A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

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dc.creatorTabei, Fatemehsadat
dc.creatorKumar, Rajnish
dc.creatorPhan, Tra
dc.creatorMcManus, David
dc.creatorChong, Jo Woon
dc.creator.orcid0000-0002-1639-1531en_US
dc.date.accessioned2018-11-08T17:33:06Z
dc.date.available2018-11-08T17:33:06Z
dc.date.issued2018-12
dc.descriptionThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
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, the PPG signals are sensitive to motion and noise artifacts (MNAs), especially when they are obtained from smartphone cameras. Moreover, the 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. In this paper, a concept of the probabilistic neural network 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 model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As for the 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.en_US
dc.description.sponsorshipTexas Tech University Open Access Initiativeen_US
dc.identifier.citationTabei, F., Kumar, R., Phan, T. N., McManus, D. D., & Chong, J. W. (2018). A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals. IEEE Access, 6, 60498–60512. https://doi.org/10.1109/ACCESS.2018.2875873en_US
dc.identifier.other10.1109/ACCESS.2018.2875873
dc.identifier.urihttps://hdl.handle.net/2346/82094
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.subjectCamerasen_US
dc.subjectElectrocardiographyen_US
dc.subjectLensesen_US
dc.subjectHeart rate monitoringen_US
dc.subjectNeural networksen_US
dc.titleA Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signalsen_US
dc.typeArticleen_US

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