date.submitted2020-08-10
dc.identifier.citation<p>Tabei F, Kumar R, Phan TN, McManus DD, Chong JW. A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals. IEEE Access. 2018;6:60498-60512. doi: 10.1109/ACCESS.2018.2875873. Epub 2018 Oct 16. PMID: 31263653; PMCID: PMC6602087. <a href="https://doi.org/10.1109/ACCESS.2018.2875873">Link to article on publisher's site</a></p>
dc.identifier.issn2169-3536 (Linking)
dc.identifier.doi10.1109/ACCESS.2018.2875873
dc.identifier.pmid31263653
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29522
dc.description.abstractPhotoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=31263653&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602087/
dc.subjectPersonalization
dc.subjectPhotoplethysmography
dc.subjectPPG
dc.subjectMotion Noise Artifacts
dc.subjectSignal Quality Index
dc.subjectAnalytical, Diagnostic and Therapeutic Techniques and Equipment
dc.subjectBiomedical Engineering and Bioengineering
dc.subjectCardiology
dc.subjectHealth Information Technology
dc.subjectMedical Biotechnology
dc.titleA Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals
dc.typeJournal Article
dc.source.journaltitleIEEE access : practical innovations, open solutions
dc.source.volume6
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/1745
dc.identifier.contextkey18844216
html.description.abstract<p>Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.</p>
dc.identifier.submissionpathfaculty_pubs/1745
dc.contributor.departmentDivision of Cardiovascular Medicine, Department of Medicine
dc.source.pages60498-60512


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