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

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    Authors
    Tabei, Fatemehsadat
    Kumar, Rajnish
    Phan, Tra Nguyen
    McManus, David D.
    Chong, Jo Woon
    UMass Chan Affiliations
    Division of Cardiovascular Medicine, Department of Medicine
    Document Type
    Journal Article
    Publication Date
    2018-10-16
    Keywords
    Personalization
    Photoplethysmography
    PPG
    Motion Noise Artifacts
    Signal Quality Index
    Analytical, Diagnostic and Therapeutic Techniques and Equipment
    Biomedical Engineering and Bioengineering
    Cardiology
    Health Information Technology
    Medical Biotechnology
    
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    Link to Full Text
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602087/
    Abstract
    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.
    Source

    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. Link to article on publisher's site

    DOI
    10.1109/ACCESS.2018.2875873
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/29522
    PubMed ID
    31263653
    Related Resources

    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1109/ACCESS.2018.2875873
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